Adaptive Neuro-Fuzzy Inference System

Nonlinear dynamic signal processing is attracting several researchers owing to its complex behavior which may be deterministic at macro level and may be in order but unruly behavior with respect to time is difficult to understand and interpret. EEG signals fall under such categories. Prediction of seizure in EEG is a challenging task. For this several prediction methodologies have been in use from time to time. But the complexity of signals which differ from person to person makes it complicated. . Keeping this view in mind, we propose to have better prediction of chaotic time series through this paper. Though there have been several attempts in the past, our research is related to use of ANFIS for chaotic time series prediction. Correlation dimension are the factors based on which convergent or divergent or chaotic nature of signal is predicted. In this paper we use correlation dimension for feature extraction providing to ANFIS model for giving précised result.

Nonlinear dynamic signal processing is attracting several researchers owing to its complex behavior which may be deterministic at macro level and may be in order but unruly behavior with respect to time is difficult to understand and interpret. EEG signals fall under such categories. Prediction of seizure in EEG is a challenging task. For this several prediction methodologies have been in use from time to time. But the complexity of signals which differ from person to person makes it complicated.. Keeping this view in mind, we propose to have better prediction of chaotic time series through this paper. Though there have been several attempts in the past, our research is related to use of ANFIS for chaotic time series prediction. Correlation dimension are the factors based on which convergent or divergent or chaotic nature of signal is predicted. In this paper we use correlation dimension for feature extraction providing to ANFIS model for giving précised result.

Nonlinear dynamic signal processing is attracting several researchers owing to its complex behavior which may be deterministic at macro level and may be in order but unruly behavior with respect to time is difficult to understand and interpret. EEG signals fall under such categories. Prediction of seizure in EEG is a challenging task. For this several prediction methodologies have been in use from time to time. But the complexity of signals which differ from person to person makes it complicated.. Keeping this view in mind, we propose to have better prediction of chaotic time series through this paper. Though there have been several attempts in the past, our research is related to use of ANFIS for chaotic time series prediction. Correlation dimension are the factors based on which convergent or divergent or chaotic nature of signal is predicted. In this paper we use correlation dimension for feature extraction providing to ANFIS model for giving précised result.

Nonlinear dynamic signal processing is attracting several researchers owing to its complex behavior which may be deterministic at macro level and may be in order but unruly behavior with respect to time is difficult to understand and interpret. EEG signals fall under such categories. Prediction of seizure in EEG is a challenging task. For this several prediction methodologies have been in use from time to time. But the complexity of signals which differ from person to person makes it complicated.. Keeping this view in mind, we propose to have better prediction of chaotic time series through this paper. Though there have been several attempts in the past, our research is related to use of ANFIS for chaotic time series prediction. Correlation dimension are the factors based on which convergent or divergent or chaotic nature of signal is predicted. In this paper we use correlation dimension for feature extraction providing to ANFIS model for giving précised result.

Nonlinear dynamic signal processing is attracting several researchers owing to its complex behavior which may be deterministic at macro level and may be in order but unruly behavior with respect to time is difficult to understand and interpret. EEG signals fall under such categories. Prediction of seizure in EEG is a challenging task. For this several
prediction methodologies have been in use from time to time. But the complexity of signals which differ from person to person makes it complicated. . Keeping this view in mind, we propose to have better prediction of chaotic time series through this paper. Though there have been several attempts in the past, our research is related to use of ANFIS for chaotic time series prediction. Correlation dimension are the factors based on which convergent or divergent or chaotic nature of signal is predicted. In this paper we use correlation dimension for feature extraction providing to ANFIS model for giving précised result.

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We develop a quantitative method of analysis of EEG records. The method is based on the wavelet analysis of the record and on the capability of the Jensen-Shannon divergence (JSD) to identify dynamical changes in a time series. The JSD is a measure of distance between probability distributions. Therefore for its evaluation it is necessary to define a (time dependent) probability distribution along the record. We define this probability distribution from the wavelet decomposition of the associated time series. The wavelet JSD provides information about dynamical changes in the scales and can be considered a complementary methodology reported earlier [

This paper presents the implementation of dual voltage source inverter (DVSI) approach to improve the microgrid performance by enhancing the power quality. This paper also improves the power quality in photovoltaic (PV) generation interactive microgrids, respectively. The power generated from PV based distributive energy resources (DER) is perfectly applied to the microgrid through the two inverters, thus the nonlinear and unbalance load related problems are compensated. Thus, the power quality problems such as voltage sag, current drops, and power factor, active, and reactive powers are reduced by dual multilevel converter (DMLC). This converter also used for cooperative controlling to minimize the unbalances and voltage harmonics in microgrid by using adaptive neuro fuzzy interface system (ANFIS) based controller, which is applied in the shunt and series manner between the two VSIs. The total load current is mutually shared among the series and shunt VSI, if one VSI fails, then the other VSI continues its operation. Thus, the DMLC components failure rate is reduced, and the system lost energy is also reduced, which leads to improved reliability by maintain the reduction of down time price at environmental free conditions. The proposed control model is implemented in MATLAB/Simulink environment, and the obtained results shows that the superiority of total harmonic distortion (THD) reduction by the proposed ANFIS controller as compared to the conventional artificial neural network (ANN) and fuzzy logic controllers (FLC) by eliminating all the load current and grid voltage-based harmonics.

This research would not have been possible without ALLAH, and then the help of many people. First of all, we would like to deeply thank our supervisor, Dr. El Sawi, who improved our experience, knowledge and skills. We have to say that we have been lucky to work with him, and we wish to him all the best. Also we would like to take this opportunity to thank the Deanship of Scientific Research, University of Science and Technology, who fully sponsored this work. We would like to convey our sincere thanks and appreciation to all peoples that share the knowledge and any kind of information with us that help us or at least guide us to accomplish this project These are the last words we write for our graduation project. We wish they would also be the best words, to acknowledge all those who directly and indirectly contributed to the support of this project.

A microgrid is a proven effective way to integrate renewable resources. This study presents an innovative control concept for decentralized AC microgrids, which is based on the architectural advantage of a radial microgrid structure. Under the proposed concept, power sharing between the distributed sources is achieved without droop control. Thus the need for a secondary control level is eliminated. Moreover, the use of explicit communication is replaced in the paper by a novel coordination mechanism based on the locally measured currents. The paper shows that, with a special design of the current control of grid-feeding converters, the proposed microgrid automatically provides equitable sharing of the load demand amongst the distributed generators (DGs). Moreover, the dynamic responses of the DGs are identical and decoupled from one another. It is further shown that the proposed AC microgrid is stable in the presence of any type of load. The findings of the paper are validated by simulations and laboratory experiments. INDEX TERMS Microgrids, decentralized control, DC-AC power converters, distributed power generation.

This research proposes the surface roughness inspection by an adaptive Neuro-fuzzy inference system. The adaptive Neuro-fuzzy inference system model developed by input parameters (Speed, Depth of cut, feed rate, and Grayscale value) and an output parameter (surface roughness). The training and testing module, which is used to generate the surface roughness value. The grayscale value derived from the machinability of the Al7075 workpiece. The machined workpiece image as converted as grayscale value, which is feed into one of the inputs of the adaptive Neuro-fuzzy inference system. The vision measurement value was compared with the stylus probe value for predicting the accuracy level of the adaptive neuro-fuzzy inference system. The accuracy was above 98%, which is helpful for inspecting all machined components in the mass production system.

Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.

Nonlinear electronic loads draw harmonic currents from the power grids that can cause energy loss, miss-operation of power equipment, and other serious problems in the power grids. This paper proposes a harmonic compensation method using multiple distributed resources (DRs) such as small distributed generators (DGs) and battery energy storage systems (BESSs) that are integrated to the power grids through power inverters. For harmonic compensation, DRs should inject additional apparent power to the grids so that certain DRs, especially operated in proximity to their rated power, may possibly reach their maximum current limits. Therefore, intelligent coordination methods of multiple DRs are required for efficient harmonic current compensation considering the power margins of DRs, energy cost, and the battery state-of-charge. The proposed method is based on fuzzy multiobjective optimization so that DRs can cooperate with other DRs to eliminate harmonic currents with optimizing mutually conflicting multi-objectives.

The high temporal variability of wind power generation represents a major challenge for the realization of a sustainable energy supply. Large backup and storage facilities are necessary to secure the supply in periods of low renewable generation, especially in countries with a high share of renewables. We show that strong climate change is likely to impede the system integration of intermittent wind energy. To this end, we analyze the temporal characteristics of wind power generation based on high-resolution climate projections for Europe and uncover a robust increase of backup energy and storage needs in most of Central, Northern and North-Western Europe. This effect can be traced back to an increase of the likelihood for long periods of low wind generation and an increase in the seasonal wind variability.

The aims of the research were to study a face recognition using ANFIS (Adaptive Neuro-Fuzzy Inference System) technique to compared with feedforward backpropagation neural network that used specific Features of Wavelet Transform and HOG (Histogram of Oriented Gradients for feature extraction). The result found that the accuracy of ANFIS face recognition technique was 99% and feedforward back propagation neural network was 98 %.

This work evaluates the potential contribution of renewable energy to energy security in Latin America (LA) in the short and long terms. Two main approaches are used. The first assesses the seasonality and variability of renewable energy resources in LA, as well as their possible complementarities. Variability indices are calculated to assess patterns of wind and solar power resources and a statistical analysis evaluates complementarities between hotspots in the region. The second determines possible impacts on the future long-term wind and solar resource complementarities due to climate change for those regions where complementarity was previously identified. For this, results of two General Circulation Models (GCMs) for two Representative Concentration Pathways-RCPs (RCP 4.5 and RCP 8.5) are used. Results show a higher variability for wind power when compared to solar power generation resources both on an hourly and monthly scales. Results also indicate that Brazil can play an important role regarding renewable energy integration in LA, since the country has the strongest capacity to complement and to be complemented by other LA countries. As for the analysis on climate change impacts, results indicate a strong negative impact on the complementarities in the 2071-2100 period, especially for RCP 8.5. These indices describe the fluctuations of a renewable energy resource and/or the energy generated from it throughout its life spam [6]. It is possible to find in the literature indices designed specifically for wind and solar energy. These indices can be classified according to the parameter on which the variability analysis is performed. In the case of wind energy, for instance, the Energy Yield Production Indices, such as the German IWET-Ingenieurwerkstatt Energietechnik [7]are the most commonly used indices. Other índices include the Wind Speed Index, the Wind power density Index and the Wind Energy Production Index from Wind Data [8]. Wind indeces for a specific site also can be calculated based on the site's wind data and turbine's power curves and are often used for the operational verification of wind turbines. Metrics describing and quantifying variability at different time scales are key-factors to a site characterization [9,10]. In the case of solar energy, the variability of solar irradiation depends on the time scale in which it is analyzed. Thus, long-term variability studies are

A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). The important features of the raw EEG data were extracted using two methods: Wavelet transform and energy estimation. This data was normalized and given as input to the neural network, which was trained using back propagation algorithm. Energy estimation was used as an amplitude threshold parameter. The wavelet transform (WT) is a powerful tool for multi-resolution analysis of non-stationary signal as well as for signal compression, recognition and restoration, which uses Daubechies 4 as the mother wavelet. The details of the wavelet decomposition level, 1, 2, 3 and energy estimation parameters are given as input to the neural network in order to detect spikes. The codes are written in C and implemented on the DSP Processor TMS320VC5402. The waveforms were observed on MATLAB. The effectiveness of the proposed technique was confirmed with and EEG layouts.

This work evaluates the potential contribution of renewable energy to energy security in Latin America (LA) in the short and long terms. Two main approaches are used. The first assesses the seasonality and variability of renewable energy resources in LA, as well as their possible complementarities. Variability indices are calculated to assess patterns of wind and solar power resources and a statistical analysis evaluates complementarities between hotspots in the region. The second determines possible impacts on the future long-term wind and solar resource complementarities due to climate change for those regions where complementarity was previously identified. For this, results of two General Circulation Models (GCMs) for two Representative Concentration Pathways-RCPs (RCP 4.5 and RCP 8.5) are used. Results show a higher variability for wind power when compared to solar power generation resources both on an hourly and monthly scales. Results also indicate that Brazil can play an important role regarding renewable energy integration in LA, since the country has the strongest capacity to complement and to be complemented by other LA countries. As for the analysis on climate change impacts, results indicate a strong negative impact on the complementarities in the 2071-2100 period, especially for RCP 8.5. These indices describe the fluctuations of a renewable energy resource and/or the energy generated from it throughout its life spam [6]. It is possible to find in the literature indices designed specifically for wind and solar energy. These indices can be classified according to the parameter on which the variability analysis is performed. In the case of wind energy, for instance, the Energy Yield Production Indices, such as the German IWET-Ingenieurwerkstatt Energietechnik [7]are the most commonly used indices. Other índices include the Wind Speed Index, the Wind power density Index and the Wind Energy Production Index from Wind Data [8]. Wind indeces for a specific site also can be calculated based on the site's wind data and turbine's power curves and are often used for the operational verification of wind turbines. Metrics describing and quantifying variability at different time scales are key-factors to a site characterization [9,10]. In the case of solar energy, the variability of solar irradiation depends on the time scale in which it is analyzed. Thus, long-term variability studies are

In this study, EEG signals were classified by using the average powers extracted by means of the rectangle approximation window based average power method from the power spectral densities of frequency sub-bands of the signals and two different artificial neural networks (ANNs) which are adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLPNN). In order to evaluate their performances together the proposed approach, four different experiments were implemented by using different mixtures of classes. The experiments showed that both classifiers with the proposed approach resulted in satisfactory classification accuracy rates, although the success of MLPNN classifier was a little better than the other. Streszczenie. W artykule zaprezentowano klasyfikacje sygnalu EEG przy wykorzystanoiu widma gestości mocy w podzakresach czestotliwości oraz sieci neuronowych: adaptacyjnego system neuro-fuzzy ANFIS oraz wielowarstwowego perceptronu MLPNN. (Klasyfikacj...

Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008-2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15-25), [25-35) and ≥ 35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under-or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in underestimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.

This paper briefly introduces soft computing techniques and present miscellaneous application in clinical decision support system  domine. study detects which methodology or methodologies of soft computing are frequently used together to solve the specific problems of  risk based prioritization for decease severity.  With the fulfillment of these work makes several major contribution of the current knowledge of mechanism of different intelligent system such as Fuzzy Logic, ANN and Artificial Neuro Fuzzy for correct diagnosis .

This paper briefly introduces soft computing techniques and present miscellaneous application in clinical decision support system domine. study detects which methodology or methodologies of soft computing are frequently used together to solve the specific problems of risk based prioritization for decease severity. With the fulfillment of these work makes several major contribution of the current knowledge of mechanism of different intelligent system such as Fuzzy Logic, ANN and Artificial Neuro Fuzzy for correct diagnosis .

The machine learning method of Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as a data-driven technique to model the dew point temperature (DPT). The input patterns, of T min, T max, and T mean, are utilized for the training. The results indicate thatANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the proposed machine learning method and the observed data. Using this prediction toolkit, DPT can be adequately predicted based on the temperature distribution. The modeling approach has shown to be promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales where the ANFIS model remains stable for almost all the numbers of the membership functions.

A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). The important features of the raw EEG data were extracted using two methods: Wavelet transform and energy estimation. This data was normalized and given as input to the neural network, which was trained using back propagation algorithm. Energy estimation was used as an amplitude threshold parameter. The wavelet transform (WT) is a powerful tool for multi-resolution analysis of non-stationary signal as well as for signal compression, recognition and restoration, which uses Daubechies 4 as the mother wavelet. The details of the wavelet decomposition level, 1, 2, 3 and energy estimation parameters are given as input to the neural network in order to detect spikes. The codes are written in C and implemented on the DSP Processor TMS320VC5402. The waveforms were observed on MATLAB. The effectiveness of the proposed technique was confirmed with and EEG layouts.

Predicting the epileptic seizure is challenging biomedical problem. EEG signal includes enormous information. Few relevant parameters are expected in the field of recognition and diagnostic purposes. Seizure detection and classification system has been designed and developed. The system uses computer based procedures to detect seizure and classified normal and abnormal subjects. Intelligent compact support property of GLCM is used for extracting essential features from the EEG signal. Selected features are classified using neural network model. The 70 samples would be divided into 50 training samples and 20 testing samples. The back propagation algorithm tested on these samples showed expected classification. The main objective of this study is to predict the epileptic seizure using GLCM feature extraction method and neural network model.

Epileptic seizure occurs as a result of abnormal transient disturbance in the electrical activities of the brain. The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. Therefore, the EEG signals are commonly used signals for obtaining the information related to the states of brain. The EEG recordings of an epileptic patient contain a large amount of EEG data which may require time-consuming manual interpretations. Thus, automatic EEG signal analysis using advanced signal processing techniques with the statistical features plays a significant role to recognize epilepsy in EEG recordings and also reduce the computation complexity. Keywords— EEG; Feature Information;EEG Signal Analysis. ______________________________________________________________________________________________________________

Offshore wind is one of the most important sources of renewable energy. Therefore, it is crucial to assess how this resource will evolve within the 21 st century, in the context of a changing climate. The North African Coastal Low-Level Jet (CLLJ) region, which encompasses offshore areas from Northwest Morocco to Senegal, has an enormous wind harvesting potential, as it provides a strong, persistent alongshore flow. In the current study, the present climate wind energy potential is featured for two heights (100 and 250m). More importantly, the climate change impact on the wind energy density in the region is also depicted. For that purpose, the newest and highest resolution regional climate simulations available are used, which include two ROM simulations (uncoupled and coupled) at 25km resolution, and 19 CORDEX-Africa runs at 50km resolution. Historical and future (under the RCP4.5 and RCP8.5 scenarios) simulations are used, for the periods 1976-2005 and 2070-2099, respectively. Overall, the results show that the annual wind energy density is projected to increase slightly in the northern offshore areas (< +10%) and decrease in the southern ones (>-10%). In close connection to the projected changes for the seasonal changes of the CLLJ system, in the further north regions (downwind Cap Ghir), the spring season shows the largest increases of wind energy, up to +20%, while in the offshore western Sahara it is projected an increase of wind energy in all seasons. For the southern areas, it is expected a decrease of wind energy.

Problem statement: The process of epilepsy diagnosis from EEG signals by a human scorer is a very time consuming and costly task considering the large number of epileptic patients admitted to the hospitals and the large amount of data needs to be scored. Therefore, there is a strong need to automate this process. Such automated systems must rely on robust and effective algorithms for detection and prediction. Approach: The proposed detection system of epileptic seizure in EEG signals is based on Discrete Wavelet Transform (DWT) and Swarm Negative Selection (SNS) algorithm. DWT was used to analyze EEG signals at different frequency bands and statistics over the set of the wavelet coefficients were calculated to introduce the feature vector for SNS classifier. The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. Thus, adapted negative selection is employed to create a set of self-tolerant ALCs. Whereas, PSO is used to evolve these ALCs away from self patterns towards non-self space and to maintain diversity and generality among the ALCs. Results: The experimental results proved that the proposed method reveals very promising performance in classifying EEG signals. A comparison with many previous studies showed that the presented algorithm has better results outperforming those reported by earlier methods. Conclusion: The technique was approved to be robust and effective in detecting and localizing epileptic seizure in EEG recording. Hence, the proposed system can be very helpful to make faster and accurate diagnosis decision.

Epilepsy is a neurological disorder characterized by recurrent, abnormal and synchronous neural activity (seizures) in the brain resulting in characteristic abnormality in the electroencephalographic pattern. An automated detection of epileptic seizures provides assistance to Neurologists in the diagnosis and timely treatment of epileptic patients. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) are two effective classifiers that can be used in an automated epilepsy detection system. This paper attempts to analyze the performance and effectiveness of ANFIS and SVM for epileptic seizure detection taking into consideration various signal features. The ANFIS classifier gives 100% efficiency for two-level classification with Mean Teager Energy and Mean Energy using Pimembership function for input node. The quadratic programming based SVM gave 100% accuracy with interquartile range being the feature for three-level classification of the EEG data.

This study presents a new method for modeling an adaptive neuro-fuzzy inference system (ANFIS) based on vibration for predicting surface roughness in the CNC turning process. The input parameters of the model are insert nose radius, cutting speed, feed rate, depth of cut and vibration amplitude, which determine the output parameter of the surface roughness. A Gauss type membership function was used to train on ANFIS. The predicted values derived from ANFIS were compared with experimental data. The obtained prediction accuracy of 97.52% demonstrates that the developed system's improved performance over other models available in the literature. The resulting ANFIS model based on vibration efficiently uses the fuzzy inference system for predicting surface roughness in turning of AISI 1040 steel.

A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). The important features of the raw EEG data were extracted using two methods: Wavelet transform and energy estimation. This data was normalized and given as input to the neural network, which was trained using back propagation algorithm. Energy estimation was used as an amplitude threshold parameter. The wavelet transform (WT) is a powerful tool for multi-resolution analysis of non-stationary signal as well as for signal compression, recognition and restoration, which uses Daubechies 4 as the mother wavelet. The details of the wavelet decomposition level, 1, 2, 3 and energy estimation parameters are given as input to the neural network in order to detect spikes. The codes are written in C and implemented on the DSP Processor TMS320VC5402. The waveforms were observed on MATLAB. The effectiveness of the proposed technique was confirmed with and EEG layouts.

In the last years, the offshore wind sector has been constantly growing in Europe, coming also with a very competitive production price. The objective of this paper is to evaluate the wind power potential in the European coastal environment of the Mediterranean Sea, an area with a high population density. Furthermore, a high energy demand exists here and the potential of the renewable energy resources needs to be assessed for further exploitation. The analysis was performed considering some reference locations. The climate change impact on the wind energy resource is also investigated. Various comparisons between historical data and future climate projections simulated by a Regional Climate Model under RCP4.5 scenario are made. The results obtained show that in various locations, especially in Golf of Lion and the Aegean Sea, there are appropriate conditions for offshore wind exploitations.

Electroencephalographic (EEG) signals are produced in brain due to firing of the neurons. Any anomaly found in the EEG indicates abnormality associated with brain functioning. The efficacy of automated analysis of EEG depends on features chosen to represent the time series, classifier used and quality of training data. In this work, we present automated analysis of EEG time series acquired from two different groups. Non-linear features have been used here to capture the characteristics of EEG in each case since it portrays the non-linear dependencies of different parameters associated with EEG. In the first case, we present the classification between alcoholics and controls. In the second case, we present classification between epileptic and controls. In the classification, we have addressed the issue of quality of training data. In the proposed scheme prior to classification, we filter the training data. This approach led to minimum 10% improvement in the classification accuracy.

Ultra High Molecular Weight Poly Ethylene (UHMWPE) is a plastic biomaterial developed and had been used for decades in biomedical applications. This material is commonly produced with machining processes. One of those process is the milling operation. This method is needed alongside with the more common turning operation since there are many implant designs are not geometrically cylindrical. The milling process like any other machining techniques requires the operators to set certain machining parameter values to achieve specific objectives. One of these is the surface roughness of the materials. Rough machined surfaces were suspected to harbor more bacteria when the implant products made from UHMWPE are being used inside the human body. This study tested the influences of feed rate and depth of cut machining parameters on the UHMWPE surface roughness. The milling machining process with a SIEMENS CNC machine on the UHMWPE biomaterial was proved capable to produce better surface finish qualities. Four feed rate values between 0.2 mm/rev. to 0.025 mm/rev. and three depth of cuts from 0.2 mm down to 0.05 mm were evaluated. Third machining parameter, i.e. cutting speed was set constant at 150 m/minute. The tool is the high speed steel end milling. Achieved minimum surface roughness was found below 2 microns. A multiple regression model was developed to guide the machine tool operators to determine proper machining parameters. Statistical tests conducted and it can be concluded that feed rate is the most influential parameter. It must be set 0.05 mm/rev. or lower in the purpose to obtain higher quality surface finish.

Surface roughness is considered as one of the most specified customer requirements in machining processes. For efficient use of machine tools, selection of machining process and determination of optimal cutting parameters (speed, feed and depth of cut) are required. Therefore, it is necessary to find a suitable way to select and to find optimal machining process and cutting parameters for a specified surface roughness values. In this work, machining process was carried out on AISI 1040 steel in dry cutting condition in a lathe, milling and grinding machines and surface roughness was measured. Forty five experiments have been conducted using varying speed, feed, and depth of cut in order to find the surface roughness parameters. This data has been divided into two sets on a random basis; 36 training data set and 9 testing data set. The training data set has been used to train different artificial neural network (ANN) models in order to predict machining processes and surface roughness parameter values through back propagation network. Experimental data collected from tests were used as input parameters of a neural network to identify the sensitivity among machining operations, cutting parameters and surface roughness. Selected indexes were used to design a suitable algorithm for the prediction of machining processes. A software was developed and implemented to predict the machining processes and surface roughness values. The results showed that the proposed models are capable of predicting machining operations, cutting parameters and surface roughness.

Electroencephalographic (EEG) signals are produced in brain due to firing of the neurons. Any anomaly found in the EEG indicates abnormality associated with brain functioning. The efficacy of automated analysis of EEG depends on features chosen to represent the time series, classifier used and quality of training data. In this work, we present automated analysis of EEG time series acquired from two different groups. Non-linear features have been used here to capture the characteristics of EEG in each case since it portrays the non-linear dependencies of different parameters associated with EEG. In the first case, we present the classification between alcoholics and controls. In the second case, we present classification between epileptic and controls. In the classification, we have addressed the issue of quality of training data. In the proposed scheme prior to classification, we filter the training data. This approach led to minimum 10% improvement in the classification accuracy.

The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners (physicians) to accelerate diagnosis and treatment processes. In this paper, the sparse matrix approach was incorporated in neural network learning algorithm for scalability, minimize higher memory usage/storage capacity, enhancing implementation time and speed up the analysis of the data. The proposed intelligent classification system maximizes the intelligently classification of data and minimizes the number of trends inaccurately identified. For robustness, the proposed method was tested with three different datasets, namely, Hepatitis, SPECT Heart and Cleveland Heart. Therefore, an attempt was made to determine the performance indicators efficacy. Compared to some similar existing methods, the approach presented achieves improved performance. The program used for implementation of the proposed model is MATLAB R2016a (version 9.0) and executed in the 4.0 GB RAM processor of PC Intel Pentium Quad Core N3700.

This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms. In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, r...

Between seizures, the electroencephalogram (EEG) of subjects who suffer from epilepsy is usually characterised by occasional spikes or spike and wave complexes (inter-ictal activity). These are notoriously difficult to detect reliably, and they are occasionally missed by the clinicians who review the paper records retrospectively. The authors investigate the training and testing of a neural network classifier for the detection of inter-ictal spikes in these subjects. For the characterisation of the EEG signal, they consider both timedomain parameters normalised with respect to context and coefficients from an autoregressive model. It is shown how to use balanced databases to evaluate the discriminatory power of these parameters when they are used as the input features to a multi-layer perceptron (MLP). Both patient-specific classifiers and a generic system tested on independent test subjects are investigated. With the former, spikes are detected with an accuracy varying from 85.6% to 95.6%, a sensitivity varying from 83.1% to 97.3% and a specificity varying from 85.9% to 95.5%. The performance of the generic MLP system is not substantially degraded with respect to this, but there are too many false positives for the system to be considered for regular clinical use at the moment. The authors suggest how this problem might be solved using a combination of techniques.

In this work, a technique is proposed to predict surface roughness by using neural network. Surface roughness could be predicted within a reasonable degree of accuracy by taking feed rate, cutting speed, depth of cut and three orthogonal axis (x,y,z) signals of vibrations of tool holder as input parameters. 27 experiments were performed by using a CNC lathe with a carbide cutting tool. Experimental data obtained from turning process were used for training and testing of neural network architecture based prediction system. When experimental and prediction results were compared, it has been seen that a mean accuracy of 91,17% was achieved.

The Surface roughness prediction method using
artificial neural network (ANN) and Adaptive Neuro Fuzzy
Inference System (ANFIS) are developed to investigate the
effects of cutting conditions during turning of EN8 material.
The ANN model of surface roughness parameters (Ra) is
developed with the cutting conditions such as cutting speed,
feed rate and depth of cut as the affecting process
parameters.
The experiments are planned and totally 27 settings
with three levels defined for each of the factors in order to
develop the knowledge based system. The ANN training
method is used for back-propagation training algorithm
(BPTA) and also for training the Adaptive neuro fuzzy
inference system (ANFIS). We have compared the
Artificial Neural Network and Adaptive neuro fuzzy
inference systems.

The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners (physicians) to accelerate diagnosis and treatment processes. In this paper, the sparse matrix approach was incorporated in neural network learning algorithm for scalability, minimize higher memory usage/storage capacity, enhancing implementation time and speed up the analysis of the data. The proposed intelligent classification system maximizes the intelligently classification of data and minimizes the number of trends inaccurately identified. For robustness, the proposed method was tested with three different datasets, namely, Hepatitis, SPECT Heart and Cleveland Heart. Therefore, an attempt was made to determine the performance indicators efficacy. Compared to some similar existing methods, the approach presented achieves improved performance. The program used for implementation of the proposed model is MATLAB R2016a (version 9.0) and executed in the 4.0 GB RAM processor of PC Intel Pentium Quad Core N3700.

A tsunami can be referred to series of water waves initiated by the dislocation of a large quantity of water, usually in ocean or a large lake. Tsunamis obliterate not only human population but all other species. There are many factors that have potential to cause a tsunami like Earthquakes, volcanic eruptions and other underwater explosions (including explosions of underwater nuclear devices), landslides, meteorite impacts and other disturbances above or below water. With the preface of modern science and computer technology, the field of Artificial Intelligence is screening a definite efficacy in all continuum of life. There are certain ways to predict such disasters and design different kinds of early warning systems. These can be predicted on the basis of climatic conditions and several other parameters. One such concept that is working as a boom in the fields of environmental science and policy is fuzzy logic. As fuzzy logic can be widely stated as working with the vague, imprecise data and the linguistic terms. So, this methodology can be successfully applied in the field of tsunami warning as the influence of different parameters that cause tsunami is different and can be said as vague. In this work, a fuzzy system has been developed to declare the occurrence of tsunami in terms of linguistic parameters rare_tsunami, advisory_tsunami and warning by taking the tsunami characteristics earthquake magnitude, volcanic eruption index, landslide and height of waves in deep ocean. Fuzzy rule base is generated and the results are compared with an IEEE research paper.

In a turning operation, involving removal of material from the outer diameter of a rotating cylindrical workpiece using a single-point cutting tool, there exist complex relationships between various cutting parameters and responses. In this paper, a turning operation under dry environment is considered with cutting speed, feed rate, and depth of cut as the input parameters, as well as material removal rate, average surface roughness, and cutting force as the responses. Dry turning operation reduces energy consumption and machining cost, thus eventually resulting in sustainable machining. For the considered process, the corresponding response values are envisaged using four prediction models, that is, multivariate regression analysis, fuzzy logic, artificial neural network, and adaptive neurofuzzy inference system (ANFIS), and their prediction performance is contrasted using five statistical metrics, that is, root mean squared percent error, mean absolute percentage error, root mean ...

The main aim of this project is Adaptive neuro fuzzy inference system (ANFIS) controller for islanded microgrid under nonlinear balanced/unbalanced load conditions. This article introduces a new control scheme for the independent process of the microgrid containing of DG units for electronic coupling. The DG units are attached to a common connection point and provide supply to unbalanced and nonlinear loads. In the field of system topology & constraints of the load is normally unknown. The current at load is assumed to be computable and minimal. Adaptive neuro fuzzy controller used for controlling the DG unit for better performance and reduces the THD of DG voltage. In the DGs the DC source replaced by PV source for balancing the load demand. The MATLAB results show that despite various unbalanced and nonlinear load conditions, the proposed controller provides a number of sinusoidal, three-phase, equilibrium voltages.

In statistical downscaling technique, regional or local information are derived by determining a statistical model which relates large-scale climate variables or predictors generated by Global Climate Models (GCMs) to regional and local variables or predictands. In this paper, the results of GCMs were statistically downscaled to produce future climate projections of mean temperature in the post-monsoon season (October and November), for the time periods 2021-2050 and 2071-2100 for Bangladesh. The future climate projections are based on the three emission scenarios RCP2.6, RCP4.5 and RCP8.5 provided by the fifth Coupled Model Intercomparison Project (CMIP5). This paper established a method to analyze GCMs for use in statistical downscaling and utilized fifteen GCMs. The GCMs were assessed based upon their performance in simulated past climate in Bangladesh and adjoining areas. Downscaling was undertaken by linking large scale climate variables, taken from the ERA-Interim (resolution ...