Search results for: fuzzy system

Authors: M. Bassoui, M. Ferfra, M. Chrayagne

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This paper presents a qualitative modeling for the nonlinear B-H curve of the saturable magnetic materials for a transformer with shunts used in the power supply for the magnetron. This power supply is composed of a single phase leakage flux transformer supplying a cell composed of a capacitor and a diode, which double the voltage and stabilize the current, and a single magnetron at the output of the cell. A procedure consisting of a fuzzy clustering method and a rule processing algorithm is then employed for processing the constructed fuzzy modeling rules to extract the qualitative properties of the curve.

Keywords: B(H) curve, fuzzy clustering, magnetron, power supply

Procedia PDF Downloads 206

Authors: Lotfi Farah, Nadir Farah, Zaiem Kamar

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This paper achieves a maximum power point tracking (MPPT) controller using a high-efficiency reduced rules neuro-fuzzy inference system (HE2RNF) for a 100 kW stand-alone photovoltaic (PV) system connected to the grid. The suggested HE2RNF based MPPT seeks the optimal duty cycle for the boost DC-DC converter, making the designed PV system working at the maximum power point (MPP), then transferring this power to the grid via a three levels voltage source converter (VSC). PV current variation and voltage variation are chosen as HE2RNF-based MPPT controller inputs. By using these inputs with the duty cycle as the only single output, a six rules ANFIS is generated. The high performance of the proposed HE2RNF numerically in the MATLAB/Simulink environment is shown. The 0.006% steady-state error, 0.006s of tracking time, and 0.088s of starting time prove the robustness of this six reduced rules against the widely used twenty-five ones.

Keywords: PV, MPPT, ANFIS, HE2RNF-based MPPT controller, VSC, grid connection

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Authors: Abbas Al-Refaie

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This paper aims at improving the performance of the tableting process using statistical quality control and fuzzy goal programming. The tableting process was studied. Statistical control tools were used to characterize the existing process for three critical responses including the averages of a tablet’s weight, hardness, and thickness. At initial process factor settings, the estimated process capability index values for the tablet’s averages of weight, hardness, and thickness were 0.58, 3.36, and 0.88, respectively. The L₉ array was utilized to provide experimentation design. Fuzzy goal programming was then employed to find the combination of optimal factor settings. Optimization results showed that the process capability index values for a tablet’s averages of weight, hardness, and thickness were improved to 1.03, 4.42, and 1.42, respectively. Such improvements resulted in significant savings in quality and production costs.

Keywords: fuzzy goal programming, control charts, process capability, tablet optimization

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Authors: Yanhua Ma, Lu Zhai, Xinchen Wang, Hongyu Liang

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In this paper, by understanding the development status of the human-machine interface in today's automobile cab, a subjective and objective evaluation system for evaluating the optimization of human-machine interface matching in automobile cab was established. The man-machine interface of the car cab was divided into a software interface and a hard interface. Objective evaluation method of software human factor analysis is used to evaluate the hard interface matching; The analytic hierarchy process is used to establish the evaluation index system for the software interface matching optimization, and the multi-level fuzzy comprehensive evaluation method is used to evaluate hard interface machine. This article takes Dongfeng Sokon (DFSK) C37 model automobile as an example. The evaluation method given in the paper is used to carry out relevant analysis and evaluation, and corresponding optimization suggestions are given, which have certain reference value for designers.

Keywords: analytic hierarchy process, fuzzy comprehension evaluation method, human-machine interface, matching optimization, software human factor analysis

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Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

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Authors: N. Drir, L. Barazane, M. Loudini

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It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.

Keywords: maximum power point tracking, neural networks, photovoltaic, P&O

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Authors: Hirofumi Miyajima, Kazuya Kishida, Noritaka Shigei, Hiromi Miyajima

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Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed.

Keywords: Box-Jenkins's problem, double-input rule module, fuzzy inference model, obstacle avoidance, single-input rule module

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Authors: N. Borjalilu, P. Rabiei, A. Enjoo

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Flight Data Monitoring (FDM) program assists an operator in aviation industries to identify, quantify, assess and address operational safety risks, in order to improve safety of flight operations. FDM is a powerful tool for an aircraft operator integrated into the operator’s Safety Management System (SMS), allowing to detect, confirm, and assess safety issues and to check the effectiveness of corrective actions, associated with human errors. This article proposes a model for safety risk assessment level of flight data in a different aspect of event focus based on fuzzy set values. It permits to evaluate the operational safety level from the point of view of flight activities. The main advantages of this method are proposed qualitative safety analysis of flight data. This research applies the opinions of the aviation experts through a number of questionnaires Related to flight data in four categories of occurrence that can take place during an accident or an incident such as: Runway Excursions (RE), Controlled Flight Into Terrain (CFIT), Mid-Air Collision (MAC), Loss of Control in Flight (LOC-I). By weighting each one (by F-TOPSIS) and applying it to the number of risks of the event, the safety risk of each related events can be obtained.

Keywords: F-topsis, fuzzy set, flight data monitoring (FDM), flight safety

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Alireza Gilani

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In this paper, we consider the fuzzification of implicative pseudo-ideal in a pseudo-BCK algebra, and then we investigate some of their properties. We prove that the family of fuzzy implicative pseudo-ideal is completely distributive lattice.

Keywords: BCK-algebra, pseudo-BCK algebra, pseudo-ideal, implicative pseudo-ideal

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Authors: Azzedine Hamza, Chouaib Chakour, Messaoud Ramdani

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Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling.

Keywords: wavelet transform, ANFIS, energy consumption prediction, greenhouse

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Authors: Katarina Rogulj, Ivana Racetin, Jelena Kilic

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In this study, the fuzzy logic approach (FLA) was developed for construction project management (CPM) under uncertainty and duality. The focus was on decision making in selecting the type of the glass facade for a residential-commercial building in the main design. The adoption of fuzzy sets was capable of reflecting construction managers’ reliability level over subjective judgments, and thus the robustness of the system can be achieved. An α-cuts method was utilized for discretizing the fuzzy sets in FLA. This method can communicate all uncertain information in the optimization process, taking into account the values of this information. Furthermore, FLA provides in-depth analyses of diverse policy scenarios that are related to various levels of economic aspects when it comes to the construction projects' valid decision making. The developed approach is applied to CPM to demonstrate its applicability. Analyzing the materials of glass facades, variants were defined. The development of the FLA for the CPM included relevant construction projec'ts stakeholders that were involved in the criteria definition to evaluate each variant. Using fuzzy Decision-Making Trial and Evaluation Laboratory Method (DEMATEL) comparison of the glass facade was conducted. This way, a rank, according to the priorities for inclusion into the main design, of variants is obtained. The concept was tested on a residential-commercial building in the city of Rijeka, Croatia. The newly developed methodology was then compared with the existing one. The aim of the research was to define an approach that will improve current judgments and decisions when it comes to the material selection of buildings facade as one of the most important architectural and engineering tasks in the main design. The advantage of the new methodology compared to the old one is that it includes the subjective side of the managers’ decisions, as an inevitable factor in each decision making. The proposed approach can help construction projects managers to identify the desired type of glass facade according to their preference and practical conditions, as well as facilitate in-depth analyses of tradeoffs between economic efficiency and architectural design.

Keywords: construction projects management, DEMATEL, fuzzy logic approach, glass façade selection

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Authors: Xinyi Le

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In this paper, 3D printing orientations were determined through our perceptual model. Some FDM (Fused Deposition Modeling) 3D printers, which are widely used in universities and industries, often require support structures during the additive manufacturing. After removing the residual material, some surface artifacts remain at the contact points. These artifacts will damage the function and visual effect of the model. To prevent the impact of these artifacts, we present a fuzzy extreme learning machine approach to find printing directions that avoid placing supports in perceptually significant regions. The proposed approach is able to solve the evaluation problem by combing both the subjective knowledge and objective information. Our method combines the advantages of fuzzy theory, auto-encoders, and extreme learning machine. Fuzzy set theory is applied for dealing with subjective preference information, and auto-encoder step is used to extract good features without supervised labels before extreme learning machine. An extreme learning machine method is then developed successfully for training and learning perceptual models. The performance of this perceptual model will be demonstrated on both natural and man-made objects. It is a good human-computer interaction practice which draws from supporting knowledge on both the machine side and the human side.

Keywords: 3d printing, perceptual model, fuzzy evaluation, data-driven approach

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Authors: Abdullah M. Almeshal, Mohammad R. Alenezi, Talal H. Alzanki

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This paper presents a hybrid fuzzy logic control strategy for a unicycle trajectory following robot on irregular terrains. In literature, researchers have presented the design of path tracking controllers of mobile robots on non-frictional surface. In this work, the robot is simulated to drive on irregular terrains with contrasting frictional profiles of peat and rough gravel. A hybrid fuzzy logic controller is utilised to stabilise and drive the robot precisely with the predefined trajectory and overcome the frictional impact. The controller gains and scaling factors were optimised using spiral dynamics optimisation algorithm to minimise the mean square error of the linear and angular velocities of the unicycle robot. The robot was simulated on various frictional surfaces and terrains and the controller was able to stabilise the robot with a superior performance that is shown via simulation results.

Keywords: fuzzy logic control, mobile robot, trajectory tracking, spiral dynamic algorithm

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Authors: Kharisma Utomo Mulyodinoto, Suwarno, A. Abu-Siada

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Dissolved Gas Analysis is one of impressive technique to detect and predict internal fault of transformers by using gas generated by transformer oil sample. A number of methods are used to interpret the dissolved gas from transformer oil sample: Doernenberg Ratio Method, IEC (International Electrotechnical Commission) Ratio Method, and Duval Triangle Method. While the assessment of dissolved gas within transformer oil samples has been standardized over the past two decades, analysis of the results is not always straight forward as it depends on personnel expertise more than mathematical formulas. To get over this limitation, this paper is aimed at improving the interpretation of Doernenberg Ratio Method, IEC Ratio Method, and Duval Triangle Method using Two Stages Adaptive Neuro-Fuzzy Inference System (ANFIS). Dissolved gas analysis data from 520 faulty transformers was analyzed to establish the proposed ANFIS model. Results show that the developed ANFIS model is accurate and can standardize the dissolved gas interpretation process with accuracy higher than 90%.

Keywords: ANFIS, dissolved gas analysis, Doernenberg ratio method, Duval triangular method, IEC ratio method, transformer

Procedia PDF Downloads 119

Authors: Z. B. Ibrahim, N. Ismail, K. I. Othman

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Fuzzy Differential Equations (FDEs) play an important role in modelling many real life phenomena. The FDEs are used to model the behaviour of the problems that are subjected to uncertainty, vague or imprecise information that constantly arise in mathematical models in various branches of science and engineering. These uncertainties have to be taken into account in order to obtain a more realistic model and many of these models are often difficult and sometimes impossible to obtain the analytic solutions. Thus, many authors have attempted to extend or modified the existing numerical methods developed for solving Ordinary Differential Equations (ODEs) into fuzzy version in order to suit for solving the FDEs. Therefore, in this paper, we proposed the development of a fuzzy version of three-point block method based on Block Backward Differentiation Formulas (FBBDF) for the numerical solution of first order FDEs. The three-point block FBBDF method are implemented in uniform step size produces three new approximations simultaneously at each integration step using the same back values. Newton iteration of the FBBDF is formulated and the implementation is based on the predictor and corrector formulas in the PECE mode. For greater efficiency of the block method, the coefficients of the FBBDF are stored at the start of the program. The proposed FBBDF is validated through numerical results on some standard problems found in the literature and comparisons are made with the existing fuzzy version of the Modified Simpson and Euler methods in terms of the accuracy of the approximated solutions. The numerical results show that the FBBDF method performs better in terms of accuracy when compared to the Euler method when solving the FDEs.

Keywords: block, backward differentiation formulas, first order, fuzzy differential equations

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Authors: Vasileiou Apostolos, Theodosiou Chrysa, Tsitroulis Ioannis, Maris Fotios

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The severity of extreme phenomena is due to their ability to cause severe damage in a small amount of time. It has been observed that floods affect the greatest number of people and induce the biggest damage when compared to the total of annual natural disasters. The detection of potential flood-prone areas constitutes one of the fundamental components of the European Natural Disaster Management Policy, directly connected to the European Directive 2007/60. The aim of the present paper is to develop a new methodology that combines geographical information, fuzzy logic and multi-criteria evaluation methods so that the most vulnerable areas are defined. Therefore, ten factors related to geophysical, morphological, climatological/meteorological and hydrological characteristics of the basin were selected. Afterwards, two models were created to detect the areas pronest to flooding. The first model defined the gravitas of each factor using Analytical Hierarchy Process (AHP) and the final map of possible flood spots were created using GIS and Boolean Algebra. The second model made use of the fuzzy logic and GIS combination and a respective map was created. The application area of the aforementioned methodologies was in Ardas basin due to the frequent and important floods that have taken place these last years. Then, the results were compared to the already observed floods. The result analysis shows that both models can detect with great precision possible flood spots. As the fuzzy logic model is less time-consuming, it is considered the ideal model to apply to other areas. The said results are capable of contributing to the delineation of high risk areas and to the creation of successful management plans dealing with floods.

Keywords: analytical hierarchy process, flood prone areas, fuzzy logic, geographic information system

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Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis

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Authors: Manish Pandey, Gurinderjit Kaur, Meenu Talwar, Sachin Chauhan, Jagbir Gill

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Health-care management systems are a unit of nice connection as a result of the supply a straightforward and fast management of all aspects relating to a patient, not essentially medical. What is more, there are unit additional and additional cases of pathologies during which diagnosing and treatment may be solely allotted by victimization medical imaging techniques. With associate ever-increasing prevalence, medical pictures area unit directly acquired in or regenerate into digital type, for his or her storage additionally as sequent retrieval and process. Data Mining is the process of extracting information from large data sets through using algorithms and Techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Forecasting may be a prediction of what's going to occur within the future, associated it's an unsure method. Owing to the uncertainty, the accuracy of a forecast is as vital because the outcome foretold by foretelling the freelance variables. A forecast management should be wont to establish if the accuracy of the forecast is within satisfactory limits. Fuzzy regression strategies have normally been wont to develop shopper preferences models that correlate the engineering characteristics with shopper preferences relating to a replacement product; the patron preference models offer a platform, wherever by product developers will decide the engineering characteristics so as to satisfy shopper preferences before developing the merchandise. Recent analysis shows that these fuzzy regression strategies area units normally will not to model client preferences. We tend to propose a Testing the strength of Exponential Regression Model over regression toward the mean Model.

Keywords: health-care management systems, fuzzy regression, data mining, forecasting, fuzzy membership function

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Authors: Salisu Umar

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Fruits are a seasonal crop and get spoiled quickly. They are dried to be preserved for a long period. The natural drying process requires more time. The investment on space requirement and infrastructure is large, and cannot be afforded by a middle class farmer. Therefore there is a need for a comparatively small unit with reduced drying times, which can be afforded by a middle class farmer. A controlled environment suitable for fruit drying is developed within a closed chamber and is a three step process. Firstly, the infrared light is used internally to preheated the fruit to speedily remove the water content inside the fruit for fast drying. Secondly, hot air of a specified temperature is blown inside the chamber to maintain the humidity below a specified level and exhaust the humid air of the chamber. Thirdly the microcontroller idles disconnecting the power to the chamber after the weight of the fruits is reduced to a known value of its original weight. This activates a buzzer for duration of ten seconds to indicate the end of the drying process. The results obtained indicate that the system is significantly reducing the drying time without affecting the quality of the fruits compared with the existing dryers.

Keywords: fruit, fuzzy controller, microcontroller, temperature, weight and humidity

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

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Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

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Authors: Wael Ata

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Tracked vehicles are exposed to severe operating conditions during their battlefield. The suspension system of such vehicles plays a crucial role in the mitigation of vibration transmitted from unevenness to vehicle hull and consequently to the crew. When the vehicles are crossing the road with high speeds, the driver is subjected to a high magnitude of vibration dose. This is because of the passive suspension system of the tracked vehicle lack the effectiveness to withstand induced vibration from irregular terrains. This paper presents vibration control of a semi-active seat suspension incorporating Magnetorheological (MR) damper fitted to a driver seat of an amphibious tracked vehicle (BMP-1). A half vehicle model featuring the proposed semi-active seat suspension is developed and its governing equations are derived. Two controllers namely; skyhook and fuzzy logic skyhook based to suppress the vibration dose at driver’s seat are formulated. The results show that the controlled MR suspension seat along with the vehicle model has substantially suppressed vibration levels at the driver’s seat under bump and sinusoidal excitations

Keywords: Tracked Vehicles, MR dampers, Skyhook controller, fuzzy logic controller

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Authors: Muhammad Shafiq

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In accelerated life testing approaches life time data is obtained under various conditions which are considered more severe than usual condition. Classical techniques are based on obtained precise measurements, and used to model variation among the observations. In fact, there are two types of uncertainty in data: variation among the observations and the fuzziness. Analysis techniques, which do not consider fuzziness and are only based on precise life time observations, lead to pseudo results. This study was aimed to examine the behavior of empirical acceleration functions using fuzzy lifetimes data. The results showed an increased fuzziness in the transformed life times as compare to the input data.

Keywords: acceleration function, accelerated life testing, fuzzy number, non-precise data

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Authors: Santhosh Kumat T., Priya E.

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The control of a doubly fed induction generator by fuzzy is described. The active and reactive power can be controlled by rotor and grid side converters with fuzzy controller. The main objective is to maintain constant voltage and frequency at the output of the generator. However the Line Side Converter (LSC) can be controlled to supply up to 50% of the required reactive current. When the crowbar is not activated the DFIG can supply reactive power from the rotor side through the machine as well as through the LSC.

Keywords: Doubly Fed Induction Generator (DFIG), Rotor Side Converter (RSC), Grid Side Converter (GSC), Wind Energy Conversion Systems (WECS)

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Authors: Anna Lower, Michal Lower, Robert Masztalski, Agnieszka Szumilas

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Contemporary cities are facing serious congestion and parking problems. In urban transport policy the introduction of the park and ride system (P&R) is an increasingly popular way of limiting vehicular traffic. The determining of P&R facilities location is a key aspect of the system. Criteria for assessing the quality of the selected location are formulated generally and descriptively. The research outsourced to specialists are expensive and time consuming. The most focus is on the examination of a few selected places. The practice has shown that the choice of the location of these sites in a intuitive way without a detailed analysis of all the circumstances, often gives negative results. Then the existing facilities are not used as expected. Methods of location as a research topic are also widely taken in the scientific literature. Built mathematical models often do not bring the problem comprehensively, e.g. assuming that the city is linear, developed along one important communications corridor. The paper presents a new method where the expert knowledge is applied to fuzzy inference model. With such a built system even a less experienced person could benefit from it, e.g. urban planners, officials. The analysis result is obtained in a very short time, so a large number of the proposed location can also be verified in a short time. The proposed method is intended for testing of car parks location in a city. The paper will show selected examples of locations of the P&R facilities in cities planning to introduce the P&R. The analysis of existing objects will also be shown in the paper and they will be confronted with the opinions of the system users, with particular emphasis on unpopular locations. The research are executed using the fuzzy inference model which was built and described in more detail in the earlier paper of the authors. The results of analyzes are compared to documents of P&R facilities location outsourced by the city and opinions of existing facilities users expressed on social networking sites. The research of existing facilities were conducted by means of the fuzzy model. The results are consistent with actual users feedback. The proposed method proves to be good, but does not require the involvement of a large experts team and large financial contributions for complicated research. The method also provides an opportunity to show the alternative location of P&R facilities. The performed studies show that the method has been confirmed. The method can be applied in urban planning of the P&R facilities location in relation to the accompanying functions. Although the results of the method are approximate, they are not worse than results of analysis of employed experts. The advantage of this method is ease of use, which simplifies the professional expert analysis. The ability of analyzing a large number of alternative locations gives a broader view on the problem. It is valuable that the arduous analysis of the team of people can be replaced by the model's calculation. According to the authors, the proposed method is also suitable for implementation on a GIS platform.

Keywords: fuzzy logic inference, park and ride system, P&R facilities, P&R location

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Authors: Kemal Polat

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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: Mohammad Reza Zamanian

Abstract:

The construction industry has always been recognized as one of the most dangerous available industries, and the statistics of accidents and injuries resulting from it say that the safety category needs more attention and the arrival of up-to-date technologies in this field. Building information modeling (BIM) is one of the relatively new and applicable technologies in Iran, that the necessity of using it is increasingly evident. The main purposes of this research are to evaluate the feasibility of using this technology in the safety sector of construction projects and to evaluate the effectiveness and operationality of its various applications in this sector. These applications were collected and categorized after reviewing past studies and researches then a questionnaire based on Delphi method criteria was presented to 30 experts who were thoroughly familiar with modeling software and safety guidelines. After receiving and exporting the answers to SPSS software, the validity and reliability of the questionnaire were assessed to evaluate the measuring tools. Fuzzy logic is a good way to analyze data because of its flexibility in dealing with ambiguity and uncertainty issues, and the implementation of the Delphi method in the fuzzy environment overcomes the uncertainties in decision making. Therefore, this method was used for data analysis, and the results indicate the usefulness and effectiveness of BIM in projects and improvement of safety status at different stages of construction. Finally, the applications and the sections discussed were ranked in order of priority for efficiency and effectiveness. Safety planning is considered as the most influential part of the safety of BIM among the four sectors discussed, and planning for the installation of protective fences and barriers to prevent falls and site layout planning with a safety approach based on a 3D model are the most important applications of BIM among the 18 applications to improve the safety of construction projects.

Keywords: building information modeling, safety of construction projects, Delphi method, fuzzy logic

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Authors: Halimi Mohamed Taher, Kordoghli Bassem, Ben Hassen Mohamed, Sakli Faouzi

Abstract:

Textile industry has been an important part of many developing countries economies such as Tunisia. This industry is confronted with a challenging and increasing competitive environment. Good quality management in production process is the key factor for retaining existence especially in raw material exploitation. The present work aims to develop an intelligent system for fabric inspection. In the first step, we have studied the method used for fabric control which takes into account the default length and localization in woven. In the second step, we have used a method based on the fuzzy logic to minimize the Demerit point indicator with appropriate total rollers length, so that the quality problem becomes multi-objective. In order to optimize the total fabric quality, we have applied the genetic algorithm (GA).

Keywords: fabric control, Fuzzy logic, genetic algorithm, quality management

Procedia PDF Downloads 559

Authors: Mostafa Mjahed

Abstract:

Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

Procedia PDF Downloads 91