Search results for: fuzzy random variables

Authors: Krishnan Manickavasagam, Manikandan Shanmugam

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Clean and abundant renewable energy sources (RES) such as solar energy is seen as the best solution to replace conventional energy source. Unpredictable power generation is a major issue in the penetration of solar energy, as power generated is governed by the irradiance received. Controlling the power generated from solar PV (SPV) panels to battery and load is a challenging task. In this paper, power flow control from SPV to load and energy storage device (ESD) is controlled by a fuzzy decision support system (FDSS) on the availability of solar irradiation. The results show that FDSS implemented with the energy management system (EMS) is capable of managing power within the area, and if excess power is available, then shared with the neighboring area.

Keywords: renewable energy sources, fuzzy decision support system, solar photovoltaic, energy storage device, energy management system

Procedia PDF Downloads 105

Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

Procedia PDF Downloads 335

Authors: John K. Avor, Choong-Koo Chang

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The transfer of electrical loads at power generation stations from Standby Auxiliary Transformer (SAT) to Unit Auxiliary Transformer (UAT) and vice versa is through a fast bus transfer scheme. Fast bus transfer is a time-critical application where the transfer process depends on various parameters, thus transfer schemes apply advance algorithms to ensure power supply reliability and continuity. In a nuclear power generation station, supply continuity is essential, especially for critical class 1E electrical loads. Bus transfers must, therefore, be executed accurately within 4 to 10 cycles in order to achieve safety system requirements. However, the main problem is that there are instances where transfer schemes scrambled due to inaccurate interpretation of key parameters; and consequently, have failed to transfer several critical loads from UAT to the SAT during main generator trip event. Although several techniques have been adopted to develop robust transfer schemes, a combination of Artificial Neural Network and Fuzzy Systems (Neuro-Fuzzy) has not been extensively used. In this paper, we apply the concept of Neuro-Fuzzy to determine plant operating mode and dynamic prediction of the appropriate bus transfer algorithm to be selected based on the first cycle of voltage information. The performance of Sequential Fast Transfer and Residual Bus Transfer schemes was evaluated through simulation and integration of the Neuro-Fuzzy system. The objective for adopting Neuro-Fuzzy approach in the bus transfer scheme is to utilize the signal validation capabilities of artificial neural network, specifically the back-propagation algorithm which is very accurate in learning completely new systems. This research presents a combined effect of artificial neural network and fuzzy systems to accurately interpret key bus transfer parameters such as magnitude of the residual voltage, decay time, and the associated phase angle of the residual voltage in order to determine the possibility of high speed bus transfer for a particular bus and the corresponding transfer algorithm. This demonstrates potential for general applicability to improve reliability of the auxiliary power distribution system. The performance of the scheme is implemented on APR1400 nuclear power plant auxiliary system.

Keywords: auxiliary power system, bus transfer scheme, fuzzy logic, neural networks, reliability

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Authors: A. Pallavi, N. Vijay Mohan

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A nation can be economically progressive only when the citizens have sufficient capacity to work efficiently to increase the productivity. So, good health must be regarded as a primary need of the community. This helps the growth and development of the body and the mind, which in turn leads to progress and prosperity of the nation. An optimum growth is a necessity for an efficient existence in a biologically adverse and economically competitive world. It is also necessary for the execution of daily routine work. Yoga is a method or a system for the complete development of the personality in a human being. It can be further elaborated as an all-around and complete development of the body, mind, morality, intellect and soul of a being. Sri Aurobindo defines yoga as 'a methodical effort towards self-perfection by the development of the potentialities in the individual.' Aerobic exercise as any activity that uses large muscle groups, can be maintained continuously, and is rhythmic I nature. It is a type of exercise that overloads the heart and lungs and causes them to work harder than at rest. The important idea behind aerobic exercise today, is to get up and get moving. There are more activities that ever to choose from, whether it is a new activity or an old one. Find something you enjoy doing that keeps our heart rate elevated for a continuous time period and get moving to a healthier life. Middle aged selected and served as the subjects for the purpose of this study. The selected subjects were in the age group of 30 to 40 years. By going through the literature and after consulting the experts in yoga and aerobic training, the investigator had chosen the variables which are specifically related to the middle-aged men. The selected physiological variables are pulse rate, diastolic blood pressure, systolic blood pressure; percent body fat and vital capacity. The selected psychological variables are job anxiety, occupational stress. The study was formulated as a random group design consisting of aerobic exercise and yogic exercises groups. The subjects (N=60) were at random divided into three equal groups of twenty middle-aged men each. The groups were assigned the names as follows: 1. Experimental group I- aerobic exercises group, 2. Experimental group II- yogic exercises, 3. Control group. All the groups were subjected to pre-test prior to the experimental treatment. The experimental groups participated in their respective duration of twenty-four weeks, six days in a week throughout the study. The various tests administered were: prior to training (pre-test), after twelfth week (second test) and twenty-fourth weeks (post-test) of the training schedule.

Keywords: pulse rate, diastolic blood pressure, systolic blood pressure; percent body fat and vital capacity, psychological variables, job anxiety, occupational stress, aerobic exercise, yogic exercise

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Authors: Latif Yanar, Muharrem Kaçan

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In this study, an Analytic Hierarchy Process and Analytic Network Process Decision Support System (DSS) model for determination of a navy base place in another country is proposed together with a decision support software (DESTEC 1.0) developed using C Sharp programming language. The proposed software also has the ability of performing the fuzzy models (Fuzzy AHP and Fuzzy ANP) of the proposed DSS to cope with the ambiguous and linguistic nature of the model. The AHP and ANP model, for a decision support for selecting the best place among the alternatives, including the criteria and alternatives, is developed and solved by the experts from Turkish Navy and Turkish academicians related to international relations branches of the universities in Turkey. Also, the questionnaires used for weighting of the criteria and the alternatives are filled by these experts.Some of our alternatives are: economic and political stability of the third country, the effect of another super power in that country, historical relations, security in that country, social facilities in the city in which the base will be built, the transportation security and difficulty from a main city that have an airport to the city will have the base etc. Over 20 criteria like these are determined which are categorized in social, political, economic and military aspects. As a result all the criteria and three alternatives are evaluated by different people who have background and experience to weight the criteria and alternatives as it must be in AHP and ANP evaluation system. The alternatives got their degrees all between 0 – 1 and the total is 1. At the end the DSS advices one of the alternatives as the best one to the decision maker according to the developed model and the evaluations of the experts.

Keywords: analytic hierarchical process, analytic network process, fuzzy logic, naval base place selection, multiple criteria decision making

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Authors: Ikbal Eski, Turan Gürgenç

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After nearly more than a century of research and development, internal combustion engines have become almost perfect. Along with such improvement in internal combustion engines, automotive manufacturers are conducting research on design of alternative fuel vehicles. Nevertheless an ideal interim solution is to increase overall efficiency of internal combustion vehicles. A potential solution to achieve that is using continuously variable transmission system which, despite being an old idea, has recently become a hope for automotive manufacturers. CVT system, by continuously varying speed ratio, raises vehicle efficiency. In this study, fuzzy logic controller is used in speed ratio control of pulley based CVT system.

Keywords: continuously variable transmission system, variator, speed ratio, fuzzy logic

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Andrés C. Cuervo Pinilla, Christian G. Quintero M., Chinthaka Premachandra

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This paper considers people’s driving skills diagnosis under real driving conditions. In that sense, this research presents an approach that uses GPS signals which have a direct correlation with driving maneuvers. Besides, it is presented a novel expert-driving-criteria approximation using fuzzy logic which seeks to analyze GPS signals in order to issue an intelligent driving diagnosis. Based on above, this works presents in the first section the intelligent driving diagnosis system approach in terms of its own characteristics properties, explaining in detail significant considerations about how an expert-driving-criteria approximation must be developed. In the next section, the implementation of our developed system based on the proposed fuzzy logic approach is explained. Here, a proposed set of rules which corresponds to a quantitative abstraction of some traffics laws and driving secure techniques seeking to approach an expert-driving- criteria approximation is presented. Experimental testing has been performed in real driving conditions. The testing results show that the intelligent driving diagnosis system qualifies driver’s performance quantitatively with a high degree of reliability.

Keywords: driver support systems, intelligent transportation systems, fuzzy logic, real time data processing

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Authors: Anuradha Banerjee

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Energy aware operation is one of the visionary goals in the area of robotics because operability of robots is greatly dependent upon their residual energy. Practically, the tasks allocated to robots carry different priority and often an upper limit of time stamp is imposed within which the task needs to be completed. If a robot is unable to complete one particular task given to it the task is reallocated to some other robot. The collection of robots is controlled by a Central Monitoring Unit (CMU). Selection of the new robot is performed by a fuzzy controller called Task Reallocator (TRAC). It accepts the parameters like residual energy of robots, possibility that the task will be successfully completed by the new robot within stipulated time, distance of the new robot (where the task is reallocated) from distance of the old one (where the task was going on) etc. The proposed methodology increases the probability of completing globally assigned tasks and saves huge amount of energy as far as the collection of robots is concerned.

Keywords: energy-efficiency, fuzzy-controller, priority, reallocation, task

Procedia PDF Downloads 319

Authors: Farahnaz Karami

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Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio.

Keywords: mobile ad hoc networks, routing, quality of service, ant colony, fuzzy logic

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Authors: Assad Maalouf, Lunjin Lu, James Lynott

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We design and implement a precise model of string operations using finite state machine transformers and state transformers to approximate the values string variables can take throughout the execution of the program.We use our model to analyze Android program string variables. Our experimental results show that our string analysis is very efficient at detecting the contextual effect of string operations on the string variables. Our model proved to be very useful when it came to verifying statements about the string variables of the program.

Keywords: abstract interpretation, android, static analysis, string analysis

Procedia PDF Downloads 183

Authors: Khalil Aryanfar, Shahrzad Sanjari, Elmira Hafez, Pariya Gholipor

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The aim of this study was to Evaluate and compare the male and female students’ life skills of theoretical, technical-vocational and Job and Knowledge branches of secondary high school period. The research method is descriptive - survey Research population was 5892 students from three high schools in Tehran, sample size was determined 342 patients according to Morgan’s table and by stratified random sampling. The data collection tool was a questionnaire designed by the researchers that the reliability was more than 85/0 respectively. Data was anglicized by Kryskal Wallis and Mann-Whitney U-test. In three branches of theoretical, technical-vocational and Job and Knowledge The variables of academic achievement, the importance of organization, problem solving, seeking knowledge, good habits, mental and physical self-concept, family orientation and future orientation was not significant differences, in the variables of cooperative behavior, and ready for change was but significant differences. Variables such as academic achievement, seek knowledge, good habits, mental and physical, seeking direction to future cooperative behavior between boys and girls with the confidence of at least 95/0 and the variable ready for change among boys and girls by ensuring 0932 / There was an However, the importance of variables, problem solving, self-concept and family orientation was not significantly different.

Keywords: life skills, high school, theoretical, technical-vocational, job and knowledge

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Authors: Hasan Keshavarzian, Tayebeh Nesari

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Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

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Authors: V. S. S. Kumar, B. Vikram, G. C. S. Reddy

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Time and cost are the two critical objectives of construction project management and are not independent but intricately related. Trade-off between project duration and cost are extensively discussed during project scheduling because of practical relevance. Generally when the project duration is compressed, the project calls for an increase in labor and more productive equipments, which increases the cost. Thus, the construction time-cost optimization is defined as a process to identify suitable construction activities for speeding up to attain the best possible savings in both time and cost. As there is hidden tradeoff relationship between project time and cost, it might be difficult to predict whether the total cost would increase or decrease as a result of compressing the schedule. Different combinations of duration and cost for the activities associated with the project determine the best set in the time-cost optimization. Therefore, the contractors need to select the best combination of time and cost to perform each activity, all of which will ultimately determine the project duration and cost. In this paper, the fuzzy set theory is used to model the uncertainties in the project environment for time-cost trade off analysis.

Keywords: fuzzy sets, uncertainty, qualitative factors, decision making

Procedia PDF Downloads 657

Authors: Teimuraz Manjapharashvili, Ketevani Manjaparashvili

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Fuzzy logic is a modeling method for complex or ill-defined systems and is a relatively new mathematical approach. Its basis is to consider overlapping cases of parameter values and define operations to manipulate these cases. Fuzzy logic can successfully create operative automatic management or appropriate advisory systems. Fuzzy logic techniques in various operational control technologies have grown rapidly in the last few years. Fuzzy logic is used in many areas of human technological activity. In recent years, Fuzzy logic has proven its great potential, especially in the automation of industrial process control, where it allows the form of a control design based on the experience of experts and the results of experiments. The engineering of chemical technological processes uses fuzzy logic in optimal management, and it is also used in process control, including the operational control of continuous cycle chemical industrial, technological processes, where special features appear due to the continuous cycle and correct management acquires special importance. This paper discusses how intelligent systems can be developed, in particular, how Fuzzy logic can be used to build knowledge-based expert systems in chemical process engineering. The implemented projects reveal that the use of Fuzzy logic in technological process control has already given us better solutions than standard control techniques. Fuzzy logic makes it possible to develop an advisory system for decision-making based on the historical experience of the managing operator and experienced experts. The present paper deals with operational control and management systems of continuous cycle chemical technological processes, including advisory systems. Because of the continuous cycle, many features are introduced in them compared to the operational control of other chemical technological processes. Among them, there is a greater risk of transitioning to emergency mode; the return from emergency mode to normal mode must be done very quickly due to the impossibility of stopping the technological process due to the release of defective products during this period (i.e., receiving a loss), accordingly, due to the need for high qualification of the operator managing the process, etc. For these reasons, operational control systems of continuous cycle chemical technological processes have been specifically discussed, as they are different systems. Special features of such systems in control and management were brought out, which determine the characteristics of the construction of control and management systems. To verify the findings, the development of an advisory decision-making information system for operational control of a lime kiln using Fuzzy logic, based on the creation of a relevant expert-targeted knowledge base, was discussed. The control system has been implemented in a real lime production plant with a lime burn kiln, which has shown that suitable and intelligent automation improves operational management, reduces the risks of releasing defective products, and, therefore, reduces costs. The special advisory system was successfully used in the said plant both for the improvement of operational management and, if necessary, for the training of new operators due to the lack of an appropriate training institution.

Keywords: chemical process control systems, continuous cycle industrial technological processes, fuzzy logic, lime kiln

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Authors: Nelson Bii, Christopher Ouma, John Odhiambo

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Non-response is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random non-response using auxiliary data. In this study, it is assumed that random non-response occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random non-response. In particular, the auxiliary information is used via an improved Nadaraya-Watson kernel regression technique to compensate for random non-response. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at a 95% coverage rate. The results obtained in this study are useful, for instance, in choosing efficient estimators of the finite population mean in demographic sample surveys.

Keywords: mean squared error, random non-response, two-stage cluster sampling, confidence interval lengths

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Authors: Zahra Abdolkarimi, Naser Zouri

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Nowadays, there is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Additionally, robotics system recommended RoboCup factor as a provider of some standardization and testing method in case of computer discussion widely. The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. In addition, decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidently. Consequences, shows method of our discussion is the best way for Particle Swarm Optimization and Fuzzy system compare to other decision of robotics algorithmic.

Keywords: PSO algorithm, inference fuzzy system, chaos theory, soccer robot league

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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Authors: Bijan Nahavandi, Navid Jafarinejad, Somayeh Mehrafzad

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Strategy maps represent the way of value creation in in each organization. Nowadays, implementation of strategy is the main concern for all organizations. Strategy map establishment is the start-up point of strategy implementation and this shows the critical importance of this concept. After some years past since emergence of strategy map, there are some shortcomings in its methodology that frequently quoted by many of researchers. One of these shortcomings is the shortage of a mechanism for refinement of objectives candidate for entrance to map. Organizations in practice have obsession and avidity to determine more number of objectives in strategy map. This study wants to represent a step by step approach to help obviate this problem using quality function deployment (QFD) as a helpful tool and fuzzy screening method. Finally, represented approach applies in a practical case and conclusions have been explained.

Keywords: balanced scorecard, fuzzy screening, house of strategic objectives (HoSO), quality function deployment, strategy map

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Authors: Shiuh-Jer Huang, Yun-Han Yeh

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A 40 steps manual adjusting shock absorber was refitted with DC motor driving mechanism to construct as a semi-active suspension system for a four-wheel drive electric vehicle. Accelerometer and potentiometer sensors are installed to measure the sprung mass acceleration and suspension system compression or rebound states for control purpose. A fuzzy logic controller was designed to derive appropriate damping target based on vehicle running condition for semi-active suspension system to follow. The damping ratio control of each wheel axis suspension system is executed with a robust fuzzy sliding mode controller (FSMC). Different road surface conditions are chosen to evaluate the control performance of this semi-active suspension system based on wheel axis acceleration signal.

Keywords: semi-active suspension, electric vehicle, fuzzy sliding mode control, accelerometer

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Authors: Min Jin Kwon, Seung Won Kim, Eui Yeon Kim, Haeyoung Lee

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Numerous anonymous chat apps that help people to connect with random strangers have been released in South Korea. However, they become a serious problem for young people since young people often use them for channels of prostitution or sexual violence. Although ISPs in South Korea are responsible for making inappropriate content inaccessible on their networks, they do not block traffic of random chat apps since 1) the use of random chat apps is entirely legal. 2) it is reported that they use HTTP proxy blocking so that non-HTTP traffic cannot be blocked. In this paper, we propose a service model that can block random chat apps at home routers. A service provider manages a blacklist that contains blocked apps’ information. Home routers that subscribe the service filter the traffic of the apps out using deep packet inspection. We have implemented a prototype of the proposed model, including a centralized server providing the blacklist, a Raspberry Pi-based home router that can filter traffic of the apps out, and an Android app used by the router’s administrator to locally customize the blacklist.

Keywords: deep packet inspection, internet filtering, juvenile protection, technical blocking

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Authors: Tanzeel bin Abdul Rauf Patker, Saba Mateen

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This research investigates the impact of Brand Loyalty on the product performance and the factors those are considered more important in brand reputation. Variables selected for this research are Brand quality, Brand Equity, Brand Reputation to explore the impact of these variables on Product performance. For this purpose, primary research has been conducted. The questionnaire survey for this research study was administered among the population mainly at the shopping malls. For this research study, a sample size of 250 respondents has been taken into consideration. Customers from the shopping malls and university students constitute the sample for this research study using random sampling (non-probabilistic) used as a sampling technique for conducting the research survey. According to the results obtained from the collected data, it is interpreted that product performance shares a direct relationship with brand quality, brand quality, and brand reputation. Result also showed that brand quality and brand equity has a significant effect on product performance, whereas brand reputation has an insignificant effect on product performance.

Keywords: product performance, brand quality, brand equity, brand reputation

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Mary Anne Roa

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Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially for today’s very high speed networks. To address this undeniably global issue, the study focuses on the development of a fuzzy-based congestion control model concerned with allocating the resources of a computer network such that the system can operate at an adequate performance level when the demand exceeds or is near the capacity of the resources. Fuzzy logic based models have proven capable of accurately representing a wide variety of processes. The model built is based on bandwidth, the aggregate incoming traffic and the waiting time. The theoretical analysis and simulation results show that the proposed algorithm provides not only good utilization but also low packet loss.

Keywords: congestion control, queue management, computer networks, fuzzy logic

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Authors: Fanous Mohammadi, Majid H. Tangestani, Mohammad H. Tayebi

Abstract:

This research aims to evaluate and compare Geographical Information Systems (GIS)-based fuzzy models for producing orogenic gold prospectivity maps in the Saqqez area, NW of Iran. Gold occurrences are hosted in sericite schist and mafic to felsic meta-volcanic rocks in this area and are associated with hydrothermal alterations that extend over ductile to brittle shear zones. The predictor maps, which represent the Pre-(Source/Trigger/Pathway), syn-(deposition/physical/chemical traps) and post-mineralization (preservation/distribution of indicator minerals) subsystems for gold mineralization, were generated using empirical understandings of the specifications of known orogenic gold deposits and gold mineral systems and were then pre-processed and integrated to produce mineral prospectivity maps. Five fuzzy logic operators, including AND, OR, Fuzzy Algebraic Product (FAP), Fuzzy Algebraic Sum (FAS), and GAMMA, were applied to the predictor maps in order to find the most efficient prediction model. Prediction-Area (P-A) plots and field observations were used to assess and evaluate the accuracy of prediction models. Mineral prospectivity maps generated by AND, OR, FAP, and FAS operators were inaccurate and, therefore, unable to pinpoint the exact location of discovered gold occurrences. The GAMMA operator, on the other hand, produced acceptable results and identified potentially economic target sites. The P-A plot revealed that 68 percent of known orogenic gold deposits are found in high and very high potential regions. The GAMMA operator was shown to be useful in predicting and defining cost-effective target sites for orogenic gold deposits, as well as optimizing mineral deposit exploitation.

Keywords: mineral prospectivity mapping, fuzzy logic, GIS, orogenic gold deposit, Saqqez, Iran

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Authors: Niranjana Devi N.

Abstract:

Test case selection is to select the subset of only the fit test cases and remove the unfit, ambiguous, redundant, unnecessary test cases which in turn improve the quality and reduce the cost of software testing. Test cases optimization is the problem of finding the best subset of test cases from a pool of the test cases to be audited. It will meet all the objectives of testing concurrently. But most of the research have evaluated the fitness of test cases only on single parameter fault detecting capability and optimize the test cases using a single objective. In the proposed approach, nine parameters are considered for test case selection and the best subset of parameters for test case selection is obtained using Interval Type-2 Fuzzy Rough Set. Test case selection is done in two stages. The first stage is the fuzzy entropy-based filtration technique, used for estimating and reducing the ambiguity in test case fitness evaluation and selection. The second stage is the ant colony optimization-based wrapper technique with a forward search strategy, employed to select test cases from the reduced test suite of the first stage. The results are evaluated using the Coverage parameters, Precision, Recall, F-Measure, APSC, APDC, and SSR. The experimental evaluation demonstrates that by this approach considerable computational effort can be avoided.

Keywords: ant colony optimization, fuzzy entropy, interval type-2 fuzzy rough set, test case selection

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Authors: Yog Raj Sood, Rajnish Shrivastava, Anchal Wadhwa

Abstract:

Power transformer is one of the electrical equipment that has a central and critical role in the power system. In order to avoid power transformer failure, information system that provides the transformer condition is needed. This paper presents an information system to know the exact situations prevailing within the transformer by declaring its health index. Health index of a transformer is decided by considering several diagnostic tools. The current work deals with UV-Vis, IFT, FP, BDV and Water Content. UV/VIS results have been pre-accessed using separate FL controller for concluding with the Furan contents. It is broadly accepted that the life of a power transformer is the life of the oil/ paper insulating system. The method relies on the use of furan analysis (insulation paper), and other oil analysis results as a means to declare health index. Fuzzy logic system is used to develop the information system. The testing is done on 5 samples of oil of transformers of rating 132/66 KV to obtain the results and results are analyzed using fuzzy logic model.

Keywords: interfacial tension analyzer (ift), flash point (fp), furfuraldehyde (fal), health index

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Authors: Kevin Worrall, Euan McGookin

Abstract:

The use of optimization algorithms is common across a large number of diverse fields. This work presents the use of a hybrid optimization algorithm applied to a mobile robot tasked with carrying out a search of an unknown environment. The algorithm is then applied to the multiple robots case, which results in a reduction in the time taken to carry out the search. The hybrid algorithm is a Random Search Algorithm fused with a Tabu mechanism. The work shows that the algorithm locates the desired points in a quicker time than a brute force search. The Tabu Random algorithm is shown to work within a simulated environment using a validated mathematical model. The simulation was run using three different environments with varying numbers of targets. As an algorithm, the Tabu Random is small, clear and can be implemented with minimal resources. The power of the algorithm is the speed at which it locates points of interest and the robustness to the number of robots involved. The number of robots can vary with no changes to the algorithm resulting in a flexible algorithm.

Keywords: algorithms, control, multi-agent, search and rescue

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Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

Abstract:

Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

Procedia PDF Downloads 496