Dina Vladimirovna Kataseva

Bio: Dina Vladimirovna Kataseva is an academic researcher from Kazan State Technical University named after A. N. Tupolev. The author has contributed to research in topics: Artificial neural network & Fuzzy logic. The author has an hindex of 5, co-authored 18 publications receiving 70 citations.

Neuro-fuzzy model of complex objects approximation with discrete output

Abstract: This paper solves the task of complex objects approximation with a discrete output based on information approach to modeling. We propose a model of fuzzy rules and the inference algorithm on the rules, and describe the neuro-fuzzy model for generation of a knowledge base. The approximation of known data sets and comparison of the results with those of other authors is performed. Examples of knowledge bases generation of the expert diagnostic systems in medicine, oil industry and information security show effectiveness of the proposed approach.

Neuro-Fuzzy Model in Supply Chain Management for Objects State Assessing

Abstract: This article considers the task of objects state assessing in conditions of uncertainty by considering the supply chain strategy. To solve it, the need to use fuzzy-production knowledge bases and fuzzy inference algorithms as part of fuzzy decision support systems is being updated. As a tool for constructing a knowledge base, a neural-fuzzy model is proposed. The proposed type of fuzzy-production rules and the logic inference algorithm on rules for objects state assessing are described. A structure of a fuzzy neural network, consisting of six layers, each of which implements the corresponding stage of the logic inference algorithm, is proposed. As a result of training a fuzzy neural network, a system of fuzzy-production rules is formed, which make up the knowledge base of the decision support system for objects state assessing. On the basis of the proposed neuro-fuzzy model, a software package has been implemented for automating the processes of forming fuzzy-production rules. The main components of the software package are the knowledge base generation module and the fuzzy inference module. As an approbation of the neuro-fuzzy model, the formation of fuzzy rules for assessing the state of water lines at the cluster pumping stations in reservoir pressure maintenance systems has been carried out. The testing results confirmed the high efficiency of the neural-fuzzy model and the possibility of its practical use for the formation of fuzzy-production rules in various subject areas of human activity.

Neural network diagnosis of anomalous network activity in telecommunication systems

Abstract: This paper describes the technology of artificial neural network application to solve the problem of anomalous network activity diagnosis. We offer methods for network activity data collection and training set formation. We select network packets parameters whose values together with network activity characteristics constitute the sample for artificial neural network training. We offer artificial neural network structure, train this network, estimate it's value and classification ability. We show the possibility of the effective use of artificial neural network model composed of intelligent system of anomalous network activity diagnosis.

Expert diagnostic system of water pipes gusts in reservoir pressure maintenance processes

Abstract: This paper solves the problem of water pipes state diagnostics in reservoir pressure maintenance processes. The solution of this problem actualizes the need of fuzzy expert system development for rapid identification of water pipe gusts. We present the structure of the system and description of its components. The main component of the expert system is a knowledge base consisting of fuzzy production rules systems. We used a fuzzy neural network to elaborate rules of knowledge base generation. We describe the features and results of knowledge base formation and present the developed method of water pipes leaks detection. The expert diagnostic system was successfully tested and commissioned. As a result, the system implementation has increased efficiency and accuracy of water pipes condition diagnostics.

Fuzzy Multi-Criterial Choice of Geological and Technical Measures

Abstract: The work solves the problem of automating the process planning of assigning geological and technical measures (GTM) at oil fields in conditions of uncertainty. A decision support system is being developed to help an expert make an informed decision about the method of influence of geological and technical measures on an oil reservoir. From the point of view of the imposed restrictions on the choice of geological and technical measures, various types of geological and physical parameters are highlighted. To solve this problem, a fuzzy-production model is proposed for the representation of expert knowledge. A feature of this model is the possibility of different types of parameters use to impose restrictions on the choice of geological and technical measures, using fuzzy restrictions and setting their weights, as well as formalizing the degree of an expert confidence in the reliability of the rule being formed. They provided the possibility of fuzzy modifier use in the conditions of fuzzy production rules for fuzzy constraint correction. To determine the weights of fuzzy constraints in the conditions of the rules, an approach is used based on a multi-criteria assessment of constraints, carried out using the hierarchy analysis method (HAM). The following were used as the criteria for evaluation the weights: the importance of the corresponding geological and physical parameter for an expert, the completeness of the available information on the studied parameter, the relevance of the values, the complexity of obtaining the values. The final choice of geological and technical measures is carried out on the basis of a fuzzy multi-criteria choice according to the following criteria: satisfaction of the fuzzy production model limitations, high technological efficiency, high economic effect, and the impact on the environment. Based on the knowledge of experts, a knowledge base has been formed that includes fuzzy production rules for choosing 81 different geological and technical measures at production wells using the restrictions on 15 geological and physical parameters. The knowledge base has been tested at the wells of the Feofanovskoye field, Alkeevskaya, Chishminskaya areas. The development of recommendations was carried out in conditions of information incompleteness on a number of parameters of the set. The results generated by the decision support system correspond to the decisions made by the experts. KeywordsKnowledge Base, Fuzzy Logic, Fuzzy Production Model, Geological And Technical Event, Hierarchy Analysis Method, Decision Support

A Reputation Value-Based Early Detection Mechanism Against the Consumer-Provider Collusive Attack in Information-Centric IoT

Abstract: As the Internet of Things (IoT) has connected large number of devices to the Internet, it is urgently needed to guarantee the low latency, security, scalable content distribution of the IoT network. The benefits of Information-Centric Networking (ICN) in terms of fast and efficient data delivery and improved reliability have raised ICN as a highly promising networking model for IoT environments. However, with the widely spread of the viruses and the explosion of kinds of network devices, the attackers can easily control the devices to form a botnet such as the Mirai. Once the devices are under control, the attackers can launch a consumer-provider collusive attack in the Information-Centric IoT context. In this attack, the malicious clients issue Interest packets that can only be satisfied by the malicious content provider, and the malicious provider replies to the clients just before exceeding the Pending Interest Table entry’s expiration time, to occupy the limited resources. In this paper, we expound the model of the consumer-provider collusive attack and analyze the negative effect of the attack. Then we propose a Reputation Value based Early Detection (RVED) mechanism to relieve the impact of the collusive attack. The method aims to adjust the packet dropping rates of different interfaces based on their reputation value, thus to protect the legitimate packets from being dropped as possible. We implement the consumer-provider collusive model and evaluate our defend mechanism in the simulator, and simulation results verify the feasibility and effectiveness against the collusive attack of the RVED mechanism.

Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty

Abstract: В данной статье решается задача построения нейронечеткой модели формирования нечетких правил и их использования для оценки состояния объектов в условиях неопределенности. Традиционные методы математической статистики или имитационного моделирования не позволяют строить адекватные модели объектов в указанных условиях. Поэтому в настоящее время решение многих задач основано на использовании технологий интеллектуального моделирования с применением методов нечеткой логики. Традиционный подход к построению нечетких систем связан с необходимостью привлечения эксперта для формулирования нечетких правил и задания используемых в них функций принадлежности. Для устранения этого недостатка актуальна автоматизация формирования нечетких правил на основе методов и алгоритмов машинного обучения. Одним из подходов к решению данной задачи является построение нечеткой нейронной сети и обучение ее на данных, характеризующих исследуемый объект. Реализация этого подхода потребовала выбора вида нечетких правил с учетом особенностей обрабатываемых данных. Кроме того, потребовалась разработка алгоритма логического вывода на правилах выбранного вида. Этапы алгоритма определяют число слоев в структуре нечеткой нейронной сети и их функциональность. Разработан алгоритм обучения нечеткой нейронной сети. После ее обучения производится формирование системы нечетко-продукционных правил. На базе разработанного математического обеспечения реализован программный комплекс. На его основе проведены исследования по оценке классифицирующей способности формируемых нечетких правил на примере анализа данных из UCI Machine Learning Repository. Результаты исследований показали, что классифицирующая способность сформированных нечетких правил не уступает по точности другим методам классификации. Кроме того, алгоритм логического вывода на нечетких правилах позволяет успешно производить классификацию при отсутствии части исходных данных. С целью апробации произведено формирование нечетких правил для решения задачи по оценке состояния водоводов в нефтяной отрасли. На основе исходных данных по 303 водоводам сформирована база из 342 нечетких правил. Их практическая апробация показала высокую эффективность в решении поставленной задачи.

Short-Term Debt and Bank Risk

Abstract: The extant literature suggests that one of the main causes of the recent financial crisis has been the excessive use of short-term debt by banks [Gorton and Metrick (2012a, b)]. Using a large sample of banks we find that increases in repurchase agreements (repos) was recognized by external capital markets to increase bank risk in the pre-crisis period. In the crisis, we find a negative relationship between repos and risk. We attribute this result to evidence suggesting that “good” banks were able to continue funding their repos, whereas “bad” banks had to significantly decrease their repo funding.

Neuro-Fuzzy Model in Supply Chain Management for Objects State Assessing

Abstract: This article considers the task of objects state assessing in conditions of uncertainty by considering the supply chain strategy. To solve it, the need to use fuzzy-production knowledge bases and fuzzy inference algorithms as part of fuzzy decision support systems is being updated. As a tool for constructing a knowledge base, a neural-fuzzy model is proposed. The proposed type of fuzzy-production rules and the logic inference algorithm on rules for objects state assessing are described. A structure of a fuzzy neural network, consisting of six layers, each of which implements the corresponding stage of the logic inference algorithm, is proposed. As a result of training a fuzzy neural network, a system of fuzzy-production rules is formed, which make up the knowledge base of the decision support system for objects state assessing. On the basis of the proposed neuro-fuzzy model, a software package has been implemented for automating the processes of forming fuzzy-production rules. The main components of the software package are the knowledge base generation module and the fuzzy inference module. As an approbation of the neuro-fuzzy model, the formation of fuzzy rules for assessing the state of water lines at the cluster pumping stations in reservoir pressure maintenance systems has been carried out. The testing results confirmed the high efficiency of the neural-fuzzy model and the possibility of its practical use for the formation of fuzzy-production rules in various subject areas of human activity.