Production system shell for mobile Devices

The CLIPS shell extension was proposed to enable the creation of applied production systems which can be used on mobile devices. The innovation was substantiated in terms of saving money resources to purchase specialized equipment for using for the resource-intensive production systems. The following studies were conducted: market research tablet devices, the most widely represented in Ukraine; production systems shells that can be used on mobile devices; freely distributable production systems shells were considered, important characteristics were identified and compared. The basic classes of match algorithms were overviewed. The results of studies about Rete and Treat match algorithms advisability were described for the general case of the applied problems. The proposed modeling environment for production systems for mobile devices will reduce development time and increase system efficiency by choosing the optimal match algorithm for minimal memory usag

Вибір оптимального алгоритму співставлення зі зразком при проектуванні продукційної системи

The garment industry quickly becomes a highly developed branch due to the rapid development of technologies that contribute to high-quality design, cutting, manufacture. However, some design stages have not yet been formalized. For solving unformalized tasks, the expert systems are used. The research deals with developing the expert system prototype for rapid reorientation of women’s outerwear production. To form a subject environment, the textual method is used. Factor and cluster analyses are used to structure the subject environment. Thus, the main objective of the study is achieved by forming twelve individual tasks according to the number of individual groups, allocated in the subject environment of rapid reorientation of women’s outerwear production. Selection rules of transformation chain and values of additions at the level of chest, waist and hips are formed in tables. In each table, results are obtained at the intersection of several conditions.The expert system prototype for flexible reorientation of women’s outerwear production is designed by using the empty expert system “Rapana”. The expert system prototype implements a dialogue with the user as a series of questions and answers of the user. Some answers can have a degree of confidence. The user can revise the way of decision-making after obtaining the results. Thus, necessary conditions for further development of artificial intelligence methods in the garment production design training management and for reducing risks of wrong decision-making in conditions of rapid change in project situations are createdПредложена совокупность характеристик продукционной системы, которая зависит от формализованного представления прикладной задачи и определяют  эффективность логического вывода. На основании данных характеристик предложено схему определения оптимального по быстродействию и затратам памяти алгоритма сопоставления с образцом на этапе проектирования продукционной системы. Приведен  пример выбора алгоритма для задачи диагностирования башенной градирни.Запропонована сукупність характеристик продукційної системи, які залежать від формалізованого представлення прикладної задачі та впливають на ефективність логічного виведення. На основі даних характеристик запропоновано схему визначення оптимального за швидкодією та затратами пам’яті алгоритму співставлення зі зразком на етапі проектування продукційної системи. Приведено приклад вибору алгоритму для задачі діагностування баштової градирні

Вибір оптимального алгоритму співставлення зі зразком при проектуванні продукційної системи

The garment industry quickly becomes a highly developed branch due to the rapid development of technologies that contribute to high-quality design, cutting, manufacture. However, some design stages have not yet been formalized. For solving unformalized tasks, the expert systems are used. The research deals with developing the expert system prototype for rapid reorientation of women’s outerwear production. To form a subject environment, the textual method is used. Factor and cluster analyses are used to structure the subject environment. Thus, the main objective of the study is achieved by forming twelve individual tasks according to the number of individual groups, allocated in the subject environment of rapid reorientation of women’s outerwear production. Selection rules of transformation chain and values of additions at the level of chest, waist and hips are formed in tables. In each table, results are obtained at the intersection of several conditions.The expert system prototype for flexible reorientation of women’s outerwear production is designed by using the empty expert system “Rapana”. The expert system prototype implements a dialogue with the user as a series of questions and answers of the user. Some answers can have a degree of confidence. The user can revise the way of decision-making after obtaining the results. Thus, necessary conditions for further development of artificial intelligence methods in the garment production design training management and for reducing risks of wrong decision-making in conditions of rapid change in project situations are createdПредложена совокупность характеристик продукционной системы, которая зависит от формализованного представления прикладной задачи и определяют  эффективность логического вывода. На основании данных характеристик предложено схему определения оптимального по быстродействию и затратам памяти алгоритма сопоставления с образцом на этапе проектирования продукционной системы. Приведен  пример выбора алгоритма для задачи диагностирования башенной градирни.Запропонована сукупність характеристик продукційної системи, які залежать від формалізованого представлення прикладної задачі та впливають на ефективність логічного виведення. На основі даних характеристик запропоновано схему визначення оптимального за швидкодією та затратами пам’яті алгоритму співставлення зі зразком на етапі проектування продукційної системи. Приведено приклад вибору алгоритму для задачі діагностування баштової градирні

Production system shell for mobile Devices

The CLIPS shell extension was proposed to enable the creation of applied production systems which can be used on mobile devices. The innovation was substantiated in terms of saving money resources to purchase specialized equipment for using for the resource-intensive production systems. The following studies were conducted: market research tablet devices, the most widely represented in Ukraine; production systems shells that can be used on mobile devices; freely distributable production systems shells were considered, important characteristics were identified and compared. The basic classes of match algorithms were overviewed. The results of studies about Rete and Treat match algorithms advisability were described for the general case of the applied problems. The proposed modeling environment for production systems for mobile devices will reduce development time and increase system efficiency by choosing the optimal match algorithm for minimal memory usage.В роботі запропоновано розширення програмної оболонки CLIPS для забезпечення можливості створення прикладних продукційних систем, використовуваних на мобільних пристроях. Обґрунтована інновація с точки зору економії грошових ресурсів на придбання спеціалізованих пристроїв для використання ресурсоємних продукційних систем. Проведено наступні дослідження: ринку планшетних пристроїв, найширше представлених в Україні; оболонок продукційних систем, які можуть застосовуватися на мобільних пристроях; більш детально, з виділенням значимих характеристик, розглянуто вільно поширювані обгортки продукційних систем. Описано основні класи алгоритмів співставлення зі зразком. Представлені результати досліджень щодо доцільності застосування Rete та Treat для загальних випадків в прикладних задач

An analysis of the efficiency of ontology and symbolic learning algorithms in indigenous knowledge representation

It is without a doubt that machine learning has been the area of focus in early days of artificial intelligence, but the early neural networks approach suffered some shortcomings and this led to a temporary decline in research capacity. New symbolic learning techniques have emerged since then which have yielded promising results and have led to a revival in research in machine learning. This has seen many researchers focusing on these techniques and experimenting with them by comparing their performances for different applications. With that in mind, the research thus decided to make an analysis of the symbolic approach against other approaches such as the neural network (connectionist) to evaluate the power of the former approach. This was done by first generating an ontology that acted as a representation of some collected indigenous knowledge. It is from this ontology that a dataset was generated. The dataset was made ambiguous to see the learning power of classifiers in such data. Two experiments were done, one using WEKA and the other using Orange as tools. The reason why the two experiments were used is because there was not a single tool which contained all the required learning algorithms. The research wanted to make use of ID3 and CN2 symbolic algorithm. However, WEKA had ID3 and not CN2 while Orange had CN2 and not ID3. The most important attributes from the ontology regarding the indigenous knowledge were the name of the plant, the province it is found and the disease the plant treats. Therefore the dataset had two features which were disease and province and one label which was the name of the plant. The learning algorithm was to use the two features to generate rules used to predict the label. However, there was ambiguity on the dataset. The challenge was that two different labels would contain the same features, thus leading to wrongful classification. This was the core of the research. Even though the learning model concluded this situation as wrongful classification, in real time, a system using the same learning model would provide desired and correct results. The only flow which was there is that the learning model simply used one label to predict under and ignore the other label with similar features. This was identified as a flow for both symbolic and non-symbolic algorithms. There is no way of giving suggestions in the case a user wants a different plant but with similar features. Therefore for classification using an ambiguous dataset, both these approaches proved to have the fore mentions flow. The research then decided to use recall to analyze the power of these approaches. It was discovered that ID3 has better recall than Multilayer perceptron and Naïve Bayes algorithms when using a training set. ID3 managed to recall clearly and effectively three of its classes by a probability of 1 while Bayes Net had only one class with recall probability of 1. To further investigate the issue of recall, cross validation was used to contrast the competence of recall of the three algorithms to strengthen the assertion that indeed ID3 has a better recall as compared to the other two algorithms. Three stages of cross-validation were done, one stage using 10 fold, the other 20 fold, and the last using 50 fold. For all the different stages of crossvalidation, Bayes Net proved to perform better in terms of recall than the other two algorithms. In cross-validation, MLP could recall approximately above 88% of the instances available in contrast to when using training set where the algorithm recall only two out of 18 instances. In overall the symbolic approach proved to be a commendable approach for use over the nonsymbolic approach. The study of machine learning involves the building of learning algorithms, improving upon learning algorithms or making comparisons of machine learning algorithms. The research raised awareness on some improvements that need to be done on not only symbolic algorithms but non-symbolic ones as well. Some improvements include improving on or coming up with algorithms that suggest alternative predictions in cases of ambiguity instead of doing wrongful classification and not reflect on other possibilities

A situational awareness model for data analysis on 5G mobile networks : the SELFNET analyzer framework

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 14-07-2017Se espera que las redes 5G provean un entorno seguro, con able y de alto rendimiento con interrupciones m nimas en la provisi on de servicios avanzados de red, sin importar la localizaci on del dispositivo o cuando el servicio es requerido. Esta nueva generaci on de red ser a capaz de proporcionar altas velocidades, baja latencia y mejor Calidad de Servicio (QoS) comparado con las redes actuales Long Term Evolution (LTE). Para proveer estas capacidades, 5G propone la combinaci on de tecnolog as avanzadas tales como Redes De nidas por Software (SDN), Virtualizaci on de las Funciones de Red (NFV), Redes auto-organizadas (SON) e Inteligencia Arti cial. De manera especial, 5G ser a capaz de solucionar o mitigar cambios inesperados o problemas t picos de red a trav es de la identi caci on de situaciones espec cas, tomando en cuenta las necesidades del usuario y los Acuerdos de Nivel de Servicio (SLAs). Actualmente, los principales operadores de red y la comunidad cient ca se encuentran trabajando en estrategias para facilitar el an alisis de datos y el proceso de toma de decisiones cuando eventos espec cos comprometen la salud de las redes 5G. Al mismo tiempo, el concepto de Conciencia Situacional (SA) y los modelos de gesti on de incidencias aplicados a redes 5G est an en etapa temprana de desarrollo. La idea principal detr as de estos conceptos es prevenir o mitigar situaciones nocivas de manera reactiva y proactiva. En este contexto, el proyecto Self-Organized Network Management in Virtualized and Software De ned Networks (SELFNET) combina los conceptos de SDN, NFV and SON para proveer un marco de gesti on aut onomo e inteligente para redes 5G. SELFNET resuelve problemas comunes de red, mientras mejora la calidad de servicio (QoS) y la Calidad de Experiencia (QoE) de los usuarios nales...5G networks hope to provide a secure, reliable and high-performance environment with minimal disruptions in the provisioning of advanced network services, regardless the device location or when the service is required. This new network generation will be able to deliver ultra-high capacity, low latency and better Quality of Service (QoS) compared with current Long Term Evolution (LTE) networks. In order to provide these capabilities, 5G proposes the combination of advanced technologies such as Software De ned Networking (SDN), Network Function Virtualization (NFV), Self-organized Networks (SON) or Arti cial Intelligence. In particular, 5G will be able to face unexpected changes or network problems through the identi cation of speci c situations, taking into account the user needs and the Service Level Agreements (SLAs). Nowadays, the main telecommunication operators and community research are working in strategies to facilitate the data analysis and decision-making process when unexpected events compromise the health in 5G Networks. Meanwhile, the concept of Situational Awareness (SA) and incident management models applied to 5G Networks are also in an early stage. The key idea behind these concepts is to mitigate or prevent harmful situations in a reactive and proactive way. In this context, Self-Organized Network Management in Virtualized and Software De ned Networks Project (SELFNET) combines SDN, NFV and SON concepts to provide a smart autonomic management framework for 5G networks. SELFNET resolves common network problems, while improving the QoS and Quality of Experience (QoE) of end users...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu