scholarly journals PROTECTION OF SCIENTIFIC AND EDUCATIONAL RESOURCES IN INFORMATION AND LIBRARY SYSTEMS

2019 ◽  
Vol 2 (2) ◽  
pp. 121-128
Author(s):  
M. A. Rakhmatullaev ◽  
Sh. B. Normatov
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
Information Technologies ◽  
Expert Knowledge ◽  
Fuzzy Model ◽  
Educational Resources ◽  
Electronic Library ◽  
Unauthorized Access ◽  
Degree Of Protection ◽  
Corporate Network

Introduction. The authors of the article highlight the results of research on the development of methods and tools for ensuring the information security of scientific and educational resources in information library systems and corporate information networks. The analysis of the state of the problem and the existing approaches on this topic are given too.Materials and Methods. The approach justifies the need to protect scientific resources from unauthorized access and the use of fuzzy logic to solve the problem. As a model and solution method, a fuzzy model correspondences is proposed, which makes it possible to comprehensively solve the problem of identifying threats in the event of a situation, as well as provide recommendations on how to eliminate them. For the formation of a knowledge base, experts are involved who determine the functions of belonging to knowlege base “Situation — Threats — Actions to eliminate the threats”.Results. The research results are being implemented as part of the ARMAT++ information library system of the corporate network of electronic libraries of 63 Uzbekistan universities for the protection of scientific and educational information from unauthorized access. Testing of the methods, programs of the subsystem and the expert knowledge base are carried out on the basis of information and resource centers under the project “Virtual electronic library of the Tashkent University of Information Technologies named after Muhammad Al-Khorezmi and its five branches”.Discussion and Conclusions. The use of the apparatus of fuzzy logic for the formation of a knowledge base of the “Situation — Threats — Actions to eliminate the threats” type in information library systems significantly increases the degree of protection of valuable information resources from unauthorized access.

scholarly journals Protection of Scientific and Educational Resources in Information and Library Systems

2019 ◽  
Vol 2 (2) ◽  
pp. 121-128
Author(s):  
Marat Rakhmatullaev ◽  
Sherbek Normatov
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Base ◽  
Information Technologies ◽  
Expert Knowledge ◽  
Fuzzy Model ◽  
Educational Resources ◽  
Electronic Library ◽  
Unauthorized Access ◽  
Degree Of Protection ◽  
Corporate Network

Introduction. The authors of the article highlight the results of research on the development of methods and tools for ensuring the information security of scientific and educational resources in information library systems and corporate information networks. The analysis of the state of the problem and the existing approaches on this topic are given too. Materials and Methods. The approach justifies the need to protect scientific resources from unauthorized access and the use of fuzzy logic to solve the problem. As a model and solution method, a fuzzy model correspondences is proposed, which makes it possible to comprehensively solve the problem of identifying threats in the event of a situation, as well as provide recommendations on how to eliminate them. For the formation of a knowledge base, experts are involved who determine the functions of belonging to knowlege base “Situation — Threats — Actions to eliminate the threats”. Results. The research results are being implemented as part of the ARMAT++ information library system of the corporate network of electronic libraries of 63 Uzbekistan universities for the protection of scientific and educational information from unauthorized access. Testing of the methods, programs of the subsystem and the expert knowledge base are carried out on the basis of information and resource centers under the project “Virtual electronic library of the Tashkent University of Information Technologies named after Muhammad Al-Khorezmi and its five branches”. Discussion and Conclusions. The use of the apparatus of fuzzy logic for the formation of a knowledge base of the “Situation — Threats — Actions to eliminate the threats” type in information library systems significantly increases the degree of protection of valuable information resources from unauthorized access.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

EXPERT MANAGEMENT SYSTEMS OF AGROTECHNOLOGIES IN CLOUD INFORMATION TECHNOLOGY

1970 ◽  
pp. 12-17
Author(s):  
I. М. Mikhaylenko ◽  
V. N. Timoshin
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Information System ◽  
Knowledge Base ◽  
Information Technologies ◽  
Agricultural Sector ◽  
Perennial Grasses ◽  
Management Decision ◽  
Agricultural Technologies ◽  
Automation Systems

The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

Expert Knowledge-Based Traction Control of a Truck Using Fuzzy Logic

1996 ◽  
Vol 29 (1) ◽  
pp. 7867-7872
Author(s):  
Ka C. Cheok ◽  
Kazuyuki Kobayashi ◽  
Francis B. Hoogterp
Keyword(s):  
Fuzzy Logic ◽  
Expert Knowledge ◽  
Traction Control ◽  
Knowledge Based

scholarly journals Fuzzy logic simulation of oat yield after using hydrogel and nitrogen biopolymer management

2020 ◽  
pp. 1319-1327
Author(s):  
Osmar Bruneslau Scremin ◽  
José Antonio Gonzalez da Silva ◽  
Ivan Ricardo Carvalho ◽  
Ângela Teresinha Woschinski De Mamann ◽  
Odenis Alessi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Biomass Yield ◽  
New Technologies ◽  
Block Design ◽  
Fuzzy Model ◽  
Logic Simulation ◽  
Efficient Tool ◽  
Randomized Block Design ◽  
Maximum Air Temperature

The fuzzy logic is an efficient tool for simulation and validation of new technologies in agriculture. The objective of the study is to adapt the fuzzy logic model for simulation of biomass and oat grain yield by nitrogen involving the nonlinearity of the maximum air temperature in the conditions of use of the biopolymer hydrogel, considering high succession systems and low release of residual N. The study was conducted in 2014 and 2015, in a randomized block design with four replicates in a 5 x 5 factorial. Five hydrogel doses (0, 30, 60, 90 and 120 kg ha-1) were added in the groove next to the seed; and 5 doses of N-fertilizer (0, 30, 60, 90 and 120 kg ha-1) applied at the fourth expanded leaf stage, respectively. The cultivar was URS Corona. The pertinence functions and the linguistic values established in the input and output variables to simulate the biomass yield and oat grains in the succession systems are adequate observed productivity. The fuzzy model makes it possible to estimate the biomass and oat grains productivity efficiently under the conditions of use of the hydrogel as a function of the nitrogen doses and maximum air temperature, adding to the existing models of simulation.

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals ANALYSIS OF THE EFFECTIVENESS OF TECHNOLOGICAL INNOVATIONS BY FUZZY MODELING METHODS: THE CONTENT OF THE MODEL AND THE CONDITIONS FOR SOFTWARE IMPLEMENTATION

2019 ◽  
Vol 29 (5) ◽  
pp. 609-620
Author(s):  
O.M. Shatalova
Keyword(s):  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Technological Development ◽  
Innovation Management ◽  
Fuzzy Model ◽  
Software Implementation ◽  
Technological Innovations ◽  
Fuzzy Evaluation ◽  
Innovative Strategy ◽  
Intelligent Methods

The article is devoted to the development of methods and instruments for solving the problem of non-stochastic uncertainty in the management of technological innovations. In this regard, the methodology of fuzzy-multiple modeling of systems is considered. It allows you to take into account both deterministic and stochastic data, as well as mental knowledge of the system on the part of decision makers, presented in the lexical description and based on fuzzy evaluation mechanism. The construction of fuzzy-multiple models is aimed at reproducing the logic of decision making and is based on the use of intelligent methods of information processing, including those presented in fuzzy and verbal characteristics, by mathematical language means, which can be transferred to machine processing. The basis of the study is the provision on the vector form of the efficiency indicator and the implementation of the correspondence function between the basic parameters of efficiency through fuzzy inference. The article describes the developed basic conditions for simulation of fuzzy-multiple modeling in assessing the effectiveness of technological innovation management systems - the structure of the model and methods for its construction; presents the means of software implementation of a simulation fuzzy-plural model developed in accordance with these conditions and the results of its practical testing. The developed conditions of fuzzy-multiple modeling in assessing the effectiveness of technological innovations form the basis of a comprehensive analysis of the conditions of technological development of the enterprise, allow to identify significant management factors and form the content of an effective innovative strategy for the technological development of the enterprise; the fuzzy model itself can be considered as a platform for the integration of deterministic, stochastic, expert knowledge of the system.

scholarly journals Gaudii: An Automated Graphic Design Expert System

2021 ◽  
Vol 24 (2) ◽  
pp. 1775-1780
Author(s):  
Carlos Glez-Morcillo ◽  
Victor Martin ◽  
David Vallejo Fernandez ◽  
Jose Castro-Schez ◽  
Javier Albusac
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Evolutionary Computation ◽  
Graphic Design ◽  
Expert Knowledge ◽  
Trial And Error ◽  
Design Elements ◽  
Design Expert ◽  
Novel Approach ◽  
Design Activities

Graphic design is the process of creating graphics to meet specific commercial needs based on knowledge of layout principles and esthetic concepts. This is usually an iterative trial and error process which requires a lot of time even for expert designers. This expert knowledge can be modelled, represented and used by a computer to perform design activities. This paper describes a novel approach named Gaudii (standing for "Intelligent Automated Graphic Design Generator") which utilizes principles and techniques known from the fields of Evolutionary Computation and Fuzzy Logic to automatically obtain design elements. Experimental results that demonstrate the potential of the proposed approach are presented in the area of poster design.

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