Search results for: fuzzy system

Authors: Hazim Abdulsada

Abstract:

The inspection of underneath vehicle system has been given significant attention by governments after the threat of terrorism become more prevalent. New technologies such as mobile robots and computer vision are led to have more secure environment. This paper proposed that a mobile robot like Aria robot can be used to search and inspect the bombs under parking a lot vehicle. This robot is using fuzzy logic and subsumption algorithms to control the robot that movies underneath the vehicle. An OpenCV library and laser Hokuyo are added to Aria robot to complete the experiment for under vehicle inspection. This experiment was conducted at the indoor environment to demonstrate the efficiency of our methods to search objects and control the robot movements under vehicle. We got excellent results not only by controlling the robot movement but also inspecting object by the robot camera at same time. This success allowed us to know the requirement to construct a new cost effective robot with more functionality.

Keywords: fuzzy logic, mobile robots, Opencv, subsumption, under vehicle inspection

Procedia PDF Downloads 442

Authors: N. Samarinas, C. Evangelides, C. Vrekos

Abstract:

The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: classification, fuzzy logic, tolerance relations, rainfall data

Procedia PDF Downloads 277

Authors: Dejan Tanikić, Jelena Đoković, Saša Kalinović, Miodrag Manić, Saša Ranđelović

Abstract:

This paper deals with measuring and modelling of the quality of the machined surface of the metal machining process. The average surface roughness (R_a) which represents the quality of the machined part was measured during the dry turning of the AISI 4140 steel. A large number of factors with the unknown relations among them influences this parameter, and that is why mathematical modelling is extremely complicated. Different values of cutting speed, feed rate, depth of cut (cutting regime) and workpiece hardness causes different surface roughness values. Modelling with soft computing techniques may be very useful in such cases. This paper presents the usage of the fuzzy logic-based system for determining metal machining process parameter in order to find the proper values of cutting regimes.

Keywords: fuzzy logic, metal machining, process modeling, surface roughness

Procedia PDF Downloads 132

Authors: Abdelkader Merah, Kada Hartani

Abstract:

A reference model based control approach for improving behavior following car is proposed in this paper. The reference model is nonlinear and provides dynamic solutions consistent with safety constraints and comfort specifications. a robust fuzzy logic based control strategy is further proposed in this paper. A set of simulation results showing the suitability of the proposed technique for various demanding cenarios is also included in this paper.

Keywords: reference model, longitudinal control, fuzzy logic, design of car

Procedia PDF Downloads 393

Authors: Raghad Alhusari, Farag Omar, Moustafa Fadel

Abstract:

A traditional greenhouse is a metal frame agricultural building used for cultivation plants in a controlled environment isolated from external climatic changes. Using greenhouses in agriculture is an efficient way to reduce the water consumption, where agriculture field is considered the biggest water consumer world widely. Controlling greenhouse environment yields better productivity of plants but demands an increase of electric power. Although various control approaches have been used towards greenhouse automation, most of them are applied to traditional greenhouses with ventilation fans and/or evaporation cooling system. Such approaches are still demanding high energy and water consumption. The aim of this research is to develop a fuzzy control system that minimizes water and energy consumption by utilizing outside weather conditions and underground heat exchanger to maintain the optimum climate of the greenhouse. The proposed control system is implemented on an experimental model of thermally isolated greenhouse structure with dimensions of 6x5x2.8 meters. It uses fans for extracting heat from the ground heat exchanger system, motors for automatic open/close of the greenhouse windows and LED as lighting system. The controller is integrated also with environmental condition sensors. It was found that using the air-to-air horizontal ground heat exchanger with 90 mm diameter and 2 mm thickness placed 2.5 m below the ground surface results in decreasing the greenhouse temperature of 3.28 ˚C which saves around 3 kW of consumed energy. It also eliminated the water consumption needed in evaporation cooling systems which are traditionally used for cooling the greenhouse environment.

Keywords: automation, earth-to-air heat exchangers, fuzzy control, greenhouse, sustainable buildings

Procedia PDF Downloads 103

Authors: Xiong Xie, Yuhang Chen

Abstract:

Vulnerability discovery technology is a significant field of the current. In this paper, a fuzzy framework based on concolic execution has been proposed. Fuzzy test and symbolic execution are widely used in the field of vulnerability discovery technology. But each of them has its own advantages and disadvantages. During the path generation stage, path traversal algorithm based on generation is used to get more accurate path. During the constraint solving stage, dynamic concolic execution is used to avoid the path explosion. If there is external call, the concolic based on function summary is used. Experiments show that the framework can effectively improve the ability of triggering vulnerabilities and code coverage.

Keywords: concolic execution, constraint solving, fuzzy test, vulnerability discovery

Procedia PDF Downloads 196

Authors: Omar Al Denali, Abdelaziz Badi

Abstract:

The ASTM A516 Grade 70 steel is a suitable material used for the fabrication of boiler pressure vessels working in moderate and lower temperature services, and it has good weldability and excellent notch toughness. The post-weld heat treatment (PWHT) or stress-relieving heat treatment has significant effects on avoiding the martensite transformation and resulting in high hardness, which can lead to cracking in the heat-affected zone (HAZ). An adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the material tensile strength of post-weld heat treatment (PWHT) experiments. The ANFIS models presented excellent predictions, and the comparison was carried out based on the mean absolute percentage error between the predicted values and the experimental values. The ANFIS model gave a Mean Absolute Percentage Error of 0.556 %, which confirms the high accuracy of the model.

Keywords: prediction, post-weld heat treatment, adaptive neuro-fuzzy inference system, mean absolute percentage error

Procedia PDF Downloads 117

Authors: Waleed Ahmed, Huang Xiaohua, Wilayat Ali

Abstract:

The wheelchair users are generally exposed to physical and psychological health problems, e.g., pressure sores and pain in the hip joint, associated with seating posture or being inactive in a wheelchair for a long time. Reclining Wheelchair with back, thigh, and leg adjustment helps in daily life activities and health preservation. The seat elevating function of an electric wheelchair allows the user (lower limb amputation) to reach different heights. An electric wheelchair is expected to ease the lives of the elderly and disable people by giving them mobility support and decreasing the percentage of accidents caused by users’ narrow sight or joystick operation errors. Thus, this paper proposed the design, analysis and obstacle avoidance control of an electric wheelchair with sit-sleep-seat elevation functions. A 3D model of a wheelchair is designed in SolidWorks that was later used for multi-body dynamic (MBD) analysis and to verify driving control system. The control system uses the fuzzy algorithm to avoid the obstacle by getting information in the form of distance from the ultrasonic sensor and user-specified direction from the joystick’s operation. The proposed fuzzy driving control system focuses on the direction and velocity of the wheelchair. The wheelchair model has been examined and proven in MSC Adams (Automated Dynamic Analysis of Mechanical Systems). The designed fuzzy control algorithm is implemented on Gazebo robotic 3D simulator using Robotic Operating System (ROS) middleware. The proposed wheelchair design enhanced mobility and quality of life by improving the user’s functional capabilities. Simulation results verify the non-accidental behavior of the electric wheelchair.

Keywords: fuzzy logic control, joystick, multi body dynamics, obstacle avoidance, scissor mechanism, sensor

Procedia PDF Downloads 106

Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

Abstract:

Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

Procedia PDF Downloads 218

Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

Abstract:

Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

Procedia PDF Downloads 395

Authors: Trishen Naidoo, Anthony Walker, Shaniel Davrajh, Glen Bright

Abstract:

An investigation into on-line inspection was performed where research is focused on the use of varied inspection (as opposed to 100% inspection) for mass customization (MC). Manufacturers need new methods for quality control in mass customization, and these methods need to address some of the old problems such as over-inspection and bottlenecking. Due to the risks of varied inspection, many manufacturers do not implement it and rather opt for sampling methods. However, there are many advantages of varied inspection and can have applications in mass customization. A control system incorporating fuzzy logic (FL) control is used to perform the variations in inspection usage in a simulated environment. The proposed system can have a key impact in appraisal costs reduction and possibly work-in-process reduction in high variety environments.

Keywords: appraisal costs, fuzzy logic, quality control, work-in-process

Procedia PDF Downloads 198

Authors: Elena Tkachenko, Elena Rogova, Daria Koval

Abstract:

The paper examines the ways of cash flows forecasting in the dynamic external environment. The so-called new reality in economy lowers the predictability of the companies’ performance indicators due to the lack of long-term steady trends in external conditions of development and fast changes in the markets. The traditional methods based on the trend analysis lead to a very high error of approximation. The macroeconomic situation for the last 10 years is defined by continuous consequences of financial crisis and arising of another one. In these conditions, the instruments of forecasting on the basis of fuzzy sets show good results. The fuzzy sets based models turn out to lower the error of approximation to acceptable level and to provide the companies with reliable cash flows estimation that helps to reach the financial stability. In the paper, the applicability of the model of cash flows forecasting based on fuzzy logic was analyzed.

Keywords: cash flow, industrial enterprise, forecasting, fuzzy sets

Procedia PDF Downloads 168

Authors: Emad A. Mohammed

Abstract:

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

Procedia PDF Downloads 114

Authors: N. Ould cherchali, M. S. Boucherit, L. Barazane, A. Morsli

Abstract:

Photovoltaic power is widely used to supply isolated or unpopulated areas (lighting, pumping, etc.). Great advantage is that this source is inexhaustible, it offers great safety in use and it is clean. But the dynamic models used to describe a photovoltaic system are complicated and nonlinear and due to nonlinear I-V and P–V characteristics of photovoltaic generators, a maximum power point tracking technique (MPPT) is required to maximize the output power. In this paper, two online techniques of maximum power point tracking using robust controller for photovoltaic systems are proposed, the first technique use fuzzy logic controller (FLC) and the second use sliding mode controller (SMC) for photovoltaic systems. The two maximum power point tracking controllers receive the partial derivative of power as inputs, and the output is the duty cycle corresponding to maximum power. A Photovoltaic generator with Boost converter is developed using MATLAB/Simulink to verify the preferences of the proposed techniques. SMC technique provides a good tracking speed in fast changing irradiation and when the irradiation changes slowly or is constant the panel power of FLC technique presents a much smoother signal with less fluctuations.

Keywords: fuzzy logic controller, maximum power point, photovoltaic system, tracker, sliding mode controller

Procedia PDF Downloads 509

Authors: Swathi Sampath, V. Kalaichelvi

Abstract:

The assessment of the risk posed by a borrower to a lender is one of the common problems that financial institutions have to deal with. Consumers vying for a mortgage are generally compared to each other by the use of a number called the Credit Score, which is generated by applying a mathematical algorithm to information in the applicant’s credit report. The higher the credit score, the lower the risk posed by the candidate, and the better he is to be taken on by the lender. The objective of the present work is to use fuzzy logic and linguistic rules to create a model that generates Credit Scores.

Keywords: credit scoring, fuzzy logic, mortgage, risk assessment

Procedia PDF Downloads 372

Authors: Preeda Sansakorn, Min An

Abstract:

In order to be capable of dealing with uncertainties, subjectivities, including vagueness arising in building construction projects, the application of fuzzy reasoning technique based on fuzzy set theory is proposed. This study contributes significantly to the development of a fuzzy reasoning safety risk assessment model for building construction projects that could be employed to assess the risk magnitude of each hazardous event identified during construction, and a third parameter of probability of consequence is incorporated in the model. By using the proposed safety risk analysis methodology, more reliable and less ambiguities, which provide the safety risk management project team for decision-making purposes.

Keywords: safety risk assessment, building construction safety, fuzzy reasoning, construction risk assessment model, building construction projects

Procedia PDF Downloads 453

Authors: Jyoti Bharti, Navneet Manjhi, M. K.Gupta, Bimi Jain

Abstract:

A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of gray scale and silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and nearest neighbour classifier are used for classification. Both achieved higher recognition.

Keywords: gait, fuzzy logic, nearest neighbour, recognition rate, moments

Procedia PDF Downloads 721

Authors: H. Bonakdari, I. Ebtehaj

Abstract:

The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

Procedia PDF Downloads 182

Authors: Saad M. Darwish, Magda M. Madbouly, Murad B. Khorsheed

Abstract:

Sign Languages (SL) are the most accomplished forms of gestural communication. Therefore, their automatic analysis is a real challenge, which is interestingly implied to their lexical and syntactic organization levels. Hidden Markov models (HMM’s) have been used prominently and successfully in speech recognition and, more recently, in handwriting recognition. Consequently, they seem ideal for visual recognition of complex, structured hand gestures such as are found in sign language. In this paper, several results concerning static hand gesture recognition using an algorithm based on Type-2 Fuzzy HMM (T2FHMM) are presented. The features used as observables in the training as well as in the recognition phases are based on Singular Value Decomposition (SVD). SVD is an extension of Eigen decomposition to suit non-square matrices to reduce multi attribute hand gesture data to feature vectors. SVD optimally exposes the geometric structure of a matrix. In our approach, we replace the basic HMM arithmetic operators by some adequate Type-2 fuzzy operators that permits us to relax the additive constraint of probability measures. Therefore, T2FHMMs are able to handle both random and fuzzy uncertainties existing universally in the sequential data. Experimental results show that T2FHMMs can effectively handle noise and dialect uncertainties in hand signals besides a better classification performance than the classical HMMs. The recognition rate of the proposed system is 100% for uniform hand images and 86.21% for cluttered hand images.

Keywords: hand gesture recognition, hand detection, type-2 fuzzy logic, hidden Markov Model

Procedia PDF Downloads 429

Authors: Seyed Sadegh Naseralavi, Najmeh Bemani

Abstract:

In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.

Keywords: adaptive neuro fuzzy inference system, anticipate method, artificial neural network, concrete design code, multi-variable regression

Procedia PDF Downloads 243

Authors: Mary Anne Roa

Abstract:

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

Procedia PDF Downloads 357

Authors: Fatemeh Torfi

Abstract:

Estimation of stochastic demand in physical distribution in general and efficient transport routs management in particular is emerging as a crucial factor in urban planning domain. It is particularly important in some municipalities such as Tehran where a sound demand management calls for a realistic analysis of the routing system. The methodology involved critically investigating a fuzzy least-squares linear regression approach (FLLRs) to estimate the stochastic demands in the vehicle routing problem (VRP) bearing in mind the customer's preferences order. A FLLR method is proposed in solving the VRP with stochastic demands. Approximate-distance fuzzy least-squares (ADFL) estimator ADFL estimator is applied to original data taken from a case study. The SSR values of the ADFL estimator and real demand are obtained and then compared to SSR values of the nominal demand and real demand. Empirical results showed that the proposed methods can be viable in solving problems under circumstances of having vague and imprecise performance ratings. The results further proved that application of the ADFL was realistic and efficient estimator to face the stochastic demand challenges in vehicle routing system management and solve relevant problems.

Keywords: fuzzy least-squares, stochastic, location, routing problems

Procedia PDF Downloads 395

Authors: Nastaran Hajiheydari, Mohammad Soltani Delgosha

Abstract:

Vendor (supplier) selection is a group decision-making (GDM) process, in which, based on some predetermined criteria, the experts’ preferences are provided in order to rank and choose the most desirable suppliers. In the real business environment, our attitudes or our choices would be made in an uncertain and indecisive situation could not be expressed in a crisp framework. Intuitionistic fuzzy sets (IFSs) could handle such situations in the best way. VIKOR method was developed to solve multi-criteria decision-making (MCDM) problems. This method, which is used to determine the compromised feasible solution with respect to the conflicting criteria, introduces a multi-criteria ranking index based on the particular measure of 'closeness' to the 'ideal solution'. Until now, there has been a little investigation of VIKOR with IFS, therefore we extended the intuitionistic fuzzy (IF) VIKOR to solve vendor selection problem under IF GDM environment. The present study intends to develop an IF VIKOR method in a GDM situation. Therefore, a model is presented to calculate the criterion weights based on entropy measure. Then, the interval-valued intuitionistic fuzzy weighted geometric (IFWG) operator utilized to obtain the total decision matrix. In the next stage, an approach based on the positive idle intuitionistic fuzzy number (PIIFN) and negative idle intuitionistic fuzzy number (NIIFN) was developed. Finally, the application of the proposed method to solve a vendor selection problem illustrated.

Keywords: group decision making, intuitionistic fuzzy set, intuitionistic fuzzy entropy measure, vendor selection, VIKOR

Procedia PDF Downloads 120

Authors: Chidozie C. Nwobi-Okoye, Basil Q. Ochieze, Stanley Okiy

Abstract:

This research reports on the modeling of age hardening process using adaptive neuro-fuzzy inference system (ANFIS). The age hardening output (Hardness) was predicted using ANFIS. The input parameters were ageing time, temperature and percentage composition of cow horn particles (CHp%). The results show the correlation coefficient (R) of the predicted hardness values versus the measured values was of 0.9985. Subsequently, values outside the experimental data points were predicted. When the temperature was kept constant, and other input parameters were varied, the average relative error of the predicted values was 0.0931%. When the temperature was varied, and other input parameters kept constant, the average relative error of the hardness values predictions was 80%. The results show that ANFIS with coarse experimental data points for learning is not very effective in predicting process outputs in the age hardening operation of A356 alloy/CHp particulate composite. The fine experimental data requirements by ANFIS make it more expensive in modeling and optimization of age hardening operations of A356 alloy/CHp particulate composite.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), age hardening, aluminum alloy, metal matrix composite

Procedia PDF Downloads 119

Authors: Mahmood Otadi, Maryam Mosleh

Abstract:

The hybrid differential equations have a wide range of applications in science and engineering. In this paper, the homotopy analysis method (HAM) is applied to obtain the series solution of the hybrid differential equations. Using the homotopy analysis method, it is possible to find the exact solution or an approximate solution of the problem. Comparisons are made between improved predictor-corrector method, homotopy analysis method and the exact solution. Finally, we illustrate our approach by some numerical example.

Keywords: fuzzy number, fuzzy ODE, HAM, approximate method

Procedia PDF Downloads 479

Authors: Abdelkhalek Bakkari

Abstract:

Image segmentation is a critical step in image processing and pattern recognition. In this paper, we proposed a new robust automatic image classification based on a dynamic fuzzy c-means algorithm and mathematical morphology. The proposed segmentation algorithm (DFCM_MM) has been applied to MR perfusion images. The obtained results show the validity and robustness of the proposed approach.

Keywords: segmentation, classification, dynamic, fuzzy c-means, MR image

Procedia PDF Downloads 439

Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

Abstract:

Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

Procedia PDF Downloads 113

Authors: Liliana Villavicencio, Yohn Garcia, Pallavi Singh, Luis Fernando Cruz, Wilfrido Moreno

Abstract:

Psychophysiological adaptive automation is a concept that combines human physiological data and computer algorithms to create personalized interfaces and experiences for users. This approach aims to enhance human learning by adapting to individual needs and preferences and optimizing the interaction between humans and machines. According to neurosciences, the working memory demand during the student learning process is modified when the student is learning a new subject or topic, managing and/or fulfilling a specific task goal. A sudden increase in working memory demand modifies the level of students’ attention, engagement, and cognitive load. The proposed psychophysiological adaptive automation system will adapt the task requirements to optimize cognitive load, the process output variable, by monitoring the student's brain activity. Cognitive load changes according to the student’s previous knowledge, the type of task, the difficulty level of the task, and the overall psychophysiological state of the student. Scaling the measured cognitive load as low, medium, or high; the system will assign a task difficulty level to the next task according to the ratio between the previous-task difficulty level and student stress. For instance, if a student becomes stressed or overwhelmed during a particular task, the system detects this through signal measurements such as brain waves, heart rate variability, or any other psychophysiological variables analyzed to adjust the task difficulty level. The control of engagement and stress are considered internal variables for the hypermedia system which selects between three different types of instructional material. This work assesses the feasibility of a fuzzy controller to track a student's physiological responses and adjust the learning content and pace accordingly. Using an industrial automation approach, the proposed fuzzy logic controller is based on linguistic rules that complement the instrumentation of the system to monitor and control the delivery of instructional material to the students. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the delivery of academic content based on the working memory demand without compromising students’ health. This work has a potential application in the instructional design of virtual reality environments for training and education.

Keywords: fuzzy logic controller, hypermedia control system, personalized education, psychophysiological adaptive automation

Procedia PDF Downloads 51

Authors: M. S. Annie Christi

Abstract:

Transportation Problem (TP) is based on supply and demand of commodities transported from one source to the different destinations. Usual methods for finding solution of TPs are North-West Corner Rule, Least Cost Method Vogel’s Approximation Method etc. The transportation costs tend to vary at each time. We can use fuzzy numbers which would give solution according to this situation. In this study the Best Candidate Method (BCM) is applied. For ranking Centroid Ranking Technique (CRT) and Robust Ranking Technique have been adopted to transform the fuzzy TP and the above methods are applied to EDWARDS Vacuum Company, Crawley, in West Sussex in the United Kingdom. A Comparative study is also given. We see that the transportation cost can be minimized by the application of CRT under BCM.

Keywords: best candidate method, centroid ranking technique, fuzzy transportation problem, robust ranking technique, transportation problem

Procedia PDF Downloads 256

Authors: Jia-Shiun Chen, Hsiu-Ying Hwang

Abstract:

Hybrid electric vehicles can reduce pollution and improve fuel economy. Power-split hybrid electric vehicles (HEVs) provide two power paths between the internal combustion engine (ICE) and energy storage system (ESS) through the gears of an electrically variable transmission (EVT). EVT allows ICE to operate independently from vehicle speed all the time. Therefore, the ICE can operate in the efficient region of its characteristic brake specific fuel consumption (BSFC) map. The two-mode powertrain can operate in input-split or compound-split EVT modes and in four different fixed gear configurations. Power-split architecture is advantageous because it combines conventional series and parallel power paths. This research focuses on input-split and compound-split modes in the two-mode power-split powertrain. Fuzzy Logic Control (FLC) for an internal combustion engine (ICE) and PI control for electric machines (EMs) are derived for the urban driving cycle simulation. These control algorithms reduce vehicle fuel consumption and improve ICE efficiency while maintaining the state of charge (SOC) of the energy storage system in an efficient range.

Keywords: hybrid electric vehicle, fuel economy, two-mode hybrid, fuzzy control

Procedia PDF Downloads 349