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«Создание наукоемкой экономики – это, прежде всего повышение потенциала казахстанской науки Из Послания Главы государства – Лидера нации Н.А. Назарбаева народу Казахстана. Казахстанский путь -2050: Единая цель, единые ...»

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Knowledge discovery However, small data sets are not efficient; we had to access large scale data sets. Following the advice of Dr. Gaudi, we applied for the access to USA data set on CF. We included also one team from USA for this application, that was the mistake, and we failed with our application, although we learned from the referees reports about “the strength of the team”, and “the methods applied”. One more drop to a further success of the strong team! Different projects took over, but few years later on, using different methods to the same data, we have got better results, that I presented at the Conference in Venice, where I was invited for the round table discussions for the session “Social Mechanisms for Better Information Discovery and Interpretation” Teleconference organised by Committee of ESCFPR:Granted access to Data After this conference, and further discussions with Gaudi, we decided to apply for the access to European Society CF Patient Registry ( ESCFPR) database. This application was met very friendly by the Committee of ESCFPR. We had the Teleconference to correct the details of the application. The Teleconference was also a chance for us to learn not only from the experts in the field, but from administrative staff as well. Few months later on we have been informed about the success of the application that gives us the access to the ESCFPR database from 26 countries with 33000 records! This data will be transferred to Australia, and I am only one, who has the access to this data base Funds for research: Recent grant application to German fund The access to to the ESCFPR database was again only the first step for a new wave of research on “Real big data sets” on CF. The current situation at Australian Universities unfortunately doesn’t support the projects with no funds. Only financially supported projects are very welcomed. Finally we found the German Fund “Mukoviszidose”, that is supporting research projects on CF. On 13 December we submitted the first round application for the project: “Clinical scoring systems for the assessment of Cystic fibrosis disease severity”. We passed the first round successfully! 87% of all applications were cut down, but we are given a chance to take a part at the final stage in this process. We had to submit more detailed application to the fund by April. We have been informed that one more fund from Switzerland is interested in this project that sounds great!In April I visited the Children Hospital in Lausanne for discussions with doctors about details of the project.

B. Tobacco Control Systems Background.Recent statistics on tobacco and health reveal that about 1.l billion people currently smoke cigarettes, 80% of which lives in low and middle-income countries. Overall, the latest global statistics show that a third of the male adult population smokers and smoking-related diseases kill one in ten adults, which translate into five million premature deaths per annum; if current trends continue, smoking will be responsible for one in six deaths by 2030. Development of theoretical and methodological frameworks in data analysis is fundamental for modelling complex tobacco control systems.

Global picture. In response, significant progress in tobacco control policy planning and development has been reported, especially in developed countries. At the moment, almost every jurisdiction in the world has to join to the tobacco control battles and enormous efforts, like policy interventions, mass media campaigns and the provision of smoking cessation information, have been made to cope with that problem.

The Framework Convention on Tobacco Control (FCTC)established by the World Health Organisation in 2003 was the first international treaty devoted to public health. Up to now 142parties, which represent 95% of the world’s population, have ratified the FCTC.

Tobacco Control data set The International Tobacco Control Policy Evaluation Survey (ITCPES), [ITC survey, 2010] is a recent coordinated international research and evaluation effort. This project provides massive survey data collected from many countries including Australia, for studying and evaluating the psychosocial and behavioural impact of diverse tobacco control policies to smoker behaviour across these countries (Figure 1).

The Framework Convention on Tobacco Control (FCTC) has been ratified by up to 142parties [].

Many countries have incorporated FCTC policies into their laws. These countries have attempted to influence the behaviour of smokers by regulating and implementing diverse tobacco control policies. [].

In this study, we were interested to find clusters: groups of smokers with similar demographics, responses to anti-smoking advertisements and warning labels, beliefs about quitting in the role of predicting the rate of quitting attempt, etc. We aim to understand better the psychosocial and behavioural impact of diverse tobacco control policies to distinct clusters of these smokers. Controlling tobacco smoking and determining effective policies is difficult because of the complexity of human nature and behaviours. Also, the success of tobacco control is not the result of single policies, but is the outcome of interactions among various policies in various domains. Therefore, cluster analysis is helpful for dealing with causalities among a set of stable clusters defined by fixed number of instances over the set of variables. In order to analyse this data set we apply modified global k-means clustering algorithm to a survey data sets about a complex tobacco control system.

Methodology development Subsequently, developing most suitable methodologies and techniques to monitor the performance and evaluatethe effectiveness of relevant tobacco control policies have becomeimportant research issues, since an efficient and effective monitoring and evaluating system can provide accurateand timely information on the performance of policies, programs and projects. This information can provide invaluable support for decision-making, decision-refinement and ongoing management of government activities, and can underpin accountability relationships. In order to describe the non-linear relationships more effectively, new global optimization-based approaches were previously proposed in the paper [Z. Dzalilov et al., 2010]. Our preliminary results indicate a possibility for a global optimal approach to covering all possible solutions in a complex tobacco control system. (see [Z. Dzalilov et al., 2010] for more details).

As the evidence of importance relating to the research topic above, considerable tobacco experts, practitioners and academic researchers around the world have been involved in comprehensive tobacco control research. There have also been several recent efforts to coordinate international research and evaluation effects, like Global Youth Tobacco Survey and International Tobacco Control Policy Evaluation Survey (ITCPES).

Methods: Data Mining and Optimisation We applied optimisation based data mining techniques developed at CIAO research Centre toData from the International Tobacco Control Policy Evaluation Survey (ITCPES).

The purpose of this project is to evaluate the psychosocial and behavioural impact of key nationallevel tobacco control policies over the years. However, if we take a closer look at these research results, we find that these approaches have fundamental limitations, since the outcomes presented in these papers did not provide any mechanism to deal with complex non-linear data, which characterise most aspects of the tobacco domain. In addition, these available techniques did not address the causal interrelatedness of smokers, nonsmokers, researchers, doctors, advocates, tobacco industries, policy makers etc in co-producing the targets of tobacco control policies. As a result, significant gaps in tobacco control policy planning and development remain.

Global networking, team work (VicHealth Centre of Tobacco Control, The Cancer Council Victoria, and the team of researchers from the Centre for Informatics and Applied Optimisation (CIAO), University of Ballarat (UB). This project is the best example for illustration useful links between research, policy development and the effects of those policies to the systems under consideration (in particular, in tobacco control systems). This project is about unique combination of theoretical frameworks, survey data and expert judgments to develop innovative models of the relationship between research knowledge, current policies and subsequent outcomes for smoke-free policies, aimed at reducing the risk from environmental tobacco smoke.

We presented attractive approaches from various aspects to model and analyse complex data. We have developed new frameworks, methodologies, and techniques for modelling and analysing complex data in tobacco control. This methodology includes information technology, mathematical analysis, data mining, and optimisation tools We used our research outcomes to model and analyse complex data provided by the VicHealth Centre of Tobacco Control, The Cancer Council Victoria. The data mainly come from the International Tobacco Control Policy Evaluation Survey (ITCPES). In particular, we connectour research outcomes for evaluation of the effects of existing instruments on smoking forbetter understanding their roles and limitations, so that we can better identify where new actions might be required.

Despite of challenges and limitations, the research outcomes providenew insights into how to maximize the effectiveness of Public Health policies. The research outcome fixed the gap between existing techniques limitations and expected outcomes by providing a good and solid template for complex data in tobacco control systems, as well as in other similar complex social data domain.

3. Learning environment design, and brain data sets. Brain Complex Networks Discussions on “Brain Data sets” I would like to connect to the problem on:

“HOW BRAIN BEST LEARNS”

The process of learning has strong correlation with the brain functioning that is a very special area of research in Neuroscience.

Brain networks as information processing systems Background: The era of discovery science for human brain function was inaugurated by the collaborative launch of the 1000 Functional Connectomes Project (FCP) on December 11, 2009. FCP entailed the aggregation and public release (via www.nitrc.org) of over 1200 resting state fMRI (R-fMRI) datasets collected from 33 sites around the world. In just over 6 months, the release generated over downloads and ~32,000 page-views from 1,223 cities in 78 countries.

1,000 Functional Connectomes Project is a collection of fMRI data sets donated by researchers from 35 centres around the world. This freely available resource includes data from more than 1,400 healthy subjects who underwent fMRI scans that assessed their brain activity when their minds were at rest (Proc.

Natl. Acad. Sci. USA 107, 4734–4739, 2010). The study showed that resting-state fMRI data— long thought of as nothing more than random, background noise—can be reliably pooled across scanners to unveil a universal architecture of activity connections within the brain.

Unlike task-based fMRI, which can be highly specific to the study site, the 1,000 Functional Connectomes resource allows for systematic explorations of healthy and diseased brains to discover hitherto unknown underlyingdifferences. “We’re moving in the direction of being able to have objective measures of neurological and psychiatric illness,” Milham says. “It’s all stepping in the direction of being a clinical tool.” The effort takes its name from the Human Connectome Project, a $30 million initiative launched by the US National Institutes of Health last year to map the entire physical circuitry of the healthy adult human brain. But functional connectivity and structural connectivity is not the same thing. Functional connections, for example, can span more than one synapse and can be modulated by emotion or sleep, whereas anatomical circuits are more or less fixed over the short term.“Having this much data in one place is a real treasure trove that is free to anybody who wants to play with it,” says Marcus Raichle, a pioneer of resting-state fMRI at Washington University in St. Louis, Missouri who was not involved with the study.“The connectomes project has the power to ask more questions,” adds Craig Bennett, a cognitive neuroscientist at the University of California–Santa Barbara who published a review this month questioning the reliability and repeatability of fMRI scans in most typical neuroimaging studies (Ann. N.Y. Acad. Sci. 1191,133–155, 2010). “You’re not just looking across one study, you’re drawing from such a large body of research that you really say things with authority.” Since having been postedonline last December, the data set has been downloaded more than 4, times from researchers across 54 countries, according to Milham. One person who has explored the resource is Nora Volkow, director of the US National Institute on Drug Abuse in Bethesda, Maryland. Volkow is now developing quantitative methods to measure functional connectivity in her lab to follow up on preliminary observations of systemic differences between males and females.

REFERENCES

1. Z. Dzalilov, A. Bagirov and M. Mammadov. Application of Optimization Based Data Mining Techniques to Medical Data Sets: A Comparative Analysis, IMMM 2012, Proceedings of The Second International Conference in Information Mining and Management, October, Venice, Italy, P: 41 to 46: ISBN: 978-1-61208-227-1;

2. Z. Dzalilov and A. Bagirov (2010). Cluster Analysis of a Tobacco Control Data Set. International Journal of Lean Thinking.1(2): 40-5.

3. Z. Dzalilov, J. Zhang, A. Bagirov and M. Mammadov (2010). Application of optimisation–based data mining technique to tobacco control dataset. International Journal of Lean Thinking.1(1):27-41.

4. G. Hafen, C. Hurst, J. Yearwood, M. Mammadov, J. Smith, Z. Dzalilov, P. Robinson. A new clinical scoring system in Cystic Fibrosis: Statistical tools for database analysis-a preliminary report. BMC Medical Informatics and Decision Making, 8: 44.

5. M.A. Mammadov, Rubinov A.M. and Yearwood, J. (2007), The study of drug-reaction relationships using global optimization techniques. Optimization Methods and Software, Volume 22, No: 1, 99-126.

6. M. Zarei and Z. Dzalilov (2009). Optimization of back-propagation neural networks architecture and parameters with a hybrid PSO/SA approach. Proceedings of fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control (ICSCCW 2009). Famagusta, North Cyprus.

7. Z. Dzalilov, A. Bagirov and M. Mammadov. Application of Optimization Based Data Mining Techniques to Medical Data Sets: A Comparative Analysis, IMMM 2012, Proceedings of The Second International Conference in Information Mining and Management, October, Venice, Italy, P: 41 to 46: ISBN: 978-1-61208-227-1;

UDCI 004.

SECURITY IN THE CLOUD – VULNERABILITIES, THREATS, AND RESPONSES

Abstract. Cloud technology is becoming one of the fastest growing sectors of the IT industry due to the reduction of costs on computation processes, along with benefits such as flexibility and scalability. Cloud computing is used widely among a lot of organizations. However, this new technology opens new prospects for threats against security of data. Mostly, threats in the cloud are similar to the regular attacks such as spyware, malware for data stealing, Trojans, viruses, worms, bots and so on. Besides regular type of attacks, there are other issues associated with cloud due to the infrastructure of the technology. This paper will discuss problems regarding reliability and security in the field of virtualization and cloud computing, it will also propose available solutions to those problems. Real examples of cyber attacks in cloud computing environment will be presented.

Key words: cloud computing, virtualization, security, cyber attacks.

Introduction An environment with network infrastructure that is used for sharing data and computations is called cloud computing. Clouds work based on the Internet and their purpose is to hide the complexity from users.

The notion of cloud computing includes both the equipment and software in data centers for provision of services, and those services in the form of applications. Virtualization technologies are used for computations on the cloud.

Currently Public, Private, and Hybrid cloud environments exist. In public cloud model resources can be accesses by the public. Services provided by public cloud might or might not be charged. Private cloud model services are internal to companies and are not available for the public use. If a part of resources are managed by company internally and the rest is available for ordinary people then such kind of environment is called hybrid cloud. The private part of a hybrid cloud is defended by firewalls and only authorized staff has a permission to access it.

The services provided in the cloud can be divided into three major categories: SaaS (Software-as-aService), PaaS (Platform-as-a-Service), and laaS (Infrastructure-as-a-Service) [2].

According to the results of the survey conducted on the global scale by Japanese security softwarecompany called Trend Micro, over a half of organizations who took part in the survey showed that they utilize cloud technology. 45 percent of surveyed companies indicated that they utilize private cloud, whereas 46 percent seem to use private cloud (see Table 1) [1].

Table 1 – Implementation of virtualization and cloud computing % that have currently deployed or are To use cloud infrastructure a lot of enterprises in a rush deploy simply physical server security on virtual machines, but new security threats specific to cloud computing and virtualization are not considered by typical physical server security. Furthermore, such kind of security might have negative influence on platform performance.

Virtualization Security Threats In this section threats and issues specific to virtualization infrastructure will be discussed.

Communication Blind Spots Connections between virtual machines on the same host are not seen by conventional network security tools. If outside the host machine all communications are connected to that security tool, then the connections are visible. However, this security technique leads to time delays. Placing a special security virtual machine on a host that can accord communication between other virtual machines can help to get rid of invisibility and to decrease the amount of delay [3].

In a virtual environment this can be counted as a good solution. For cloud environment a special security virtual machine is not ideal though, because such virtual machine should use hypervisor and in some cloud environments hypervisor is not accessible. The virtual machines in the cloud are self-defending, thus outside communication is not necessary.

Inter-VM attacks and hypervisor compromises Operating systems and applications used by virtualized and physical servers are the same. Therefore, attackers might use vulnerabilities of those applications and systems, and thus become a threat for virtual environment. If attacker is able to compromise any one part of the virtualized environment, the other parts are under threat as well, unless virtualization-aware security is provided [1].

One scenario suggests that after compromise of one guest VM by an attacker, the compromised VM can distribute the infection to other guest VMs on the same host. Close allocation of several VMs lifts the chances of further compromise distribution. In this case, malware should be discovered by intrusion detection and prevention along with firewall systems, without regard to the placement of the VM inside the virtual environment.

Attackers also include hypervisor in their attack plans. Hypervisor is a program with a help of which several VMs are able to run on a one computer. So, on the one hand hypervisors are a great help and on the other hand it might lead to computing risks. That is why to have a secure hypervisor is very important task.

“Hyperjacking” is a type of attack when malicious software that entered one VM is able to attack the hypervisor. Guest VM attacks a hypervisor, other VMs on that host are attacked by compromised hypervisor [4].

To make a requests to the hypervisor VMs use different kinds of techniques, those methods usually tend to have some API (application programming interface. The primary goal for the creation of API is to be able to control VMs remotely from the host [5]. So, APIs are often attacked by malware. Therefore, APIs must be secure and VMs should make only authorized requests.

Mixed Trust Level VMs When the same host is occupied by mission-critical data VMs and VMs with less critical information mixed trust level VMs are formed. Some companies may try to separate this secure data of mixed levels on different host machines. However, this may result in thwarting of the aim of virtualized environment - to use resources more effectively. For companies it is crucial that while the advantages of virtualization are being realized, mission-critical data is safe. VMs can be protected with the help of self-defending VM security even in environments of different trust levels. The protection tools include “detection and prevention, a firewall, integrity monitoring, log inspection and antivirus capabilities”.

Instant-on-gaps Even though virtualized environments are innately safer than their physical analogues, in practice virtualization may pose a threat of having vulnerabilities, unless administrators know about them and take certain actions to remove them. One of the possible vulnerabilities that may occur are instant-on gaps.

Companies use VMs in their needs to consolidate servers, decommission, migrate and clone VMs for testing environments and VMs' dynamic nature is especially advantageous there [6]. Therefore, activating and deactivating VMs, updating and securing them may be difficult.

After some time inactive VMs may diverge from the minimum security state that far that even activating them may cause an occurrence of serious vulnerabilities in security. For example, some inactive VMs may still be accessed by attackers even if they are inactive. Furthermore, security out of date might facilitate cloning process of new VMs from templates.

Outdated security of VMs may enable attackers to maximize the benefit of using VMs for a longer time.

In general, when antivirus is being used or updated but guest VM is not online, the VM will become inactive and unprotected. However, when a guest VM becomes online, it will become immediately vulnerable. A solution for this problem could be a special security VM for every host which will update VMs automatically when it is powered on or cloned. This gives companies an opportunity to realize advantages of virtualization [1].

Cloud Computing Control And Security Cloud computing is a result of addition of automation and virtualization. By the use of virtual environments the capacity of physical servers is used to the full extent and thus contributes to the acquisition of more computing power. It was discovered by service providers that by using virtualization it became possible to enable multi-tenant usage of physical servers instead of single-tenant. Private clouds built on the virtual infrastructure also seem to have improvements in utilization of resources and facilitation of service supply. Different cloud models mentioned above (private cloud, public cloud and hybrid cloud) enable distinct control levels and they differently affect security[1].

Cloud Computing Threats Since cloud computing works based on virtualization threats discussed for virtual environments are also dangerous for cloud computing. The boundaries of cloud computing covers a lot: information on public clouds, private clouds and mobile devices. This opens new prospects for threats, and therefore accordingly new security measures should be taken.

Security threats in cloud are cloning and rapid resource pooling, motility of data and data remnants, elastic perimeter, unencrypted data, shared multi-tenant environments of the public cloud, control and availability.

Cloning and rapid resource pooling Regardless of the model of the cloud, due to the increased demand there might be created a “glut of VMs”. VMs can be quickly delivered by cloud self-service portals. VMs can be transferred to previous versions, can be paused and restarted. All of this can be done relatively easily. It is also possible to clone them, and move between physical servers. Errors and vulnerabilities can be propagated without knowledge about them. The difficult part might be maintaining record of the state of security at any point of time [1].

Recently, a member of Amazon Web Services uploaded and a pre-built image and this posed a threat on whole Amazon community, since the image contained the publisher's secure shell (SSH) on it. This is because the image could enable the publisher to log in to any machine that has the image. As a result, this event made the use of pre-built machine images questionable, despite their handiness in saving time.

Conclusions Virtualization and cloud computing help to eliminate traditional boundaries in networks. These new technologies must support consumers with a widening scope of devices to access data in smart phones, tablet computers, net books, notebooks, and traditional laptops. Cloud security architecture must adapt to these shifting patterns, it must also support the infrastructure benefits of flexibility and cost savings.

This paper discussed threats in virtualization and cloud computing environments. Recommendations to solve those issues are presented as well.

REFERENCES

1. Trend Micro. Security Threats to Evolving Data Centers. Retrieved from http://www.

trendmicro.com/cloud-content/us/pdfs/security-intelligence/reports/rpt_security-threats-to-datacenters.pdf http://www.katescomment.com/iaas-paas-saas-definition/ techtarget.com/definition/virtualization http://www.ca.com/us/~/media/files/industryresearch/security-cloud-computing-users_235659.aspx 5. F. Sabahi. (2011). Cloud computing security threats and responses. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber= 6. J. W.Rittinghouse and J. F.Ransome. (2010). Cloud Computing: Taylor and Francis Group, LLC.

Retrieved from www.efgh. com/software/rijndael. htm Тйіндеме. Апаратты технологиялар индустриясында блтты технологиялар те арынды даму стіндегі секторларды бірі болып табылады. Мны себебі – есептеуіш процестерге кететін шыындарды азаюуы жне технологияны ыайлы болуы. Блтты технология кптеген йымдар арасында ке тараандытан, блта атысты маызды мселелерді арастыран абзал. Блт провайдерлері кп кездесетін мселелерді бірі ауіпсіздік болып табылады. Бл маалада блтты технологиялар саласындаы ауіпсіздік проблемалары арастырылады жне сол проблемаларды ммкін шешімдері сынылады.

Тйін сздер: блтты технологиялар, виртуализация, ауіпсіздік, компьютерлікшабуылдар Резюме. Облачные технологии становятся одним из наиболее быстро растущих секторов ИТ-индустрии в связи с сокращением расходов на вычислительные процессы, наряду с преимуществами, такими как гибкость и масштабируемость. Облачные технологии широко используется среди большого количества организаций, таким образом, есть необходимость рассмотреть некоторыеиз важнейших вопросов, связанных с облаком.

Безопасность является одним из наиболее важных вопросов, с которыми облачные провайдеры пытаются справиться. В этом статье рассматриваются проблемы, касающиеся надежности и безопасности в области облачных вычислений, также предлагаются доступные решения этих проблем.

Ключевые слова: облачные технологии, виртуализация, безопасность, компьютерные атаки UDCI 658. Almaty University of Power Engineering & Telecommunications 1.

AUTOMATION OF HYDROTHERMAL PROCESSING OF PLYWOOD RAW MATERIAL

Abstract. The report focuses on the use of modern devices and digital automation system with the creation of an effective computerized system of hydrothermal treatment plywood raw material. The application of automated systems to improve productivity and quality heating plywood raw material and reduce the loss of heat and energy.

Key words: PSF (plywood, manufactured using a phenol-formaldehyde resin adhesive), FC (plywood obtained when gluing veneer glue) SCADA - supervisory control and data acquisition.

The technological process of hydrothermal treatment of veneer stock highly influences the product quality and raw materials consumption. The existing control systems are not effective and characterized by deviations from predetermined process conditions thus resulting in overuse of thermal power, unsatisfactory preheating of plywood stock material and appearance of chips and cracks in veneer sheets [1].The enterprises, which produce laminated, FSF and FK plywood, have no effective automated control systems of hydrothermal treatment of veneer stock which negatively characterizes these processes at all performance factors. Applied automation means consist of analog temperature sensors for the heating water and primitive devices changing the heat supply into the basin for hydrothermal treatment of veneer stock. Without the use of modern devices and digital automation systems it is impossible to create an effective computerized system of hydrothermal treatment of veneer stock. Such computerized systems must improve the productivity and quality of preheating of the veneer stock, and reduce the loss of heat and energy [2], [3].

Therefore, it is required to develop and introduce mathematical, data, algorithmic, and machine supply for the computerized systems for hydrothermal processing of the veneer stock of which the laminated, FSF, and FK plywood is being produced, thus saving energy and increasing productivity.

For this purpose at first they conduct a system analysis, development of mathematical models and survey of processes of hydrothermal treatment as controlled objects. Production of plywood also requires development [3], [4]. of mathematical models and data supply of processes of hydrothermal processing. This includes heat supply into the basin for the hydrothermal treatment, heat losses through the surface of the basin, heat losses through the basin insulation, models of the preheated veneer stock, a heat balance of the basin, and heat currents during hydrothermal treatment [4], [5].

Mathematical model characterizes description of veneer stock heating by ordinary differential equations, which is acceptable for the construction of computerized control process of hydrothermal treatment of the veneer stock.

During the preheating of veneer stock heat losses occur. Processes of heat leakage through the water surface in the basin have variable physical nature. Losses can be subdivided into the following components:

convective losses, losses by radiation, and losses by evaporation of water from the basin [5], [6], [7].

Also a mathematical model has been developed to assess the amount of convective heat given by the water surface. This mathematical model helps estimate the amount of convective heat given by the water surface. In order to manage the process it is necessary to maintain the temperature of the basin water at the pre-set level [8], [9].

The scheme includes (position) pool 1, the heat exchanger 2 feed line 3, the return line 4, 5 filters, water filling line 6, the actuator control the supply of water in the pool 7, the sensors measure the water level in the pool 8, actuators, control [10] and plum topping water from the basin 9 and 10, the water drain pipe 11, a sensor measuring the water temperature in the delivery pipe 12, a screw pump with a motor 13, a sensor measuring the water pressure in the supply pipe 14, water temperature sensors installed at various points in the pool 15, an input module signals from sensors (eg ADAM-5017H) 16, the output unit (for example, ADAM-5024) 17 industrial Controller (for example, ADAM-5000 / TCP) 18, the control computer (for example, SIMATIC RACK PC 840 V2) 19, [11], [12] an operator console 20.

The system operates as follows. From the operator panel 20 or from the host computer 19 is signaled to turn on the system controller 18, with the output module 17 test pulses are fed to the actuators 7-10 and motor screw pump 13, with [13], [14] the input module 16 test pulses go to the sensors 15, 14, 12, 8, back with devices receive signals about their serviceability or malfunction of an industrial controller 18, an operator console 20, the control computer 19 After receiving the information about the serviceability of sensors and actuators industrial controller 18 compares the values of the temperature obtained from the sensors located in the basin, with the temperature set by the technological requirements, a signal to the switch actuator 7, located on the flow line 3, [15] and the heated water is pumped into the pool. On the flow line 3 are sensors measuring the temperature of the water 12 and 14 measure the water pressure, with information which comes to the industrial controller 18 through the input module, where it is processed, and the operator panel 20 and the control computer 19 [16].

The developed algorithm of computerized control system of water heating and treatment SCADA control system for the hydrothermal treatment of veneer logs lets effectively manage all the SCADA components such as purifying filter, hydrothermal treatment basin, the plate heat exchanger, water supply pump from the basin, a blow fan for supplying waste drying agent in the plate heat exchanger [17], [18].

The developed control algorithm for the process of hydrothermal processing of raw materials for the production of the laminated, FK and FSF plywood made it possible to develop software for the computeraided process control. An algorithm for automated determination of recuperation device parameters has given the opportunity to develop software for the automated basin heating [19].The developed algorithm for automated determination of the parameters of the circulating water purification during automated hydrothermal processing of the veneer stock made it possible to develop software for automated cleaning of heating water in the basin.

A scheme was presented in graphical form showing automation control system (ACS) of the process of hydrothermal treatment of the veneer stock for the production of the laminated, FSF and FK plywood, which is invariant to changes in ambient temperature. [20]ACS scheme was presented in graphical form showing the process of hydrothermal treatment of the veneer stock, which is invariant to changes in ambient temperature, as well as reducing the response rate of the hydrothermal treatment basin in the process of adjustment.

To implement a simple flexible positive feedback and functioning of the compensating element this automation system includes steam supply conduit with temperature and flow sensors, set into the heating water supply pipeline into the basin for the veneer stock hydrothermal treatment, as well as the ambient temperature gauge. It enables to record a change in temperature of the air, wind speed [21].

A data from the environmental factors gauge arrives at the industrial microcontroller, where the measured value is compared with the set temperature. If an attempt to eliminate the deviation by increasing the flow of heating water fails and an ambient temperature gauge detects a decrease in air temperature, which in the future will further increase the deviation of actual temperature from the preset temperature, the controller will regulate the temperature of heating water in the basin, taking into account the compensating element, and the basin will be additionally supplied by steam from the boiler, thus compensating the deviation [22], [23].

Software developed on the basis of the mathematical support of the veneer stock hydrothermal treatment, can effectively manage the process of hydrothermal treatment of wood. The selected complex of automation technical means can maintain the parameters of the hydrothermal treatment at a preset level. This hardware-software complex will improve the efficiency of enterprises that produce the laminated, FSF and FK plywood, and reduce consumption of raw materials [24], [25].

REFERENCES

1. Akitsu, H., Norimoto, M., Morooka, T. and Rowell, R.M. (1993a). Effect of humidity on vibrational properties of chemically modified wood. Wood and Fiber Science, 25(3), 250–260.

2. Akitsu, H., Gril, J. and Norimoto, M. (1993b). Uniaxial modelling of vibrational properties of chemically modified wood. Mokuzai Gakkaishi, 39(3), 258–264.

3. Arni, P.C., Gray, J.D. and Scougall, R.K. (1961a). Chemical modification of wood. I. Use of trifluoroacetic acid in the esterification of wood by carboxylic acids. Journal of Applied Chemistry, 11, 157–163.

4. Arni, P.C., Gray, J.D. and Scougall, R.K. (1961b). Chemical modification of wood II. Use of trifluoroacetic acid as a catalyst for the acetylation of wood. Journal of Applied Chemistry, 11, 163–170.

5. Arora, M., Rajawat, M.S. and Gupta, R.C. (1979). Studies on the acetylation of wood. Holforschung und Holzverwertung, 31(6), 138–141.

6. Arora, M., Rajawat, M.S. and Gupta, R.C. (1980). Effect of acetylation time on degree of acetylation in wood. Holforschung und Holzverwertung, 32(6), 138–139.

7. Arora, M., Rajawat, M.S. and Gupta, R.C. (1981). Effect of acetylation on properties of particleboards prepared from acetylated and normal particles of wood. Holforschung und Holzverwertung, 33(1), 8–10.

8. Sergovskii, P. S. Gidrotermicheskaia obrabotka i konservirovanie drevesiny, 2nd ed. Moscow, 1968.

9. Sergovskii P.S Hydrothermal treatment and preserving wood. M.: Forest Industry, 1998 – 10. Sterlin M.D. Elemental analysis of the flow of heat at gidrotermo¬obra¬bot-ku raw material in the manufacture of plywood. In: New.: in the production of plywood, chipboard and plastics drevesnosloietyh. - M.:

Forestry, 1990, issue 6, s.50-52.

11. Melkostupova G.A Hydrothermal treatment of fine wood. Fibreboard and plywood, scientific tehn.ref.sb., VNIPIEI-lesproi, 2000, No.1, p.7.

12. Waldenberg YS and others. Automated process control systems. M.: Statistics, 2005.- 180 p.

13. Bergervoet, T. (1983). Presteaming radiata pine heartwood to improve treatability. International Research Group on Wood Preservation, Doc. No. IRG/WP 3239.

14. Bhuiyan, M.T.R., Hirai, N. and Sobue, N. (2000). Changes of crystallinity of wood cellulose by heat treatment under dried and moist conditions. Journal of Wood Science, 46(6), 431–436.

15. Bhuiyan, M.T.R., Hirai, N. and Sobue, N. (2001). Effect of intermittent heat treatment on crystallinity in wood cellulose. Journal of Wood Science, 47(5), 336–341.

16. Bhuiyan, M.T.R., Hirai, N. and Sobue, N. (2002). Behaviour of piezoelectric. dielectric, and elastic constants of wood during about 40 repeated measurements between 100 °C and 220 °C. Journal of Wood Science, 48(1), 1–7.

17. Birkinshaw, C. (1998). Chemically modified wood—a review with consideration of the opportunities for application to Irish timber. Irish Forestry, 55(2), 21–34.

18. Birkinshaw, C. and Hale, M.D. (2002). Mechanical properties and fungal resistance of acetylated fast grown softwoods. I. Small specimens. Irish Forestry, 59(1–2), 49–58.

19. Bourgois, J., Janin, G. and Guyonnet, R. (1991). Measuring colour: a method of studying and optimizing the chemical transformations of thermally-treated wood. Holzforschung, 45(5), 377–382.

20. Bowyer, J.L., Shmulsky, R. and Haygreen, J.G. (2003). Forest Products and Wood Science: an Introduction, 4th edn. Iowa State University Press, USA.

21. Brebner, K.I. and Schneider, M.H. (1985). Wood–polymer combinations: bonding of alkoxysilane coupling agents to wood. Wood Science and Technology, 19(1), 75–81.

Автоматизация гидротермической обработки фанерного сырья Резюме. Доклад посвящен использованию современных устройств и цифровых систем автоматизации с созданием эффективной компьютеризированной системы гидротермальной обработки фанерного сырья.

Рассматривается применение автоматизированных систем для улучшения производительности и качество подогрева фанерного сырья, и уменьшение потери тепла и энергии.

Ключевые слова: ФСФ (фанера, изготавливаемая с применением смоляного фенолформальдегидного клея), ФК (фанера, получаемая при приклеивании шпонов карбамидным клеем), SCADA - диспетчерское управление и сбор данных.

Фанера шикізатыны гидротермиялы деуіні автоматтандырылуы Тйіндеме. Бл баяндама азіргі замана сай рылыларды игеруге жне цифрлы жйелерді автоматтандырылуын компьютерлік жйені кмегімен, тиімді фанера шикізатын гидротермалды деуге арналан. Автоматталан жйені олданысы ндірісті жасарту шін фанера шикізаттыны жылыту сапасын, жылыны жне айратты шыыныны кемуі арастырылады.

Тйін сздер: ФСФ (шайырлы фенолформальдегидті желімні олдануымен даярланан фанер), ФК (карбамид желіммен жабыстырылатын фанера), SCADA - диспетчер басармасы жне деректерді жиыныны.

УДК 378. kaiyr@mail.ru, otarbayev_zh@kazntu.kz, bagdat.yag@gmail.com

ANALYSIS OF EXPERT SYSTEMS, REVEALING OF THE MAIN DEFICIENCIES AND

SPECIFICATION OF WAYS OF ITS SOLVING

Abstract: The article deals with expert systems and issues of its use in education with distance education technologies and shows the main deficiencies and ways of its solving for the purpose of ES modulus creation for the system of distance educational technologies.

Key words: Distance educational technology, learning technology.

The need for development of distance learning technologies is obvious for all countries of the world.

The history of development of open distance education over the whole world has more than 40 years.

Higher education is considered as one of the leading factors of the social and economic progress. As for the present day the important value and main capital of the modern society is a human capable of searching for and mastering, the new knowledge and making out-of-the-box solutions.

Alongside with such changes the Republic of Kazakhstan (further referred to as – RoK) started to introduce the distance learning form to the educational process in experimental mode as from the year 2005.

Later the distance learning form was changed for distance education technology (further referred to as DET) [1].

Education has become one of the widest areas of human activity in the modern society. For the last ten-year period, the world has changed its attitude to all kinds of education, on the efficiency of which the prospects of human development depend in many ways [2].

Information resources management suggests availability of the aggregate of appropriate technologies based on using some or other means of collection, transfer, processing, storage, submission of information in the process of managerial activity. Depending on predominance of any of the above-listed informational processes, intensity or significance thereof one chooses the proper means of realization thereof that in the context of variety of the latter brings forth the problem of selection and use of means providing for information resources management.

Solving of the problem of selection and use of ES for the educational process for the purpose of conduct of laboratory works by the method of distance learning suggests the availability of the managerial personnel, specialists for introduction to the existing distance learning system, general knowledge of the recommended approaches etc.

Expert systems of project management are designed for planning and management of resources of different kinds (material, technical, information) when realizing complex scientific-and-research and projectand-construction works.

Expert systems and decision-making support systems are designed for the purpose of realizing the technologies of information provision of the managerial decision making processes by virtue of using economical-and-mathematical modeling and artificial brain principles.

ES are designed for the purpose of solving certain practical tasks in some narrow directions, for the basis of which the knowledge of expert specialists are taken. Expert systems became the first program developments to have attracted enormous attention to the results of investigation in the artificial brain area.

Systems of intellectual design and management system improvement are designed for use of so-called CASE-technologies (Computer Aid System Engineering) focused on computer-aided development of project decisions on creation and improvement of organization management systems [3].

ES development stages are considered in the figure 1.

Correctly selected expert and successful formalization of his knowledge allows providing ES with unique and valuable knowledge.

The first stage. To specify tasks and identify problems, to reveal the development pruposes, to specify experts and user types. By the example of introduction to DET the highly-qualified teaching staff of a higher educational institution will be in the role of experts.

The second stage. To analyze the task of a certain subject area, to reveal the definitions to be used and relationship thereof, to specify the task solving methods. By the example of introduction to DET the particular discipline shall be taken providing for laboratory works on this direction.

The third stage. To specify the logic structure (ways of representing of all types of knowledge), to select the appropriate software, project and system work model.

The fourth stage. The process of knowledge base introduction by the expert.

The fifth stage. Implementation of ES.

The sixth stage. Inspection and analysis of ES work.

The seventh stage. Operation of ES.

The eighth stage. Support and constant filling of the knowledge base with the current data.

If ES is not supported in the up-to-date state (regular filling of the knowledge base with the expert new knowledge), it will lose its being in demand with time.

The main deficiency of ES is inability of self-learning. Support of expert systems in the up-to-date state requires constant intervention in the knowledge base of knowledge engineers.

The most famous ES is Wolfram|Alpha. W|A does not return the list of references based on the inquiry results but calculates an answer basing on its own knowledge base which contains the data on mathematics, physics, astronomy, chemistry, biology, medicine, history, geography, politics, music, cinematography and also information on well-known people and internet-sites [4].

For the present day, ES is widely used in medicine but exclusively in highly specialized subject areas.

The first expert system called Dendral was developed in Stanford. This was the expert system determining the structure of organic molecules by chemical formulae and spectrographic data on the chemical bonds in molecules. The Dendral value consisted in the following. Organic molecules are generally very large and that is why the number of possible structures of these molecules is great as well. Owing to heuristic knowledge of chemical experts laid in the expert system, the right decision from amongst a million of possible ones was found by several attempts. Principles and ideas laid in Dendral appeared to be so effective that they have been used by now in chemical and pharmaceutical laboratories all over the world [5].

ES use in the learning process will certainly give its positive results. Using of standard computer programs or programs on particular directions of activity is already not quite the variant when the experienced user`s demands are very high. While higher educational institutions have started using supercomputers for research purposes and in the learning process, one ought to activate work on the direction of ES and artificial brain. Development of ES for managerial processes is possible if there is the enormous reserve of information resources. The figure 3 shows the general scheme of ES use in the learning process with DET. When developing ES on conduct of laboratory works on certain sections or disciplines the trainees would have the possibility of on-line carrying out these projects with the help of the electronic expert.

For the trainees on the direction of computer sciences it is possible to develop ES on conduct of laboratory-and-research works (For example: 5В060200 - Informatics, 5В070200 - Automation and management, 5В070300 - Information systems, 5В070400 - Computer science and software, 5В100200 Information Security Systems).

DET trainees who are just being familiarized with programming may by means of ES develop independently applications where they may receive at once highly qualified maintenance on the work under development.

Using of moduli of speech recognition programs plays the role of no little importance. The speech recognition program allows achieving a higher level of management and fulfillment of the set tasks on conduct of laboratory-and-research works while working remotely from the principal server. Thereby it is possible to create additional opportunities, which simplify the process of data management and entry, mastering of educational programs by the students with the use of distance learning technologies.

Integration of modern decisions of moduli for speech recognition programs to the distance education systems provides for improvement of the educational portal.

Using of the speech recognition programs for DE test programs gives a number of advantages. Many higher educational institutions put in practice the method of provision of information resources in the form of text files or documents and knowledge control is implemented by the method of computer testing (in some cases without the trainee personal identification in the on-line mode). In our opinion, such democratic approach in higher education is inacceptable and rather fits for qualification improvement and etc. For example, Educational Testing Service (ETS) company introduces highly-technological platforms of personal identification for examination conduct for the purpose of providing an impeccable reputation in the world.

While ETS, the test development company itself, has introduced biometrical programs of voice identification for the purpose of increasing the testing safety and also reliable and fair test conduct all over the world. As a supplement to the existing complex safety system of the program for test participant authentication, the proved safety method forms the basis of the recently launched safety measure [6]. Using of such the most modern components improves the ability to detect attempts of receiving unfair advantage that is now the common concern in the academic group of not only Kazakhstan but many open universities of the world [7].

REFERENCES

1. http://www.edu.gov.kz.

2. Makulov K., Otarbayev Zh. Significance of expert systems in distance educational technologies. Human Forum. Barcelona. Spain. 2013.

3. И.К. Корнеев, В.В. Годин. Управление информационными ресурсами. – М.: ИНФРА-М, 2000. – 352 с.

4. http://wikipedia.org/ 5. http://www.ruslion.ru/psyhology 6. http://www.mbastrategy.ua/content 7. Otarbayev Zh., Makulov K. Materials of international scientific-practical conference. University of SCO – New horizons for distance education: Experience, practice and prospects. For the issues of pedagogic communication in learning with the use of distance education technologies. Karaganda. Republic of Kazakhstan. 2013. – 222c.

8. Makulov K., Otarbayev Zh., Yagaliyeva B. Significance of Expert Systems in Distance Educational Technologies // Materials of the VII International Research and Practice Conference Vol. II April 23h – 24th, Munich, Germany 2014. P.69- Аннотация: В статье рассмотрены экспертные системы. Вопросы их применения в образовании с дистанционными образовательными технологиями. Отражены основные недостатки и определение путей их решения в целях создания модуля ЭС для системы дистанционных образовательных технологии.

Адатпа: Бл маалада сарапты жйе арастырылан. ашытан оыту технологиясыны білім беруде олданылу мселелері. ашытан оыту технологиясы жйесі шін сарапты жйе модулін рудаы шешімдерін анытау жолдары мен негізгі кемшіліктері крсетілген.

UDC004.89:004.4:681.

INTELLECTUAL TECHNOLOGY OF IMMUNE NETWORK MODELING

FOR PREDICTION OF MEDICINES PROPERTIES

Abstract. Given article is devoted to development of the intellectual technology, computing algorithms and programs for computer molecular design of the medical products with given properties (on an example barbiturate) with usage immune net modeling. Classification of chemical substances on the prognostic groups is carried out.

Necessary requirements to the intellectual artificial immune system (AIS) for research of the relation between structure and activity of the chemical compounds are considered. The system approach on the basis of association of the chemical structural information processing methods with molecular modeling and the images recognition on the basis of AIS are developed. The intellectual technology immune net forecasting of the barbiturates properties on the basis multialgorithmic approach is offered.

Key words: artificial immune system, intellectual technology, prediction of dependence of the structure – property, barbiturates, computer molecular design.

Introduction

Nowadays, the development of new medicines is a very important task. All over the world conducted the researches on this problem. In the paper [1] authors used artificial neural network, which is a branch of artificial Intelligence (AI). An analysis by neural networks improve the classification accuracy, data quantification. In present study, an effort is being made to prepare the logical assembling of and reduce the number of analogues necessary for correct classification of biological active compounds the various advanced methods which will be circulating around the Artificial Neural Network. It is reported that drug industries need the fast screening of chemical molecule to determine drug like properties in molecules.

Paper [2] describes the implementation of the Tabu Search (TS) algorithm in concert with the Computer-Aided Molecular Design (CAMD) scheme. Although other optimization approaches have been applied to CAMD with properties predicted using group contribution techniques, the TS algorithm implemented with novel neighbor-generating operators and combined with property prediction via connectivity index based correlations provides a powerful technique for generating lists of near-optimal molecular candidates for a given application.

Paper [3] described peptide deformylase (PDF), which is essential in a variety of pathogenic bacteria but it is not required for cytoplasmic protein synthesis in eukaryotes, which makes this enzyme an attractive target for developing novel antibiotics. Authors designed a series of PDF inhibitors and predicted their biological activities using molecular simulation methods. The binding conformations and binding affinities of these inhibitors have been obtained using the flexible docking protocol FlexX. Calculations performed for test compounds suggested that FlexXcan reproduce the binding conformation of the crystal structure. A series of designed PDF inhibitors have been docked to the PDF model and the computed docking scores have been used as a reference standard to evaluate the activities of these inhibitors.

Article [4] presents a dynamic ensemble neural network model for a pharmaceutical drug design problem. By designing a drug, we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem, authors propose a dynamic ensemble neural network model and the performance is compared with several neural network architectures and learning approaches. The idea is to build a dynamic ensemble neural network depicting the dependence between inputs and outputs for the drug design problem. In paper [5] described a construction method and a training procedure for a topology preserving neural network (TPNN) in order to model the sequence-activity relation of peptides. The building blocks of a TPNN are single cells (neurons) which correspond one-to-one to the amino acids of the peptide. The cells have adaptive internal weights and the local interactions between cells govern the dynamics of the system and mimic the topology of the peptide chain. The TPNN can be trained by gradient descent techniques, which rely on the efficient calculation of the gradient by backpropagation.

Article [6] is devoted to prediction of anticancer/non-anticancer drugs. The quantitative structureactivity relationship (QSAR) model developed discriminate anticancer/non-anticancer drugs using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used here is a feed-forward neural network with a standard back-propagation training algorithm. These methods were trained and tested on a non redundant data set of 180 drugs. The proposed model can be used for the prediction of the anti-cancer activity of novel classes of compounds enabling a virtual screening of large databases.

Authors of article [7] present self-organizing map or Kohonen network and counter propagation neural network as powerful tools in quantitative structure property/activity relationship modeling. Two areas of applications are discussed: estimation of toxic properties in environmental research and applications in drug research.

Article [8] is devoted to describe the QSAR method. Antimicrobial peptides are ubiquitous in nature where they play important roles in host defense and microbial control. QSARs method, which attempts to correlate chemical structure to biological measurement, has shown promising results in the optimization and discovery of peptide candidates.

In paper [9] authors reviewed the implementation of genetic algorithm (GA) in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesianregularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments.

Also, in the process of the drug design there is used the relatively new biological approach [10] artificial immune system (AIS). In paper [11] present the first application of the artificial immune recognition system (AIRS) to the recognition of the substrates of the multidrug resistance (MDR) ATP– binding cassette (ABC) transporter permeability glycoprotein (P– glycoprotein, P– gp). We evaluated the AIRS algorithm for a dataset of 201 chemicals. The classifiers were computed from 159 structural descriptors from five classes, namely constitutional descriptors, topological indices, electro topological state indices, quantum descriptors, and geometrical indices. The AIRS algorithm is controlled by eight user defined parameters: affinity threshold scalar, clonal rate, permutation rate, number of nearest neighbors, initial memory cell pool size, number of instances to compute the affinity threshold, stimulation threshold, and total resources. The AIRS predictions are better than those of five of these algorithms (alternating decision tree, Bayesian network, logistic regression with ridge estimator, random tree, and fast decision tree learner), showing that P–gp substrates may be successfully recognized with AIRS. This article focuses on the development of intellectual technology of immune network modeling and "structure-property" relations prediction of unknown chemical compounds that can be considered as candidates for the creation of new drugs.

The structure of the article is follow: section 2 is dedicated to the AIS approach description and to the presentation of a mathematical model of a formal peptide, in Section 3 there are the objectives of the analysis. Section 4 is dedicated to the immune network model construction based on the structure of the compounds. There is also represented an algorithm for the prediction based on AIS and described the process of immune network model optimization. Section 5 presents the results of the modeling. The article ends with section 6 with the conclusion.

Description of the AIS approach Under AIS there is understood an information technology, which use concepts of a theoretical immunology for various applications, including the prediction of properties of new drug compounds. The actual direction of AIS is the approach based on the mathematical implementation of the molecular recognition mechanisms. The advantages of artificial immune system are: distribution, self-organization and evolution, not much demanding of computer resources, the lack of centralized control, self-learning, individual approach to the unique events.

This approach uses the term of a formal peptide [12], as a mathematical abstraction of the free energy protein molecule from its spatial form, which is described in quaternion algebra.

A mathematical model of the formal peptide has the following form:

where n 0 - number of the elements;

Q = {Q 0, Q k } - multitude of the unit quaternion, where quaternion Q k =Q k { k, k } and the resulting quaternion FP 0 are defined as their product function (without index), defined from the elements of the resulting quaternion by the following quadratic form:

This mathematical model of the formal peptide is used in further researches during the immune network model construction that describes the structure of a chemical compound by the descriptors.

Statement of the problem The statement of the problem is formulated as follows: there is a need to develop an intellectual system of forecasting the pharmacological activity of organic compounds on the basis of biological artificial immune systems approach for the selection of promising chemical compounds -candidates for new drugs.

Immune network model construction and prediction of the new substances properties based on AIS The implementation of the proposed intelligent prediction technology of "structure-property" relations includes the following major steps:

1. The choice of chemicals for research.

2. Structure description and classification.

3. Pattern recognition based on AIS.

4. Analysis and evaluation of the properties predicting results of unknown compounds.

The Figure 1 shows a block diagram of prediction of the pharmacological activity of unknown compounds based on immune network modeling.

Figure 1- The block diagram of drugs properties predicting based on AIS The following algorithm is developed on the basis of AIS:

1. Development of a descriptors database for the compound structure description by the numerical parameters.

2. Pre-processing: normalization, centering and data recovery.

3. Immune network model construction based on the descriptors (formal peptides - standards that are regarded as antigens) that describe the structure of the test compound with known properties.

4. Optimal immune network model construction by informative descriptors providing (using factor analysis algorithms and neural networks).

5. Education according to the immune network standards with the teacher and assessment.

6. Formation of the matrix - the images that will be considered as antibodies.

7. The pattern recognition [12] problem solution based on the singular value decomposition (SVD) and finding the minimum energy between formal peptides (antibodies and antigens).

8. Evaluation of the power errors based on homologous proteins.

9. Selection for the further optimization algorithm usage of the immune network (principal component analysis or neural network approach), which gives the minimum error of generalization.

10. Prediction of pharmacological activities of unknown compounds.

11. Selection of connections - drug candidates for further research.

Modeling results Example. Let’s consider the following example of drugs properties predicting in the class of barbiturates.

Barbiturates(barbiturates) - a group of drugs, barbituric acid derivatives (CONHCOCH2CONH), providing a depressing effect on the central nervous system [13]. These drugs have a sedative, anticonvulsant and narcotic effect. Various barbiturates have their lasting effects on the body. There are drugs with longterm (barbital, phenobarbital, barbital sodium), medium (cyclobarbital, barbamil, etaminalsodium) and short (geksobarbital), duration of action. For this example, information about the structures of chemical compounds of barbiturates are taken from the books of E.W. Stuper, U. Bruegger and P. Dzhurs [14].

Experts identify two classes:

Class 1 - strongly acting barbiturates (sleep duration discussed).

Class 2 - weak acting barbiturates.

There is created the descriptors database (Table 1). Reduction of uninformative descriptors and optimal immune network model construction is made by using factor analysis and principal component analysis (PCA) [15].

The procedure of factor analysisis carried out in the statistical treatment of the data package of SPSS.

Component Figure in the rotated space is shown in Figure 2.

As a result of PCA there is obtained a rotated component matrix (Table 2), which shows the most relevant components of the descriptor database, described in the Table1.

During the pattern recognition [16] problem solving experts form the standard matrix for the two classes: A1 и A2, which are formed as the result of the respective coagulation of the time series in the Table 2 for improvement the recognition specificity [12].

There is carried out the procedure of singular value decomposition (SVD). We calculate their left and right singular vectors: {x1, y1}, {x2, y 2 }. Then, there are formed the matrix images: B1, B2,..... Bm ( m number of images), which are also obtained as a result of folding of matrix time series.

After training on AIS standards there is introduced the matrix of images and found a measure of intimacy of our image with the standards. The binding energies between the standards and images (antigens and antibodies) can be calculated by the formulas:

where T - the symbol of transposition. The minimum value of the binding energy indicates the class to which the image belongs to:

where k - the number of the class. The results are shown in the Table 3.

According to the calculated formula of the binding energy there is a minus, then in the table 3 there is shown the maximum value belonging to the first or second class. Figure 3 shows the dependence of the minimum energy of W1 and W2.

Then there is an evaluation of the energy errors based on the properties of homologous proteins [17], which allows you to identify the peptides on the borders of the nonlinear separated classes with are nearly of the same parameters.

Figure 3 - Graphical visualization of the energy minimum between formal peptides These lection of informative descriptors and optimal immune network model construction is also possible using the neural network approach and the application package of NeuroShell [18-19]. Highperformance parallel computing allows to select the best optimization algorithm that gives the minimum error of generalization for a particular set of multidimensional data.

Conclusion

Thus immune network approach can be successfully applied to the modeling of dependencies "Structure - Property" of the drugs. The advantages of this smart technology is the ability to use in immune network model constructing the descriptors of different levels. To analyze the latent dependencies on the basis of homologous proteins in order to increase the accuracy of the prediction. The use of the technology can significantly reduce the financial and time resources for the selection of candidate materials with desired properties for further investigation as a new drug.

At designed software there is received the copyright certificate of state registration of intellectual property, registered in the Committee on Intellectual Property of the Ministry of Justice of the Republic of Kazakhstan [20].

This work was performed as a part of the project «Intellectual technology of immune network modeling of drugs» funded by the KN RK State registration number: 0112RK02222.

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9. Fernandez M., Caballero J., Fernandez L., Sarai A. Genetic algorithm optimization in drug design QSAR:

Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM) //Molecular Diversity. -Sрringer:2011. – Vol. 15, №1. – Р. 269- 289.

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12. Tarakanov A.O. Formal peptide as a basic of agent of immune networks: from natural prototype to mathematical theory and applications //Proceedings of the International Workshop of Central and Eastern Europe on Multi-Agent Systems. -1999. -P. 37.

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14. E. Stuler, U. Bruegger, P. Dzhurs. Computer analysis of the relation ship of the chemical structure and biological activity. –Мoscow: Mir, 1982. – 240 p.

15. К. Iberla. Factor analysis. – Moscow: Statistics – 1980. – 235 p.

16. Samigulina.G. A., Chebejko S.V. Development of immune network modeling technologies for computer simulation molecular design of drugs. –Harkov: 2011. – №17. – Р. 142-148.

17. Samigulina G. A. Development of the decision support systems on the basis of the intellectual technology of the artificial immune systems //Automation and remote control. –Springer:2012. – Vol. 74, №2. –Р. 397-403.

18. Samigulina G. A, Samigulina Z.I. Development of the optimal immune network model construction for for the prediction of properties of the unknown drug compounds. –Prague, Science and education: 2013. – Р. 4-6.

19. Samigulina G. A, Samigulina Z.I. Development of the optimal immune network model construction for the prediction of properties of the unknown drug compounds based on the multi algorithmic approach //Problems of Informatics. –Novosibirsk:2013. - №2. - Р. 21-29.

20. Samigulina G.A., Samigulina Z.I. Development of the immune network model technology for the computational molecular design of drugs (computer program). Certificate of state registration of rights to copyright in the Committee on Intellectual Property Rights, Ministry of Justice of RK. Astana, 28th of March 2011. №473(23).

Интеллектуальная технология прогнозирования свойств новых лекарственных препаратов Резюме. Данная статья посвящена разработке интеллектуальной технологии, вычислительных алгоритмов и программ для компьютерного молекулярного дизайна лекарственных препаратов с заранее заданными свойствами (на примере барбитуратов) с использованием иммунносетевого моделирования.

Осуществлена классификация химических веществ по прогностическим группам. Рассмотрены необходимые требования к интеллектуальной искусственной иммунной системе (ИИС) для исследования связи между структурой и активностью химических соединений. Разработан системный подход на основе объединения методов обработки химической структурной информации с молекулярным моделированием и распознаванием образов на основе ИИС. Предложена интеллектуальная технология иммунносетевого прогнозирования свойств барбитуратов на основе мультиалгоритмического подхода.

Ключевые слова: искусственная иммунная система, интеллектуальная технология, прогнозирование зависимости структура – свойство, барбитураты, компьютерный молекулярный дизайн.

Жасанды иммунды жйелерге негізделген жаа дрілік препараттарды ерекшеліктерін Tйіндеме. Бл маала иммунды желіні моделдеуді олданып алдын - ала берілген асиеттерімен (барбитуратар мысалында) емдік дрі - дрмектерді компьютерлік молекулярлы лгісіне арналан бадарламалар жне есептеу алгоритмдері зерделі технологиясын руа арналан. Болжамалы топтар бойынша химиялы заттарды топтастыру іске асырылды. Белсенді химиялы байланыс жне рылым арасындаы байланысты зеттеу шін зерделі жасанды иммунды жйеге (ЗЖИЖ) ажетті талаптар арастырылды. ЗЖИЖ негізіне бейне тану жне молекулярлы моделдеумен химиялы рылымды апараттарды деу дісін біріктіру негізінде жйелік тсіл рылды. Мульти алгоритмдік тсіл негізінде барбитураттар асиетін иммунды желілі болжауды зерделі технологиясы сынылды.

Tйін сздер: жасанды иммунды жйе, зерделі технология, асиет – ралыма байланысты болжау, компьютерлі молекулярлы лгі.

УДК 004.056. Казахский национальный технический университет имени К.И. Сатпаева

ПРЕДЛАГАЕМЫЕ ПРИНЦИПЫ ОБЕСПЕЧЕНИЯ БЕЗОПАСНОСТИ ПРОГРАММНОГО

Аннотация. В представленной статье рассматривается принципы обеспечения технологической безопасности при обосновании, планировании работ и проектном анализе ПО, принципы обеспечения технологической безопасности на этапах стендовых и приемосдаточных испытаний, принципы обеспечения безопасности при эксплуатации программного обеспечения.

Ключевые слова: принципы обеспечения технологической безопасности при обосновании, планировании работ и проектном анализе ПО, статистический анализ информации.

В качестве объекта обеспечения технологической и эксплуатационной безопасности ПО рассматривается вся совокупность его компонентов в рамках конкретной. В качестве доминирующей должна использоваться стратегия сквозного тотального контроля технологического и эксплуатационного этапов жизненного цикла компонентов ПО. Совокупность мероприятий по обеспечению технологической и эксплуатационной безопасности компонентов ПО должна носить, по возможности, конфиденциальный характер. Необходимо обеспечить постоянный, комплексный и действенный контроль за деятельностью разработчиков и пользователей компонентов ПО. Кроме общих принципов, обычно необходимо конкретизировать принципы обеспечения безопасности ПО на каждом этапе его жизненного цикла. Далее приводятся один из вариантов разработки таких принципов [1].

Принципы обеспечения технологической безопасности при обосновании, планировании работ и проектном анализе ПО. Принципы обеспечения безопасности ПО на данном этапе включает следующие принципы [1]:

- Комплексности обеспечения безопасности ПО, предполагающей рассмотрение проблемы безопасности информационно - вычислительных процессов с учетом всех структур КС, возможных каналов утечки информации и несанкционированного доступа к ней, времени и условий их возникновения, комплексного применения организационных и технических мероприятий.

- Планируемости применения средств безопасности программ, предполагающей перенос акцента на совместное системное проектирование ПО и средств его безопасности, планирование их использования в предполагаемых условиях эксплуатации.

- Обоснованности средств обеспечения безопасности ПО, заключающейся в глубоком научнообоснованном подходе к принятию проектных решений по оценке степени безопасности, прогнозированию угроз безопасности, всесторонней априорной оценке показателей средств защиты.

- Достаточности защищенности программ, отражающей необходимость поиска наиболее эффективных и надежных мер безопасности при одновременной минимизации их стоимости.

- Гибкости управления защитой программ, требующей от системы контроля и управления обеспечением безопасности ПО способности к диагностированию, опережающей нейтрализации, оперативному и эффективному устранению возникающих угроз.

- Заблаговременности разработки средств обеспечения безопасности и контроля производства ПО, заключающейся в предупредительном характере мер обеспечения технологической безопасности работ в интересах недопущения снижения эффективности системы безопасности процесса создания ПО.

- Документируемости технологии создания программ, подразумевающей разработку пакета нормативно-технических документов по контролю программных средств на наличие преднамеренных дефектов.

разработки. Принципы обеспечения безопасности ПО на данном этапе включают следующие принципы:

- Регламентации технологических этапов разработки ПО, включающей упорядоченные фазы промежуточного контроля, спецификацию программных модулей и стандартизацию функций и формата представления данных.

- Автоматизации средств контроля управляющих и вычислительных программ на наличие преднамеренных дефектов.

- Создания типовой общей информационной базы алгоритмов, исходных текстов и программных средств, позволяющих выявлять преднамеренные программные дефекты.

- Последовательной многоуровневой фильтрации программных модулей в процессе их создания с применением функционального дублирования разработок и поэтапного контроля.

- Типизации алгоритмов, программ и средств информационной безопасности, обеспечивающей информационную, технологическую и программную совместимость, на основе максимальной их унификации по всем компонентам и интерфейсам.

- Централизованного управления базами данных проектов ПО и администрирование технологии их разработки с жестким разграничением функций, ограничением доступа в соответствии со средствами диагностики, контроля и защиты.

- Блокирования несанкционированного доступа соисполнителей и абонентов государственных и негосударственных сетей связи, подключенных к стендам для разработки программ.

- Статистического учета и ведения системных журналов о всех процессах разработки ПО с целью контроля технологической безопасности. Использования только сертифицированных и выбранных в качестве единых инструментальных средств разработки программ для новых технологий обработки информации и перспективных архитектур вычислительных систем.

Принципы обеспечения технологической безопасности на этапах стендовых и приемосдаточных испытаний [1]. Принципы обеспечения безопасности ПО на данном этапе включают принципы:

- Тестирования ПО на основе разработки комплексов тестов, параметризуемых на конкретные классы программ с возможностью функционального и статистического контроля в широком диапазоне изменения входных и выходных данных.

- Проведения натурных испытаний программ при экстремальных нагрузках с имитацией воздействия активных дефектов.

- Осуществления «фильтрации» программных комплексов с целью выявления возможных преднамеренных дефектов определенного назначения на базе создания моделей угроз и соответствующих сканирующих программных средств. Разработки и экспериментальной отработки средств верификации программных изделий.

- Проведения стендовых испытаний ПО для определения непреднамеренных программных ошибок проектирования и ошибок разработчика, приводящих к невыполнению целевых функций программ, а также выявление потенциально «узких» мест в программных средствах для разрушительного воздействия. Отработки средств защиты от несанкционированного воздействия нарушителей на ПО.

- Сертификации программных изделий по требованиям безопасности с выпуском сертификата соответствия этого изделия требованиям технического задания.

Принципы обеспечения безопасности при эксплуатации программного обеспечения [1].

Принципы обеспечения безопасности ПО на данном этапе включают принципы:

- Сохранения эталонов и ограничения доступа к ним программных средств, недопущение внесения изменений в эталоны. Профилактического выборочного тестирования и полного сканирования программных средств на наличие преднамеренных дефектов.

- Идентификации ПО на момент ввода его в эксплуатацию в соответствии с предполагаемыми угрозами безопасности ПО и его контроль.

- Обеспечения модификации программных изделий во время их эксплуатации путем замены отдельных модулей без изменения общей структуры и связей с другими модулями.

- Строгого учета и каталогизации всех сопровождаемых программных средств, а также собираемой, обрабатываемой и хранимой информации.

- Статистического анализа информации обо всех процессах, рабочих операциях, отступлениях от режимов штатного функционирования ПО.

- Гибкого применения дополнительных средств защиты ПО в случае выявления новых, непрогнозируемых угроз информационной безопасности.

Согласно ГОСТ 28195-89 качеством программного обеспечения называется совокупность потребительских свойств ПО, характеризующих способность программного обеспечения удовлетворять потребность пользователей в обработке данных в соответствии с назначением [2].

ЛИТЕРАТУРА

1. Казари О.В. Безопасность программного обеспечения компьютерных систем. М.: МГУЛ, 2003, 212с.

2. ГОСТ 28195-89: Оценка качества программных средств. Общие положения. – М.: Издательство стандартов, 1989.

Бадарламалы амтама (б) ауіпсіздігін амтамалауды сынылатын принциптері Тйіндеме. Берілген маалада негіздеме, жоспарлау жмыстары мен Б жобалы талдау кезіндегі технологиялы ауіпсіздікті амтамалауды принциптері, стендтік жне абылдау ткізу сынатары кезедеріндегі технологиялы ауіпсіздікті амтамасыз ету принциптері, бадарламалы амтамалауды пайдалану кезіндегі ауіпсіздікті амтамасыз ету принциптері арастырылады.

Тйін сздер: негіздеме, жоспарлау жмыстары мен Б жобалы талдау кезіндегі технологиялы ауіпсіздікті амтамалауды принциптері, апаратты статистикалы талдау.

Summary. In the present article deals with the principles of process safety in the justification, planning work and project analysis software, principles of process safety during bench and acceptance tests, the principles of safety in the operation of the software.

Key words: principles of process safety in the justification, planning work and project analysis software, statistical analysis of data.

УДК 004.9 (514) Казахский национальный технический университет имени К.И. Сатпаева

СОВЕРШЕНСТВОВАНИЕ И ПРАКТИКА ПРИМЕНЕНИЯ КОМПЬЮТЕРНЫХ

ТЕХНОЛОГИЙ В ПРОЦЕССЕ ПРОГНОЗИРОВАНИЯ ПОТРЕБНОСТИ В ЗАПАСАХ НА

Аннотация. Торговые и производственные предприятия имеют запасы. Но запасы, в свою очередь, влекут за собой затраты на их хранение и обслуживание. Следовательно, основной проблемой данных предприятий является снижение издержек на содержание запасов. А для этого необходимо выбрать правильную политику управления запасами.

Для эффективного управления запасами необходимо усовершенствовать процесс прогнозирования (планирования) потребностей в них, поэтому основная цель написания статьи заключается в изучении сущности управления запасами на предприятии и повышение эффективности управления запасами посредством снижения издержек, связанных с объемом заказа, путем применения информационных систем для автоматизации процесса формирования потребности в запасе и планирования заказа.

В данной статье описана логистическая концепция планирования потребности в запасе и дается обзор функционала программных решений в области управления запасами. Приведено подробное описание рассматриваемой темы с детальным разбором и иллюстрированием всех этапов процесса прогнозирования потребностей в запасах. Примеры моделировались с использованием демонстрационной базы программы «1С:

Управление торговым предприятием».

Ключевые слова: прогнозирование потребности в запасе, «1С:Управление торговым предприятием», точка заказа, помощник планирования, календарный план закупок.

Текст статьи: Основой коммерческой деятельности на торговых предприятиях является закупочная работа запасов с целью удовлетворения потребительского спроса и получения прибыли.

Известно, что избыток запасов у предприятия могут привести к увеличению затрат на их хранение, неполучению возможных доходов из-за замораживания финансовых ресурсов в запасах, потерям в результате физической порчи и моральному старению запасов. Но при этом, дефицит запасов приводит к падению объемов сбытовой деятельности компании.

Поэтому торговым компаниям следует поддерживать необходимый уровень запасов. Для этого необходимо правильно планировать количество поступающих запасов на предприятие, т.е.

оптимизировать процесс закупа [1].

Подсистему, решающую задачи планирования, можно считать ведущей. Подсистема планирования предприятия дает ответы на следующие вопросы:

какие товары и услуги предприятие собирается предложить рынку?

что для этого требуется?

Ответы даются с учетом возможностей подсистемы планирования в «1С: Предприятие 8.

Управление торговым предприятием» в детализированном виде, ставя реальные задачи оперативного управления запасам.

Конфигурация «Управление торговым предприятием 8» позволяет осуществлять планирование:

при помощи «Помощника планирования» - оставление планов продаж и закупок;

объемно-календарное планирование закупок при помощи документа «Формирование потребностей»;

планирование «по точке заказа»;

при помощи обработки «Календарный план закупок» [2].

Конфигурация «Управление торговым предприятием» позволяет автоматически сформировать план продаж и закупок на период при помощи обработки «Помощник планирования» (меню «Планирование Помощник планирования, смотрите рисунок 1).

Рисунок 1 – Экранная форма обработки «Помощник планирования»

Работа с обработкой начинается с закладки «Конечные планы» - первой закладки «Помощника планирования». Здесь определяется список конечных планов, которые требуется получить.

Для каждого конечного плана указываются его параметры, причем некоторые из параметров являются обязательными для заполнения:

вид плана – план закупок или план продаж;

сценарий планирования;

период плана – дата начала планирования и дата конца планирования.

Профиль распределения задается в том случае, если нужно перейти от планов с более длительным периодом с более коротким периодом.

На следующей закладке «Стратегия расчета количества» определяются исходные данные, которые будут использоваться для получения состава и количества показателей формируемого плана.

Каждая стратегия характеризуется видом стратегии, который определяет источник, используемый для получения данных:

заказы поставщикам;

заказы покупателей;

внутренние заказы;

складские остатки;

плановые остатки.

Третья закладка «Стратегия расчета суммы» задает алгоритм расчета суммовых параметров плана. Данная закладка показана на рисунке 2.

Допустимы три метода расчета:

из источника расчета количества. В таком случае расчет суммы производиться не будет, и данные о суммовых показателях будут приняты из источников принятия количественных параметров.

К примеру, для методики расчета количества «Объемы закупок» данные о суммовых показателях будет приняты из реальных продаж. Подобно и для иных стратегий.

по типу цен номенклатуры. В таком случае будет выполняться расчет суммовых параметров на основе цен номенклатуры. Если тип цен номенклатуры определен, то конкретно по этому типу цен и будут вычислены суммовые параметры. В ситуации не определения типа цен будет выполняться расчет сумм по всем существующим типам цен учитывая заданного метода расчета («Максимум», «Среднее» либо «Минимум»).

по типу цен контрагентов. В таком случае будет выполняться расчет суммовых параметров на основе цен номенклатуры контрагентов. В виде типа цены имеет возможность быть определен тип цен номенклатуры контрагентов либо тип цен номенклатуры [3].

В первом случае, если тип цен номенклатуры контрагентов определен, то конкретно по этому типу цен и будут вычислены суммовые параметры.

Во втором случае, если тип цен номенклатуры определен, то расчет цен будет выполняться среди тех типов цен номенклатуры контрагентов, для которых в виде типа цены номенклатуры определен указанный тип цены номенклатуры. В таком случае будет выполняться расчет цен учитывая заданного метода расчета («Максимум», «Среднее» либо «Минимум»).

В ситуации не определения типа цен будет выполняться расчет сумм по всем существующим типам цен учитывая заданного метода расчета («Максимум», «Среднее» либо «Минимум»).

Закладка «Отборы». Перечень доступных полей фильтрации разделен по видам методике расчета количества. Таким образом, для назначения фильтрации по какому или параметру требуется включение фильтрации по каждой из отобранных стратегий. Перечень доступных отборов меняется динамически и зависит от отобранных стратегий расчета суммы. Для назначения фильтрации по свойствам и категориям объектов требуется включить флажок «Использовать свойства и категории»

в нижней части страницы «Отборы». Вкладка отборы показана на рисунке 3.

По завершении ввода параметров требуется выполнить создание нажатием кнопки «Выполнить» нижней меню формы. В ситуации вызова обработки из формы документов «План закупок» и «План продаж» и успешного выбора и расчета параметров будет занесена нижняя таблица соотносящегося документа. Сформированные документы будут помещены в табличное поле на странице «Сформированные документы» [4].



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