Journal of Intelligent & Fuzzy Systems - Volume 35, issue 5

Authors: Ansari, Abdul Quaiyum | Hasan, Mashhood | Islam, Noorul

Article Type: Research Article

Abstract: The battery era has started to compensate the demand of the energy while the charging issues still exist. Thus, demand of reliable and optimized charging is required to charge cell/battery. In this paper novel optimized technique is proposed, based on gravitational search algorithm (GSA) to charge e-rickshaw battery using single sensor based maximum power point tracking (MPPT) of solar photovoltaic (SPV) module. There are various metaheauristic and heuristic techniques are available like Cauchy and Gaussian sine cosine optimization (CGSCO) intelligent technique, evolutionary algorithms, stochastic algorithms, Swarm optimization technique, ant colony technique, neural algorithms, fuzzy logic algorithms to optimize the charging …current of cell/battery. These techniques take more iteration to give the optimal solution. Moreover, GSA is the high level intelligent technique which is used in multi area to optimize the various parameters in engineering fields. It is very ease to find the optimal solution in search space. This approach is novel in the field of e-rickshaw battery charging. Therefore, the mathematical algorithm based on GSA has been developed to optimize the current of charging cell/battery. The performance of GSA optimization technique is verified and compared with the metaheauristic based CGSCO optimization technique. It is observed that GSA is easy to design and reduce the cost of charger. Show more

Keywords: Gravitational search algorithm, gravitational constant, boost converter, insolation, agent

DOI: 10.3233/JIFS-169792

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5077-5084, 2018

Authors: Jain, Harshit | Fatema, Nuzhat

Article Type: Research Article

Abstract: User activity classification is one of the most popular research topic in the domain of health care and social care, since this automated technology can provide monitoring and understanding of activities of patients. Smartphone inbuilt sensors based User Activity Classifier (UAC) recognizes user activities using features extracted from sensors like accelerometer and gyroscope in build in smartphones. In this research paper, we are proposing a new user activity classifier system using Layer Recurrent Neural Network (LRNN) which is Artificial Neural Network (ANN). We utilize synthesized data, containing features of user activity classification system, extracted from the raw data recorded in …smartphones. With these derived features, we train and test Layer Recurrent Neural Network classifier for user activity classifier. In order to evaluate this system, we have compared the performance of this Layer Recurrent Neural Network based user activity classifier against the convention Multilayer Perceptron (MLP) and Naive Bayes based user activity classifier. Test results show that the proposed Layer Recurrent Neural Network -based user activity classifier is able to recognize user activities reliably and outperforms the Multilayer Perceptron based user activity classifier. We have achieved the classification accuracy of 98.56% for the activities. The results are much more accurate than Multilayer Perceptron based classifier and Naive Bayes classifier. Show more

Keywords: Human activity, classification, artificial neural network, naive bayes classifier, smartphone

DOI: 10.3233/JIFS-169793

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5085-5097, 2018

Authors: Narayan, Yogendra | Mathew, Lini | Chatterji, S.

Article Type: Research Article

Abstract: Selection of suitable features plays a pivotal role in Electromyography pattern recognition (EMG-PR) based system designing. Time-domain features are widely used in EMG-PR based application and show improved proficiency in the development of rehabilitation robotics. Even though, the performance of existing features is not satisfactory. In this study, we proposed four novel time-domain features obtained by using first-order differentiation of original surface electromyogram (sEMG) signals feature. Here, sEMG signals were acquired from ten healthy volunteers with the help of myotrace400 device for six different arm movements. The data acquisition and pre-processing stage were carried out followed by the feature extraction …process for better classification results. Four different classifiers namely, k-nearest neighbors (KNN), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Medium tree (MT) classifiers were utilized for the performance evaluation of proposed and conventional features. Experimental results demonstrate that proposed features extracted by using first-order differentiation of sEMG signals feature attained better classification accuracy with MT classifier as compared to the feature extracted from original sEMG signals with the conventional features. The accuracy of proposed feature based on first-order differentiation improved up to 6%. The results indicate that proposed features may be considered for developing the EMG-PR based system designing. Show more

Keywords: sEMG signal, pattern recognition, time domain features, differentiation technique, classification accuracy

DOI: 10.3233/JIFS-169794

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5099-5109, 2018

Authors: Kukker, Amit | Sharma, Rajneesh

Article Type: Research Article

Abstract: This work comes up with a reinforcement learning (RL) based neural classifier for elbow, finger and hand movements for a multitasking prosthetic hand. First, key statistical features are extracted from the Electromyogram (EMG) signals pertaining to elbow angles, finger movements for typing keys and hand movements. Next, these statistical features are fed to a neural network based reinforcement learning (NNRL) classifier for predicting elbow angle, typing key finger movements and hand movements. For the first task (elbow angle) EMG signals have been recorded with varying weights for different elbow positions; for the second task (typing keys) EMG data is for …four tying keys and for the last task (hand movement) EMG data pertains to six hand movements. For the elbow angle prediction task, EMG signal for two channels: Biceps (channel 1) and Triceps (channel 2) has been recorded for 10 subjects and we extract 4 features from each channel. The classifier is able to achieve an average classification accuracy of 97.51%. For the typing keys and hand movement classification tasks, we have used two channels: right hand (channel 1) and left hand (channel 2) for EMG and extract 4 features for the typing keys and 10 features for hand movement. The classifier achieved an accuracy of 98.73% and 97.6% for the typing keys and hand movements tasks, respectively. NNRL gives superior results in comparison to the existing classifiers. High classification accuracy achieved by NNRL classifier shows that our approach could be used as a stepping stone for building a multi-tasking prosthetic hand. Show more

Keywords: Electromyogram (EMG), Typing task, Hand movement, Elbow movement, NNRL

DOI: 10.3233/JIFS-169795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5111-5121, 2018

Authors: Shahid, Abdulla | Wahab, Mohd | Rafiuddin, Nidal | Saad Bin Arif, M. | Malik, Hasmat

Article Type: Research Article

Abstract: Brain-computer interface may be delineated as the merger of machine and software through which brain activity is allowed to govern a peripheral device or computer. The major aim is to aid a critically paralyzed person to live a normal healthy life. This arrangement passes over numerous stages which include data acquisition, feature extraction, data classification and control. The present work emphasizes the use of selective wavelet based features and classifies them using an artificial intelligence based technique namely support vector machine for wrist movement in four different directions. The data base used is the data set-3 of Brain-computer interface competition-4, …which pertains to MEG signals acquired from two healthy subjects performing wrist movement in four different directions. The signal was processed using both wavelet packet transform and discrete wavelet transform and thereafter statistical features were extracted. The best discriminating features were selected after ranking all the extracted features using Principle component analysis. These features were then fed to the support vector machine based classifier for classification. The accuracy achieved is better than most reported in theliterature. Show more

Keywords: BCI, MEG, support vector machine, wavelet packet transform, discrete wavelet transform

DOI: 10.3233/JIFS-169796

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5123-5130, 2018

Authors: Fatema, Nuzhat

Article Type: Research Article

Abstract: Quality management is for providing better resources to the customers. It is applicable for hospitals for giving best services. QM has an initiative i.e. FMEA, which is applicable in the purchasing process of the hospital (PPH) for calculating the RPN, which determines the risks linked with the problems occurring in purchasing the particular equipment for the hospital. RPN is conquered from past experience and engineering decisions which leads to errors and discrepancies. In this study, Neuro-Fuzzy approach based technique is applied to improve the purchasing process in Indian private hospitals. Neuro-Fuzzy approach eliminates insufficiencies in the assessment of the RPN, …resulting in saving of time. PPH in India has never been improved before by applying neuro-fuzzy scheme based FMEA technique. Analyzed results show that the applied neuro-fuzzy method is able to solve the problems which arise from conventional FMEA approach and will effectively find out RPN. Proposed method provides quality assurance in the process. Show more

Keywords: Artificial neural network (ANN), Fuzzy logic, FMEA, quality management, Risk priority numbers (RPN)

DOI: 10.3233/JIFS-169797

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5131-5146, 2018

Authors: Agrawal, Sudhir | Giri, V.K. | Tiwari, A.N.

Article Type: Research Article

Abstract: In industry loading on bearing and its fault severity in an induction motor is unpredictable. Hence, in the present article wide range of vibration data from induction motor bearing surface has been taken for extraction of fault features and classified for the detection of mechanical faults presents in the bearing so that condition based monitoring possible. The vibration data which is selected in this paper includes four different kinds of loading and three different types of fault with three different fault sizes. Firstly, Wavelet Packet Transform (WPT) is applied to decompose the vibration signal and develop Bearing Damage Index (BDI) …from the decomposed signal to select the useful signal from the original recorded signal. This BDI based useful signal is further applied for extraction of statistical features and fed to the classifier. Total eleven time domain features has been calculated and Principal Component Analysis (PCA) is applied for the selection of significant features. The selected features further used as input to the Dendogram Support Vector Machine (DSVM) classifier to identify the faults. This proposed method shows significant improvement in classification rate as compared to conventional method, which is quite promising and encouraging. Show more

Keywords: Bearing damage index (BDI), dendogram, support vector machine (DSVM), principal component analysis (PCA), wavelet packet transform (WPT)

DOI: 10.3233/JIFS-169798

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5147-5158, 2018

Authors: Rajinder, | Sreejeth, Mini | Singh, Madhusudan

Article Type: Research Article

Abstract: Being the most widely used motor in all types of industrial and domestic applications, operation of the induction motor at optimal efficiency is paramount for conservation of energy. This paper presents simple and easily realizable techniques for implementation of a PI and Fuzzy Logic controller based efficiency optimization algorithm for a vector controlled induction motor drive. This is obtained by optimal control of the flux current component of the IM drive to reduce the core losses under light load conditions. A new approach to optimize the efficiency based on optimal control of iron losses only is introduced and compared …with the optimal control of the total losses. The developed algorithm is simulated using MATLAB/Simulink and is tested under different operating conditions. Show more

Keywords: Dynamic model, efficiency, induction motor, iron losses, optimization, vector control

DOI: 10.3233/JIFS-169799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5159-5167, 2018

Authors: Asim, Mohammed | Riyaz, Ahmed | Tiwari, Saurabh | Verma, Archana

Article Type: Research Article

Abstract: DC power supply is required by majority of power electronics devices such as Uninterrupted Power Supply, rectifier Switch Mode Power Supply etc. These draw highly distorted input current of nonlinear nature for a short time. This results in higher total harmonic distortion (THD) due to interference in other electrical equipments. The problems related to poor power factor are mitigated by Power factor correction (PFC) techniques. A comparative study based on type of frequency, current amplifier IC’s used etc. This paper explains the development of fuzzy logic-based PFC operating in continuous conduction mode. Analysis has been carried out for different …load and power factor is seen to improve with increase in the load. Show more

Keywords: Fuzzy logic, PFC, boost converter

DOI: 10.3233/JIFS-169800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5169-5175, 2018

Authors: Tabrez, Md. | Bakhsh, Farhad Ilahi | Hassan, Mahboob | Shamganth, K. | Al-Ghnimi, Sami

Article Type: Research Article

Abstract: This paper deals with MATLAB/SIMULINK simulation and analysis of a position sensor-less field oriented control of permanent magnet synchronous motor. Adaptive position estimators are required as the parameters of the machines like rotor resistance, inductance changes sometimes. Adaptive position and speed estimators viz. SMO, MRAS are much discussed in literature but the artificial neural network, adaptive neuro-fuzzy inference based estimators are least discussed. In this paper a MATLAB study of MRAS, ANN and ANFIS based position estimator in a Field oriented control of a permanent magnet synchronous motor drive is being done. MRAS, ANN, ANFIS estimators adaptive in nature so …these estimators can adapt if there is any parameters change online. The performances of these three drives are analyzed, and results are compared. It is seen that ANFIS based system performance is better even when the parameters of the machines vary with time. This work is limited to analysis and simulation only and could be extended to a practical realization in future work. Show more

Keywords: Field oriented control (FOC) drive, permanent magnet synchronous machine (PMSM), artificial neural network (ANN), model reference adaptive system (MRAS), ANFIS

DOI: 10.3233/JIFS-169801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 5, pp. 5177-5184, 2018