297 results on '"Chaudhary, Gopal"'
Search Results
52. RETRACTED ARTICLE: On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems
- Author
-
Srivastava, Vishal, primary, Srivastava, Smriti, additional, Chaudhary, Gopal, additional, Guzmán-Guzmán, Xiomarah, additional, and García-Díaz, Vicente, additional
- Published
- 2021
- Full Text
- View/download PDF
53. Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection
- Author
-
Gupta, Meenu, primary, Wu, Hao, additional, Arora, Simrann, additional, Gupta, Akash, additional, Chaudhary, Gopal, additional, and Hua, Qiaozhi, additional
- Published
- 2021
- Full Text
- View/download PDF
54. Numerical solution of optimal control problem
- Author
-
Agashe, S. D., primary, Lande, B. K., additional, Jain, Vanita, additional, Chaudhary, Gopal, additional, and Al-turjman, Fadi, additional
- Published
- 2021
- Full Text
- View/download PDF
55. Comparative Study of Evolutionary Computation Based PI, FOPI and NN Controllers for DSTATCOM
- Author
-
Srivastava, Smriti, primary, Wu, Qiming, additional, Gupta, Monika, additional, Chaudhary, Gopal, additional, Hua, Qiaozhi, additional, and Li, Jianbin, additional
- Published
- 2021
- Full Text
- View/download PDF
56. On comparing the performance of swarm-based algorithms with human-based algorithm for nonlinear systems
- Author
-
Srivastava, Vishal, Srivastava, Smriti, Chaudhary, Gopal, Guzmán Guzmán, Xiomarah María, and García Díaz, Vicente
- Subjects
GeneralLiterature_INTRODUCTORYANDSURVEY - Abstract
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
- Published
- 2021
57. Multimodal Human Eye Blink Recognition Using Z-score Based Thresholding and Weighted Features.
- Author
-
Lamba, Puneet Singh, Virmani, Deepali, Pillai, Manu S., and Chaudhary, Gopal
- Subjects
BLINKING (Physiology) ,RECEIVER operating characteristic curves ,EYELIDS - Abstract
A novel real-time multimodal eye blink detection method using an amalgam of five unique weighted features extracted from the circle boundary formed from the eye landmarks is proposed. The five features, namely (Vertical Head Positioning, Orientation Factor, Proportional Ratio, Area of Intersection, and Upper Eyelid Radius), provide imperative gen (z score threshold) accurately predicting the eye status and thus the blinking status. An accurate and precise algorithm employing the five weighted features is proposed to predict eye status (open/close). One state-of-the-art dataset ZJU (eye-blink), is used to measure the performance of the method. Precision, recall, F1-score, and ROC curve measure the proposed method performance qualitatively and quantitatively. Increased accuracy (of around 97.2%) and precision (97.4%) are obtained compared to other existing unimodal approaches. The efficiency of the proposed method is shown to outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
58. Key Player Algorithm-Particle Swarm Optimization (KPA-PSO) for Efficient Relay Nodes Positioning in WSNs
- Author
-
Jain, Aarti, primary, Vaiyapuri, Thavavel, additional, and Chaudhary, Gopal, additional
- Published
- 2021
- Full Text
- View/download PDF
59. An Enhanced Texture-Based Feature Extraction Approach for Classification of Biomedical Images of CT-Scan of Lungs
- Author
-
Srivastava, Varun, primary, Gupta, Shilpa, additional, Chaudhary, Gopal, additional, Balodi, Arun, additional, Khari, Manju, additional, and García-Díaz, Vicente, additional
- Published
- 2021
- Full Text
- View/download PDF
60. AI-enabled COVID-9 Outbreak Analysis and Prediction: Indian States vs. Union Territories
- Author
-
Gupta, Meenu, primary, Jain, Rachna, additional, Arora, Simrann, additional, Gupta, Akash, additional, Javed Awan, Mazhar, additional, Chaudhary, Gopal, additional, and Nobanee, Haitham, additional
- Published
- 2021
- Full Text
- View/download PDF
61. Multimodal Human Eye Blink Recognition Using Z-score Based Thresholding and Weighted Features
- Author
-
Singh Lamba, Puneet, primary, Virmani, Deepali, additional, Pillai, Manu S., additional, and Chaudhary, Gopal, additional
- Published
- 2021
- Full Text
- View/download PDF
62. AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories
- Author
-
Gupta, Meenu, primary, Jain, Rachna, additional, Arora, Simrann, additional, Gupta, Akash, additional, Javed Awan, Mazhar, additional, Chaudhary, Gopal, additional, and Nobanee, Haitham, additional
- Published
- 2021
- Full Text
- View/download PDF
63. Voltage Regulation using Probabilistic and Fuzzy Controlled Dynamic Voltage Restorer for Oil and Gas Industry
- Author
-
Gupta, Monika, primary, Srivastava, Smriti, additional, Chaudhary, Gopal, additional, Khari, Manju, additional, and Fuente, Javier Parra, additional
- Published
- 2020
- Full Text
- View/download PDF
64. A systematic approach for COVID-19 predictions and parameter estimation
- Author
-
Srivastava, Vishal, primary, Srivastava, Smriti, additional, Chaudhary, Gopal, additional, and Al-Turjman, Fadi, additional
- Published
- 2020
- Full Text
- View/download PDF
65. An intelligent framework for detection of fatigue induced by sleep-deprivation.
- Author
-
Virk, Jitender Singh, Singh, Mandeep, Panjwani, Usha, Ray, Koshik, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
AIR traffic controllers ,FEATURE extraction ,SLEEP deprivation ,MENTAL fatigue ,WAKEFULNESS - Abstract
Most of the people who do not take required sleep are prone to sleep-deprived mental fatigue. This mental fatigue due to sleep deprivation is very harmful to persons involved in critical jobs like Pilots, Surgeons, Air traffic controllers and others. The present research paper proposes an intelligent method based on re-enforced learning, followed by classification supported by the adaptive threshold. Moreover, the method proposed by us is non-intrusive, in which the subject is unaware of being monitored during the test; it helps prevent biased results. The novelty lies in the use of the Inter-frame interval of an open and close eye for feature extraction that leads to the detection of "Alertness" or "Fatigue" based on the adaptive threshold. The proposed self-learning framework is real-time in nature and has a detection accuracy of 97.5 %. Since the method is self-learning, as the size of the data set increases, its accuracy and sensitivity are likely to increase further. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
66. Distracted driver detection using compressed energy efficient convolutional neural network.
- Author
-
Alzubi, Jafar A., Jain, Rachna, Alzubi, Omar, Thareja, Anuj, Upadhyay, Yash, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
CONVOLUTIONAL neural networks ,ELECTRONIC equipment ,TRAFFIC accidents - Abstract
The availability of techniques for driver distraction detection has been difficult to put to use because of delays caused due to lag in inferencing the model. Distractions caused due to handheld devices have been major causes of traffic accidents as they affect the decision-making capabilities of the driver and gives them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents, which can easily be avoided if they had been attentive. As such, problems related to the driver's negligence towards safety a possible solution is to monitor the driver and driving behavior and alerting them if they are distracted. In this paper, we propose a novel approach for detecting when a driver is distracted due to in hand electronic devices which is not only able to detect the distraction with high accuracy but also is energy and memory efficient. Our proposed compressed neural got an accuracy of 0.83 in comparison to 0.86 of heavyweight network. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
67. Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting.
- Author
-
Fatema, Nuzhat, Malik, Hasmat, Abd Halim, Mutia Sobihah, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
MEDICAL forecasting ,MEDICAL tourism ,HILBERT-Huang transform ,MONTE Carlo method ,BOX-Jenkins forecasting - Abstract
This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach is firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results show that the proposed hybrid forecasting approach for medical tourism has outperforming characteristics. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
68. Cyberattacks identification in IEC 61850 based substation using proximal support vector machine.
- Author
-
Malik, Hasmat, Alotaibi, Majed A., Almutairi, Abdulaziz, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
SUPPORT vector machines ,CYBERTERRORISM ,LOGIC circuits ,COMPUTER hacking ,IDENTIFICATION ,GEESE - Abstract
Maintaining the reliable, efficient, secure and multifunctional IEC 61850 based substation is an extremely challenging task, especially in the ever-evolving cyberattacks domain. This challenge is also exacerbated with expending the modern power system (MPS) to meet the demand along with growing availability of hacking tools in the hacker community. Few of the most serious threats in the substation automation system (SAS) are DoS (Denial of Services), MS (Message Suppression) and DM (Data Manipulation) attacks, where DoS is due to flood bogus frames. In MS, hacker inject the GOOSE sequence (sqNum) and GOOSE status (stNum) number. In the DM attacks, attacker modify current measurements reported by the merging units, inject modified boolean value of circuit breaker and replay a previously valid message. In this paper, an intelligent cyberattacks identification approach in IEC 61850 based SAS using PSVM (proximal support vector machine) is proposed. The performance of the proposed approach is demonstrated using experimental dataset of recorded signatures. The obtained results of the demonstrated study shows the effectiveness and high level of acceptability for real side implementation to protect the SAS from the cyberattacks in different scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
69. Investigation on effect of solar energy generation on electricity price forecasting.
- Author
-
Kumar, Neeraj, Tripathi, M.M., Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
ELECTRICITY pricing ,ELECTRIC power production ,RENEWABLE energy sources ,SOLAR energy ,WIND power ,FORECASTING ,ELECTRIC power consumption - Abstract
Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power increasing immensely across the globe. Solar energy is widely expanding in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for accurate electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. In this paper, investigation has been done on the effect of solar energy generation on electricity price forecasting. Different state of the art Machine learning (ML) models have been applied and compared with LSTM model for electricity price forecasting and the evaluation of the impact of solar energy generation on electricity price has been done. During the investigation it was found from the results that the LSTM model outperform all other models and impact of solar energy generation on electricity price is evaluated using forecasting metrics. The forecasted electricity price considering the factor of solar energy generation was lower as compared with the forecast without solar energy generation. The reliability test of the MAPE values has been performed by calculating confidence interval for proposed model. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
70. Distributed probability density based multi-objective routing for Opp-IoT networks enabled by machine learning.
- Author
-
Ajith Kumar, S.P., Banyal, Siddhant, Bhardwaj, Kartik Krishna, Thakur, Hardeo Kumar, Sharma, Deepak Kumar, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
MACHINE learning ,AD hoc computer networks ,KEY performance indicators (Management) ,PROBABILITY theory ,DIGITAL technology ,DENSITY ,DELAY-tolerant networks - Abstract
Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
71. Deep learning and signal processing based algorithm for autorecognition of harmonic loads.
- Author
-
Srikanth, Pullabhatla, Koley, Chiranjib, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
DEEP learning ,SIGNAL processing ,ARTIFICIAL neural networks ,INTERCONNECTED power systems ,CONVOLUTIONAL neural networks ,MACHINE learning - Abstract
A convolution neural network (CNN) based deep learning method has been proposed for automatic classification and localization of nonlinear loads present in an interconnected power system. The identification of nonlinear loads has been previously dealt with the use of Nonlinear Auto Regression neural network with eXogenous inputs (NARX), Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and Fuzzy Logic (FL). However, these techniques had not explored the area of classification of industrial and domestic nonlinear loads in an interconnected power system. Also, a Deep learning-based solution for identification of the type of nonlinear load has not been reported in the literature to date. Hence, to address these shortcomings, an IEEE-9 Bus system with industrial nonlinear loads has been used to obtain various current waveforms with distortions. The recorded current waveforms are transformed into a time-frequency (TF) domain plane, and the obtained images are then fed to the deep learning algorithm. The colored images of the TF plots of each type of nonlinear load in Red-Green-Blue (RGB) index provide the best visual features for extraction. The TF domain signatures of individual events are scaled to a standard size before feeding to the algorithm. Through these TF signatures, unique features were extracted with the deep learning algorithm, and then passed on to different stages of convolution and max-pooling with fully connected layers. The softmax classifier at the end classifies the input data into the type of nonlinear present in the power system. The algorithm, when run at different buses, also identifies the location of the nonlinear load. The proposed methodology avoids the usage of any additional fusion layer for obtaining unique features, reduces the training time and maintains the highest accuracy of 100%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
72. Data driven intelligent model for quality management in healthcare.
- Author
-
Fatema, Nuzhat, Malik, Hasmat, Ahmad, Wakeel, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
TOTAL quality management ,MEDICAL care ,HEPATIC fibrosis ,MEDICAL personnel ,HILBERT-Huang transform ,QUALITY of service ,MEDICAL quality control - Abstract
It is the need of today's world, to deliver with quality health care services to meet the health needs of target populations. The healthcare system includes procedures of prevention and screening of all types of diseases, their treatment and diagnostics, recent research and development. These procedures must be maintained at a desired level of excellence, which comes under quality management. Quality management in healthcare incorporates with making of various quality policies, quality planning and assurance, quality control and quality improvement. Quality improvement (QI) is the scheme used for betterment of the services delivered to the patients, such as diagnosis and treatment. If these schemes are recent and advanced technology based, services provided would be cost effective, accurate, less time consuming and hassle-free for both healthcare provider as well as patients. In this study we are applying artificial intelligent and machine learning techniques to enhance the diagnosis accuracy of the liver fibrosis which is caused by hepatitis C virus (HCV). Generally, the SLBs (serial liver biopsies) are utilized to diagnose the liver fibrosis levels (LFLs), which is the gold standard method in this domain. However, SLB has various impediment and not appropriate to the patients which leads to higher prognosis cost with invasive way. So, there is a big research gap in the medical field to find out the alternative non-invasive approach/method for SLB. The proposed data-driven intelligent model for identification of liver fibrosis using hybrid approach is designed and implemented to overcome the SLBs problems with higher diagnostic accuracy. The empirical mode decomposition (EMD) approach is used to extract the IMFs (intrinsic mode functions), which are used as input features to the ANN-J48 algorithm based intelligent classifiers. The proposed approach shows the evidence for utilization in a non-invasive way to diagnose the LFLs without high level clinical expert skills. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
73. Grid interfaced solar-wind hybrid power generating systems using fuzzy-based TOGI control technique for power quality improvement.
- Author
-
Prasad, Dinanath, Kumar, Narendra, Sharma, Rakhi, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
HYBRID power systems ,MAXIMUM power point trackers ,PERMANENT magnet generators ,REACTIVE power ,SYNCHRONOUS generators ,DC-to-DC converters ,RENEWABLE energy sources ,FUZZY logic - Abstract
This paper bestows 3-phase grid interfaced solar-wind hybrid renewable energy system (RES), feeding three-phase loads. The proposed system includes solar photovoltaic, permanent magnet based synchronous generator (PMSG), DC-DC converter, maximum power point tracker (MPPT) based on incremental conductance, three phases IGBT based voltage source converter (VSC), with a third order generalized integrator (TOGI) control technique. This control technique bestows multifunctional capabilities as harmonic mitigations, load balancing, and reactive power compensation. A fundamental component of load current is extracted by TOGI based controller, and further it is utilized to provide switching pulses to VSC for power quality enrichment. The fuzzy logic-based controller is used for loss computation of VSC as well as for maintaining DC link voltage. Moreover, fuzzy logic provides better dynamic performance compared to conventional PI controller. The results are presented in many aspects for linear and nonlinear loads such as, intermittent nature of solar and wind as well as disturbances in the system. A comparative analysis between proposed TOGI based controller and conventional control algorithm has been presented. Test results are performed by using MATLAB/ Simulink environment and demonstrate, AC-grid current is maintained within the IEEE-519 standard. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
74. A new hybrid model combining EMD and neural network for multi-step ahead load forecasting.
- Author
-
Malik, Hasmat, Alotaibi, Majed A., Almutairi, Abdulaziz, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
LOAD management (Electric power) ,HILBERT-Huang transform ,LOAD forecasting (Electric power systems) ,FORECASTING ,ENERGY storage ,POWER resources - Abstract
The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error). [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
75. Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network.
- Author
-
Sharma, Sachin, Kumar, Vineet, Rana, K.P.S., Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
PRONY analysis ,PREDICATE calculus ,KALMAN filtering ,FREQUENCIES of oscillating systems ,HILBERT-Huang transform ,OSCILLATIONS ,IMPULSE response - Abstract
Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
76. Designing of a rigorous image retrieval system with amalgamation of artificial intelligent techniques and relevance feedback.
- Author
-
Dhingra, Shefali, Bansal, Poonam, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
IMAGE retrieval ,IMAGING systems ,AMALGAMATION ,IMAGE databases ,BACK propagation ,SUPPORT vector machines - Abstract
Retrieving out the most comparable images from huge databases is the challenging task for image retrieval systems. So, there is a great need of constructing a capable and rigorous image retrieval system. In this implementation, an exclusive and competent Content based image retrieval (CBIR) system is schemed by the integration of Color moment (CM) and Local binary pattern (LBP). A hybrid feature vector is created by the combination of these two techniques through the process of normalization. This hybrid feature vector is given as the input to the intelligent classifiers i.e. Support vector machine (SVM) and Cascade forward back propagation neural network (CFBPNN). After that, Relevance feedback (RF) technique is applied so as to get the high level information in order to reduce the semantic gap. So, here two Artificial Intelligent CBIR models are proposed, first one is (Hybrid+SVM+RF) and second is (Hybrid+CFBPNN+RF) and their performance parameters are compared. The implementations are performed on two benchmark dataset Corel-1K and Oxford flower dataset which contains 1000 and 1360 images respectively. Different parameters are figured such as accuracy, precision, average retrieval time, recall etc. The average precision obtained for the first model is 93% with Corel 1K database and 91% with Oxford flower database. And similarly for the second model, it is 97% and 94% respectively which is higher than the first model. This implemented technique is validated on both the datasets and the attained results outperforms with other related s approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
77. Optimal power management strategy by using fuzzy logic controller for BLDC Motor-Driven E-Rickshaw.
- Author
-
Gautam, Abhinav K., Tariq, Mohd, Pandey, Jai Prakash, Verma, Kripa Shankar, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
FUZZY logic ,SOLAR energy ,ENERGY storage ,POWER resources ,POLITICAL succession - Abstract
In this paper, the authors have addressed the modeling and design of the BLDC Motor-Driven E-Rickshaw based on hybrid energy storage system (HESS) for optimum power management using fuzzy logic. In Hybrid energy sources, solar power is used to charge a battery (primary source) that is effectively coupled to supercapacitor (ancillary source) for peak demand supplies. A power-split control strategy is proposed to control the power supply by using the HESS Fuzzy Logic in different engine operating modes. Projected power layering improves the battery life cycle with the proper use of the Supercapacitor. By providing a new switching algorithm, the DC link voltage is boosted to effectively transfer power to the HESS unit. Fuzzy logic-based HESS provides better performance in electric vehicles, such as deep discharge protection of the battery, and faster acceleration. Also, there is a quick comparison of E-rickshaw solar power with traditional E-rickshaw. The planned design model was simulated by MATLAB
® /Simulink environment. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
78. Improved ANFIS based MRAC observer for sensorless control of PMSM.
- Author
-
Suryakant, Sreejeth, Mini, Singh, Madhusudan, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
POSITION sensors ,PERMANENT magnet motors ,TORQUE control ,ADAPTIVE control systems - Abstract
Detection of the rotor position is an important prerequisite for controlling the speed and developed torque in permanent magnet synchronous motor (PMSM). Even though use of incremental encoder and resolver is one of the popular schemes for sensing the rotor position in a PMSM drive, it increases the size and weight of the drive and reduces its reliability. Dynamic modeling of the motor and control algorithms are often used in sensor-less control of PMSM to estimate rotor position and motor speed. Most sensor-less control algorithms use machine parameters like torque constant, stator inductances and stator resistance for estimating the rotor position and speed. However, with accuracy of such estimation and the performance of the motor degrades with variation in motor parameters. Model reference adaptive control (MRAC) provides a simple solution to this issue. An improved Adaptive neuro-fuzzy inference system (ANFIS) based MRAC observer for speed control of PMSM drive is presented in this paper. In the proposed method adaptive model and adaptive mechanism are replaced by an improved ANFIS controller, which neutralize the effect of parametric variation and results in improved performance of the drive. The modeling equations of PMSM are used to estimate the rotor position for speed and torque control of the drive. Simulation studies have been carried out under various operating condition using MATLAB/Simulink. In addition, a comparative analysis of the conventional MRAC based observer and improved ANFIS based MRAC observer is carried out. It is observed that the proposed method results in better performance of the PMSM drive. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
79. Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear.
- Author
-
Kumar, Indrajeet, Bhatt, Chandradeep, Vimal, Vrince, Qamar, Shamimul, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
CONVOLUTIONAL neural networks ,IMAGE segmentation ,LEVEL set methods ,CELL nuclei - Abstract
The white corpuscles nucleus segmentation from microscopic blood images is major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists to identify the diseases and take appropriate decision for better treatment. Therefore, fully automated white corpuscles nucleus segmentation model using deep convolution neural network, is proposed in the present study. The proposed model uses the combination of 'binary_cross_entropy' and 'adam' for maintaining learning rate in each network weight. To validate the potential and capability of the above proposed solution, ALL-IDB2 dataset is used. The complete set of images is partitioned into training and testing set and tedious experimentations have been performed. The best performing model is selected and the obtained training and testing accuracy of best performing model is reported as 98.69 % and 99.02 %, respectively. The staging analysis of proposed model is evaluated using sensitivity, specificity, Jaccard index, dice coefficient, accuracy and structure similarity index. The capability of proposed model is compared with performance of the region-based contour and fuzzy-based level-set method for same set of images and concluded that proposed model method is more accurate and effective for clinical purpose. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
80. Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM).
- Author
-
Singh, Saumya, Srivastava, Smriti, Malik, Hasmat, and Chaudhary, Gopal
- Subjects
MATHEMATICAL optimization ,FUZZY algorithms ,KERNEL functions ,GENETIC algorithms ,CLUSTER analysis (Statistics) ,DATA analysis - Abstract
In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM). [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
81. Wind integrated power system to reduce emission: An application of Bat algorithm.
- Author
-
Venkateswara Rao, B., Devarapalli, Ramesh, Malik, Hasmat, Bali, Sravana Kumar, García Márquez, Fausto Pedro, Chiranjeevi, Tirumalasetty, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
STATIC VAR compensators ,WIND power plants ,ELECTRICAL load ,ELECTRICITY pricing ,GENERATION gap ,ALGORITHMS ,WIND power ,WASTE minimization - Abstract
The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired Bat Algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
82. Classification of arrhythmia's ECG signal using cascade transparent classifier.
- Author
-
Setiawan, Noor Akhmad, Nugroho, Hanung Adi, Persada, Anugerah Galang, Yuwono, Tito, Prasojo, Ipin, Rahmadi, Ridho, Wijaya, Adi, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
ARRHYTHMIA ,ELECTROCARDIOGRAPHY ,FEATURE extraction ,MACHINE learning ,DECISION trees ,CARDIAC patients - Abstract
Arrhythmia is an abnormality often encountered in patients with cardiac problems. The presence of arrhythmia can be detected by an electrocardiogram (ECG) test. Automatic observation based on machine learning has been developed for long time. Unfortunately, only few of them have capability of explaining the knowledge inside themselves. Thus, transparency is important to improve human understanding of knowledge. To achieve this goal, a method based on cascaded transparent classifier is proposed. Firstly, ECG signals were separated and every single signal was extracted using feature extraction method. Several of extracted feature's attributes were selected, and the final step was classifying data using cascade classifier which consists of decision tree and the rule based classifier. Classification performance was evaluated with publicly available dataset, the MIT-BIH Physionet Dataset. The methods were tested using 10-fold cross validation. The average of both accuracy and number of rules generated was considered. The best result using rule-based classifier achieves the accuracy and the number of rules 92.40% and 40, respectively. And the best result using cascade classifier achieves the accuracy and the number of rules 92.84% and 80, respectively. As a conclusion, transparent classifier shows a competitive performance with reasonable accuracy compared with previous research and promising in addressing the need for interpretability model. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
83. A fuzzy rule based control algorithm for MPPT to drive the brushless dc motor based water pump.
- Author
-
Hasan, Mashhood, Alhazmi, Waleed Hassan, Zakri, Waleed, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
WATER pumps ,BRUSHLESS electric motors ,MAXIMUM power point trackers ,CENTER of mass ,PHOTOVOLTAIC power systems ,INTELLIGENT control systems ,FUZZY logic - Abstract
In this paper, a solar photovoltaic model integrated with brushless DC motor via DC to DC zeta converter is controlled in two stage. In first stage, a fuzzy rule based maximum power point tracking (PPT) is proposed to generate the pulse for DC to DC zeta converter. It is efficient intelligent control approach to extract maximum power from the solar PV system and enhance the speed to track the maximum power. The basic three process of fuzzy logic controller (FLC) are fuzzifier, inference and defuzzifier where the defuzzification process is used center of gravity (COG) method to convert its original value. The FLC to extract maximum PPT for solar PV based brushless DC motor can be examined the performance under transient and dynamic condition with different solar insolation. Moreover, in second stage a trapezoidal control approach based electronic commutation is chosen to generate the pulses of voltage source inverter (VSI) and it offers the smooth control of the brushless DC motor which can easily applicable for water pumping or irrigation purpose. A second stage, trapezoidal control approach is close loop control algorithm using sensorless drive. The performance of proposed fuzzy rule based control algorithm is shown using simulation results on MATLAB platform. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
84. Fuzzified time-frequency method for identification and localization of power system faults.
- Author
-
Srikanth, Pullabhatla, Koley, Chiranjib, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
ELECTRIC lines ,TEST systems - Abstract
In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
85. Blockchain technology based decentralized energy management in multi-microgrids including electric vehicles.
- Author
-
Meghana, Pulimamidi, Yammani, Chandrasekhar, Salkuti, Surender Reddy, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
BLOCKCHAINS ,ENERGY management ,ELECTRIC vehicles ,MICROGRIDS ,CLEAN energy ,ELECTRIC automobiles - Abstract
This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
86. CNN-based Multimodal Touchless Biometric Recognition System using Gait and Speech.
- Author
-
Sarin, Sumit, Mittal, Antriksh, Chugh, Anirudh, Srivastava, Smriti, Malik, Hasmat, and Chaudhary, Gopal
- Subjects
MULTIMODAL user interfaces ,BIOMETRIC identification ,SPEECH ,GAIT in humans ,CONVOLUTIONAL neural networks ,BIOMETRY ,DEEP learning - Abstract
Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
87. An approach to solve OPF problems using a novel hybrid whale and sine cosine optimization algorithm.
- Author
-
Devarapalli, Ramesh, Venkateswara Rao, B., Dey, Bishwajit, Vinod Kumar, K., Malik, H., García Márquez, Fausto Pedro, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
MATHEMATICAL optimization ,COSINE function ,PROBLEM solving ,ELECTRICAL load ,ROBUST optimization ,NUMBER systems ,WHALES - Abstract
Nowadays, improvement in power system performance is essential to obtaine economic and technical benifits. To achieve this, optimize the large number of parameters in the system based on optimal power flow(OPF). For solving OPF problem efficiently, it needs robust and fast optimization techniques. This paper proposes the application of a newly developed hybrid Whale and Sine Cosine optimization algorithm to solve the OPF. It has been implemented for optimization of the control variables. The reduction of true power generation cost, emission, true power losses, and voltage deviation are considered as different objectives. The hybrid Whale and Sine Cosine optimization is validated by solving OPF problem with various intentions using IEEE30 bus system. To varidate the proposed technique, the results obtained from this are compared with other methods in the literature. The robustness achieved with the proposed algorithm has been analyzed for the considered OPF problem using statistical analysis and whisker plots. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
88. A new ensemble based approach for intrusion detection system using voting.
- Author
-
Bhati, Nitesh Singh, Khari, Manju, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
VOTING ,SUPPORT vector machines ,MACHINE learning - Abstract
With the increase in the amount of data available today, the responsibility of keeping that data safe has also taken a more severe form. To prevent confidential data from getting in the hands of an attacker, some measures need to be taken. Here comes the need for an effective system, which can classify the traffic as an attack or normal. Intrusion Detection Systems can do this work with perfection. Many machine learning algorithms are used to develop efficient IDS. These IDS provide remarkable results. However, ensemble-based IDS using voting have been seen to outperform individual approaches (Support Vector Machine and ExtraTree). Since the Voting methodology is able to work around both, theoretically similar and different classifiers and produce a single classifier based on the majority characteristics, it proved to be better than the other ensemble based techniques. In this paper, an ensemble IDS implementation is presented based on the voting ensemble method, using the two algorithms, Support Vector Machine (SVC) and ExtraTree. The experiment is performed on the KDDCup99 Dataset. The evaluation of the performance of the proposed method is based on the comparison with an unoptimized implementation of the same. The results based on performing the experiment in Python fetched an accuracy of 99.90%. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
89. A data-driven intelligent hybrid method for health prognosis of lithium-ion batteries.
- Author
-
Bisht, Vimal Singh, Hasan, Mashhood, Malik, Hasmat, Sunori, Sandeep, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
PARAMETER estimation ,RANK correlation (Statistics) ,LITHIUM-ion batteries - Abstract
For estimation of the RUL (Remaining useful life) of Lithium ion battery we are required to do its health assessment using online facilities. For identifying the health of a battery its internal resistance and storage capacity plays the major role. However the estimation of both these parameters is not an easy job and requires lot of computational work to be done. So to overcome this constraint an easy alternate way is simulated in the paper through which we can estimate the RUL. For formation of a linear relationship between health index of the battery (HI) and its actual capacity used of power transformation method is done and later on to validate the result a comparison study is done with Pearson & Spearman methods. Transformed value of Health Index is used for developing a neural network. The results demonstrated in the paper shows the feasibility of the proposed technique resulting in great saving of time [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
90. Whale optimization based synchronization and control of two identical fractional order financial chaotic systems.
- Author
-
Gupta, Sangeeta, Varshney, Pragya, Srivastava, Smriti, Malik, Hasmat, and Chaudhary, Gopal
- Subjects
PARTICLE swarm optimization ,MATHEMATICAL optimization ,SYNCHRONIZATION ,PID controllers ,CHAOTIC communication - Abstract
This paper proposes a scheme to synchronize fractional order chaotic systems employing fractional PID controller. The parameters of FOPID are tuned using Swarm based optimization techniques, viz.: Whale optimization algorithm and Particle swarm optimization techniques. To assert the complete synchronization, master-slave method has been implemented. Chaotic systems are highly dependent upon initial conditions and parameter perturbations. Therefore, taking these properties into consideration, synchronization of two identical fractional order financial chaotic systems is performed with distinct initial conditions. To show the efficacy of the proposed method, analysis is performed for orders between 0 to 1, and also for sensitivity to initial conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
91. Multimodal semantic analysis with regularized semantic autoencoder.
- Author
-
Malik, Shaily, Bansal, Poonam, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
IMAGE retrieval ,MACHINE learning - Abstract
The real-world data is multimodal and to classify them by machine learning algorithms, features of both modalities must be transformed into common latent space. The high dimensional common space transformation of features lose their locality information and susceptible to noise. This research article has dealt with this issue of a semantic autoencoder and presents a novel algorithm with distinct mapped features with locality preservation into a commonly hidden space. We call it discriminative regularized semantic autoencoder (DRSAE). It maintains the low dimensional features in the manifold to manage the inter and intra-modality of the data. The data has multi labels, and these are transformed into an aware feature space. Conditional Principal label space transformation (CPLST) is used for it. With the two-fold proposed algorithm, we achieve a significant improvement in text retrieval form image query and image retrieval from the text query. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
92. Multimodal biometric system using deep learning based on face and finger vein fusion.
- Author
-
Tyagi, Shikhar, Chawla, Bhavya, Jain, Rupav, Srivastava, Smriti, Malik, Hasmat, and Chaudhary, Gopal
- Subjects
FINGERS ,HUMAN fingerprints ,DEEP learning ,CONVOLUTIONAL neural networks ,BIOMETRIC identification ,VEINS ,BIOMETRY - Abstract
Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
93. Hybrid optimization based PID control of ball and beam system.
- Author
-
Srivastava, Vishal, Srivastava, Smriti, Malik, Hasmat, and Chaudhary, Gopal
- Subjects
AUTOMATIC control systems ,BENCHMARK problems (Computer science) ,MATHEMATICAL optimization ,PARTICLE swarm optimization - Abstract
Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
94. MPPT-based on Bat algorithm for photovoltaic systems working under partial shading conditions.
- Author
-
Asim, Mohammed, Agrawal, Piyush, Tariq, Mohd, Alamri, Basem, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
MAXIMUM power point trackers ,PHOTOVOLTAIC power systems ,ALGORITHMS ,MICROCONTROLLERS ,METAHEURISTIC algorithms ,TYPHOONS - Abstract
Under partial shading conditions (PSC), most traditional maximum power point tracking (MPPT) techniques may not adopt GP (global peak). These strategies also often take a considerable amount of time to reach a full power point (MPP). Such obstacles can be eliminated by the use of metaheuristic strategies. This paper shows, in partial shading conditions, the MPPT technique for the photovoltaic system using the Bat Algorithm (BA). Simulations have been performed in the MATLAB
® /Simulink setting to verify the efficacy of the proposed method. In MPPT applications, the results of the simulations emphasize the precision of the proposed technique. The algorithm is also simple and efficient, on a low-cost microcontroller, it could be implemented. Hardware in Loop (HIL) validation is performed, with a Typhoon HIL 402 setup. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
95. Solar energy forecasting using intelligent techniques: A step towards sustainable power generating system.
- Author
-
Alsaidan, Ibrahim, Rizwan, Mohammad, Alaraj, Muhannad, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
RENEWABLE energy sources ,ENERGY consumption ,SOLAR power plants ,EMISSIONS (Air pollution) ,POWER resources ,SOLAR energy ,SMART power grids - Abstract
The rapid advancements in the technology, increase in comfort levels, movement of population to urban areas, depletion of fossil fuels and increasing greenhouse gas emissions have invigorated the use of renewable energy resources for power generation in the last few years. The major renewable energy resources which have potential to fulfill the requirements includes solar energy, wind energy, small hydro and biomass etc. Among these major resources, solar energy-based technology is considered as one of the fastest growing technology because of its various advantages and ubiquitous availability of the resources. However, there are certain challenges in the utilization of solar energy for power generation because of various uncertainties in the atmosphere. As a result, the power generated from solar based power plants is fluctuating in nature which is not desirable. Therefore, the utilities are adopting the smart grid approach which has ability to integrate the solar power plants efficiently and the solar energy forecasting is one of the essential tools for this new model. In this paper, AI based techniques are utilized to forecast solar energy using high quality measured solar irradiance data. The forecasting accuracy of the developed models is evaluated based on statistical indices such as absolute relative error and mean absolute percentage error. The results obtained from the developed models are compared to observe the forecasting ability and performance with the high-quality measured data and found accurate. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
96. A deep learning- based frechet and dirichlet model for intrusion detection in IWSN.
- Author
-
Alzubi, Omar A., Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
INTRUSION detection systems (Computer security) ,WIRELESS sensor networks ,DATA transmission systems ,DATA security ,FEATURE extraction ,COMMUNICATION models - Abstract
Industrial Wireless Sensor Network (IWSN) includes numerous sensor nodes that collect data about target objects and transmit to sink nodes (SN). During data transmission among nodes, intrusion detection is carried to improve data security and privacy. Intrusion detection system (IDS) examines the network for intrusions based on user activities. Several works have been done in the field of intrusion detection and different measures are carried out to increase data security from the issues related to black hole, Sybil attack, Worm hole, identity replication attack and etc. In various existing approaches, secure data transmission is not achieved, therefore resulted in compromising the security and privacy of IWSNs. Accurate intrusion detection is still challenging task in terms of improving security and intrusion detection rate. In order to improve intrusion detection rate (IDR) with minimum time, generalized Frechet Hyperbolic Deep and Dirichlet Secured (FHD-DS) data communication model is introduced. At first, Frechet Hyperbolic Deep Traffic (FHDT) feature extraction method is designed to extract more relevant network activities and inherent traffic features. With the help of extracted features, anomalous or normal data is predicted. Followed by Statistical Dirichlet Anomaly-based Intrusion Detection model is applied to discover intrusion. Here, Dirichlet distribution is evaluated to attain secure data transmission and significantly detect intrusions in WSNs. Experimental evaluation is carried out with KDD cup 99 dataset on factors such as IDR, intrusion detection time (IDT) and data delivery rate (DDR). The observed results show that the generalized FHD-DS data communication method achieves higher IDR with minimum time. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
97. Principal component analysis technique for early fault detection.
- Author
-
Sarita, Kumari, Devarapalli, Ramesh, Kumar, Sanjeev, Malik, H., García Márquez, Fausto Pedro, Rai, Pankaj, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
FAST Fourier transforms ,PRINCIPAL components analysis ,PLANT maintenance ,DETECTION alarms ,INDUSTRIAL equipment - Abstract
Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
98. Development of wide area monitoring system for smart grid application.
- Author
-
Malik, Hasmat, Ahmad, Md Waseem, Alotaibi, Majed A., Almutairi, Abdulaziz, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
GRIDS (Cartography) ,ELECTRIC potential measurement ,VOLTAGE ,MACHINE-to-machine communications - Abstract
PMU can directly measure positive sequence voltage, phase and system frequency. In this paper, the design and implementation for optimum placement of PMU in power system network (PSN) has been performed using 5 different intelligent approaches at an emulation platform. Different case studies based on IEEE 7, 14 and 30 bus system have been performed and analyzed. In the studies, PMU device is used for the measurement of voltage and current magnitude as well as its phase and its performance has been compared with measured real signals of PSN. PMU measurement gives the accurate results and reliability to PSN. But PMUs are not economical, so PSN operator needs to install a minimum number of PMU in PSN so that system should be fully observable in a real-time scenario. In this paper for optimal placement of PMU, five different intelligent methods have been analyzed for three different bus systems and obtained results are compared. For the further validation of selected PMUs for the PSN, a state estimation using WLS algorithm has been performed using conventional data and PMU data on IEEE14 and IEEE30 bus systems. The obtained results for voltage estimation error and phase estimation error with and without PMU data are compared. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
99. Power quality enhancement of a hybrid energy source powered packed e-cell inverter using an intelligent optimization technique.
- Author
-
Riyaz, Ahmed, Sadhu, Pradip Kumar, Iqbal, Atif, Tariq, Mohd, Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
MATHEMATICAL optimization ,HYBRID power systems ,RENEWABLE energy sources ,PULSE width modulation ,ENERGY consumption ,WIND power - Abstract
The most installed Renewable Energy Sources (RES) in micro-grids (MG) are Photovoltaic (PV) power and wind power. Due to the intermittent behaviour of renewable sources, parallel operation of RES and battery storage known as hybrid system is important particularly in remote micro-grids to reduce the fuel consumption by diesel generators and continuity of supply to the load. In this paper, multilevel inverter called Packed E-Cell (PEC) is used for parallel operation of RES and battery storage optimally for micro-grid applications. The PEC requires less components compared to other Multi-level inverters (MLI) topology with relatively low total harmonic distortion (THD). Further, selective harmonic technique based on optimization principle is used to enhance the harmonic profile using low frequency switching technique. The 3rd and 5th harmonics are eliminated using Genetic Algorithm (GA) optimization technique. The simulation-based analysis is done using Simulink/MATLAB and the results obtained for THD in the output current and voltage are presented and discussed in the paper. A comparative analysis is also presented with high frequency modulation technique phase disposition pulse width modulation (PDPWM) technique. The experimental validation of the proposed scheme is done using Typhoon HIL (hardware in loop). [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
100. Reactive power compensation for grid by Packed-U-Cell inverter using model predictive control strategy with intelligent multi-objective scheme.
- Author
-
Anees, Mohd. Anas, Tariq, Mohd, Lodi, Kaif Ahmed, Alam, Mahetab, Chakrabortty, Ripon K., Ryan, Michael J., Malik, Hasmat, Chaudhary, Gopal, and Srivastava, Smriti
- Subjects
ELECTRIC power distribution grids ,INTELLIGENT control systems ,PREDICTION models ,ELECTRIC inverters ,REACTIVE power ,MICROGRIDS ,CAPACITOR switching ,TYPHOONS ,LOW voltage systems - Abstract
This paper proposes a model predictive control strategy for 15 level Packed-U-Cell inverter that satisfies multiple-objectives of low current total harmonic distortion (THD), capacitor voltage balances, supply of desired active and reactive power, as well as lower switching and lower voltage stresses on the switching devices. The proposed device performs well under dynamic conditions and can successfully track the current command during step changes in the power demand. A detailed modeling is presented and discussed. MATLAB/Simulink is used for obtaining the simulation results, and the results are validated in the real time by using a hardware-in-the-loop (HIL) Typhoon 402 real-time emulator. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.