scholarly journals Application of Fuzzy Logic for Problems of Evaluating States of a Computing System

2019 ◽  
Vol 9 (15) ◽  
pp. 3021 ◽  
Author(s):  
Buriboev ◽  
Kang ◽  
Ko ◽  
Oh ◽  
Abduvaitov ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Computer Systems ◽  
Computing System ◽  
The State ◽  
Computer Hardware ◽  
Computing Systems ◽  
Inference System ◽  
Determine System ◽  
The Impact ◽  
And Storage

The monitoring utilization and workloads of computer hardware components, such as CPU, RAM, bus, and storage, are an ideal way to evaluate the effectiveness of these components. In this paper, we surveyed the basic concepts, characteristics, and parameters of computer systems that determine system performance, and the types of models that provide adequate modeling of these systems. We investigated and developed the applied aspects of the theory of fuzzy sets’ principles and the Matlab environment tools for monitoring and evaluating the state of computing systems. The idea of the paper is to identify the state of the computer infrastructure by using the models of Mamdani and Sugeno FIS (fuzzy inference system) to evaluate the impact of RAM and storage on CPU performance. With this approach, we observed the behavior of computer infrastructure. The results are useful for understanding performance issues with regard to specific bottlenecks and determining the correlation of performance counters. Moreover, the model presents linguistic results. Hereafter, performance counter correlations will support the development of algorithms that can detect whether the performance of a given computer will be affected by a reasonable priority. The performance assertions derived from these approaches allow resource management policies to prevent performance degradation, and as a result, the infrastructure will be able to serve safely as expected. These methods can be applied across the entire spectrum of computer systems, from personal computers to large mainframes and supercomputers, including both centralized and distributed systems. We look forward to their continued use, as well as their improvement when it is necessary to evaluate future systems.

scholarly journals Simulation of Pan-Evaporation Using Penman and Hamon Equations and Artificial Intelligence Techniques

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 793
Author(s):  
Abdul Razzaq Ghumman ◽  
Mohammed Jamaan ◽  
Afaq Ahmad ◽  
Md. Shafiquzzaman ◽  
Husnain Haider ◽  
...  
Keyword(s):  
Wind Speed ◽  
Water Resources ◽  
Arid Regions ◽  
Fuzzy Inference ◽  
Sunshine Duration ◽  
Pan Evaporation ◽  
Estimation Of Parameters ◽  
Inference System ◽  
Daunting Task ◽  
The Impact

The evaporation losses are very high in warm-arid regions and their accurate evaluation is vital for the sustainable management of water resources. The assessment of such losses involves extremely difficult and original tasks because of the scarcity of data in countries with an arid climate. The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). The results from five types of ANN models with different training functions were compared to find the best possible training function. The impact of using various input variables was investigated as an original contribution of this research. The average temperature and mean wind speed were found to be the most influential parameters. The estimation of parameters for Penman and Hamon’s equations was quite a daunting task. These parameters were estimated using a state of the art optimization algorithm, namely General Reduced Gradient Technique. The results of the Penman and Hamon’s equations, ANN, and ANFIS were compared. Thirty-eight years (from 1980 to 2018) of manually recorded pan-evaporation data regarding mean daily values of a month, including the relative humidity, wind speed, sunshine duration, and temperature, were collected from three gauging stations situated in Al Qassim, Saudi Arabia. The Nash and Sutcliffe Efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of pan-evaporation modeling techniques. The study shows that the ANFIS simulation results were better than those of ANN and Penman and Hamon’s equations. The findings of the present research will help managers, engineers, and decision makers to sustainability manage natural water resources in warm-arid regions.

scholarly journals Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1548
Author(s):  
Marjana Čubranić-Dobrodolac ◽  
Libor Švadlenka ◽  
Svetlana Čičević ◽  
Aleksandar Trifunović ◽  
Momčilo Dobrodolac
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Fuzzy Inference ◽  
Road Traffic ◽  
Space Perception ◽  
Road Traffic Accidents ◽  
Inference System ◽  
Interval Type ◽  
The Impact

A constantly increasing number of deaths on roads forces analysts to search for models that predict the driver’s propensity for road traffic accidents (RTAs). This paper aims to examine a relationship between the speed and space assessment capabilities of drivers in terms of their association with the occurrence of RTAs. The method used for this purpose is based on the implementation of the interval Type-2 Fuzzy Inference System (T2FIS). The inputs to the first T2FIS relate to the speed assessment capabilities of drivers. These capabilities were measured in the experiment with 178 young drivers, with test speeds of 30, 50, and 70 km/h. The participants assessed the aforementioned speed values from four different observation positions in the driving simulator. On the other hand, the inputs of the second T2FIS are space assessment capabilities. The same group of drivers took two types of space assessment tests—2D and 3D. The third considered T2FIS sublimates of all previously mentioned inputs in one model. The output in all three T2FIS structures is the number of RTAs experienced by a driver. By testing three proposed T2FISs on the empirical data, the result of the research indicates that the space assessment characteristics better explain participation in RTAs compared to the speed assessment capabilities. The results obtained are further confirmed by implementing a multiple regression analysis.

scholarly journals Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu ◽  
H. Michael Zhang
Keyword(s):  
Traffic Safety ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Traffic Volume ◽  
Learning Ability ◽  
Adaptive Network ◽  
Freeway Traffic ◽  
Inference System ◽  
Traffic Conflicts ◽  
The Impact

The truck operation of freeway has an impact on traffic safety. In particular, the gradually increasing in truck proportion will inevitably affect the freeway traffic operation of different traffic volume. In this paper, VISSIM simulation is used to supply the field data and orthogonal experimental is designed for calibrate the simulation data. Then, SSAM modeling is combined to analyze the impact of truck proportion on traffic flow parameters and traffic conflicts. The serious and general conflict prediction model based on the Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to determine the impact of the truck proportion on freeway traffic safety. The results show that when the truck proportion is around 0.4 under 3200 veh/h and 0.6 under 2600 veh/h, there are more traffic conflicts and the number of serious conflicts is more than the number of general conflicts, which also reflect the relationship between truck proportion and traffic safety. Under 3000 veh/h, travel time and average delay increasing while mean speed and mean speed of small car decreases with truck proportion increases. The mean time headway rises largely with the truck proportion increasing above 3000 veh/h. The speed standard deviation increases initially and then fall with truck proportion increasing. The lane-changing decreases while truck proportion increasing. In addition, ANFIS can accurately determine the impact of truck proportion on traffic conflicts under different traffic volume, and also validate the learning ability of ANFIS.

scholarly journals Measuring Software Quality Product Based on Fuzzy Inference System Techniques in ISO Standard

2021 ◽  
Author(s):  
Atrin Barzegar
Keyword(s):  
Software Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Iso Standard ◽  
Software Products ◽  
Software Product ◽  
The Impact

The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable software according to the goals and needs of the organization makes measuring the quality of software products an important issue for most organizations and institutions. To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

scholarly journals Modeling and analysis: power injection model approach for high performance of electrical distribution networks

2021 ◽  
Vol 10 (6) ◽  
pp. 2943-2952
Author(s):  
Baraa Jalil Abdulelah ◽  
Yousif Ismail Mohammed Al-Mashhadany ◽  
Sameer Algburi ◽  
Gozde Ulutagay
Keyword(s):  
High Performance ◽  
Fuzzy Inference ◽  
Block Diagram ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Effect Of Temperature ◽  
Inference System ◽  
Modeling And Analysis ◽  
Point Tracking ◽  
The Impact

The generation of electrical energy varies depending on the needs of the user, initial requirements, capacity, intended use, waste generation, and economic efficiency. In order to meet the challenges of the proposed overvoltage of the presented system, it is possible to use the solar collectors and profit from them economically through smart grid smart control systems. The mathematical model with four main parts was created: simulation, correlation, and evaluation according to the solar program set of photovoltaic solar modules, maximum power point tracking (MPPT), an adaptive neuro-fuzzy inference system (ANFIS) controller, and 600-volt electric network. Then in this phase, the investigation of the effects on the network on the basis of the output power with the coincidence of radiation and the effect of temperature in the network is carried out. An analysis was carried out to evaluate the impact of these fundamental limitations in practical application. In this section, the simulation of the proposed system is discussed. The block diagram of the developed system is presented in the last part. The proposed system was assessed from the Matlab simulation tapes and graphs for each part of the system, and the results of the overall system simulation were taken into account.

scholarly journals Conceptualization, Definition and Assessment of Internal Logistics through Different Approaches Using Artificial Intelligence

2020 ◽  
Author(s):  
Orlem Pinheiro de Lima ◽  
Sandro Breval Santiago ◽  
Carlos Manuel Rodríguez Taboada ◽  
Jorge Laureano Moya Rodríguez ◽  
Maida Bárbara Reyes Rodríguez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Mathematical Tool ◽  
Computing Systems ◽  
Inference System ◽  
Processing Information ◽  
Definition Of ◽  
Human Brains ◽  
A Company

The aim of this chapter is to develop a new concept of internal logistics, its components parts and how to evaluate it. To quantify the level of performance of the internal logistics of a company is an important issue to gain competitiveness. There are few papers now at days that analyze how to quantify this issue. In recent years, it has been developed numerous applications of Fuzzy logic and Neural Networks to solve diverse problems of Engineering. Fuzzy logic is a mathematical tool that emulates the method used for humans for managing and processing information and Neural Networks are computing systems inspired by the biological neural networks that constitute human brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules. This chapter offers a new definition of internal logistics and shows the procedure to evaluate its level in a company. This procedure for assessing the internal logistics was developed through an Excel tab, a fuzzy inference system and a neural network. To validate this procedure, it was applied to 93 companies in the Industrial Pole of Manaus. Results obtained by different approaches are very similar, demonstrating the validity of the procedure developed.

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