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RISS 인기검색어
- 검색키워드
- 저자 : Arun Kumar U. (검색결과 4 건)
- 무료
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Fault diagnosis of rolling element bearing based on artificial neural network
Rohit S. Gunerkar,Arun Kumar Jalan,Sachin U Belgamwar 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.2
This paper proposes the expert system for accurate fault detection of bearing. The study is based upon advanced signal processing method as wavelet transform and artificial intelligence technique as artificial neural network (ANN) and K-nearest neighbor (KNN), for fault classification of bearing. An adaptive algorithm based on wavelet transform is used to extract the fault classifying features of the bearing from time domain signal. These features have been used as inputs to proposed ANN models and the same features have also been used for KNN. Dedicated experimental setup was used to perform the test upon the bearing. Single data set for four fault conditions of bearing is collected to train ANN and KNN. The processed and normalized data was trained by using backpropagation multilayer perceptron neural network. The results obtained from ANN are compared with KNN, ANN results proved to be highly effective for classification of multiple faults.
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Solar PV and Wind Energy Based Reconfigurable Microgrid for Optimal Load Dispatch
Varghese Lijo Jacob,Arun Kumar U.,Sunitha D. 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4
Distribution generation (DG), energy storage systems (ESS), distributed reactive sources (DRS), and resilient loads that may operate in both linked and isolated modes form the microgrid (MG). Unpredictable and variable DGs, like as renewable energy sources like wind and photovoltaic systems, are especially difficult for MG planners to make judgments on (PVES). This work provides a rigorous hybrid optimization approach for designing reconfigurable MGs to tackle technological and economic uncertainty. The suggested technique leverages the moth flame optimization (MFO) algorithm paired with a heuristic fuzzy for optimum DG positioning and reconfiguration, manufacturing costs, and loss avoidance. This hybrid algorithm improves electricity quality, increases customer savings, and benefits the distributed system operators (DSO). The MFO algorithm is used to optimize and reorganize DG sites, and the fuzzy technique is utilized to deal with multi-objective problems, all to reduce microgrid expenses like emission costs and the supply of reliable energy. Using the supplied technique to address three distinct situations helps DSO choose the appropriate structures. For testing, a 33-bus IEEE RDS microgrid is employed. Validation is done utilizing a 24-h daily load pattern and 24-h typical load dispatching behavior for both WES and PVES to ensure reliability. Studies show micro grids outperform present structures.
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Sajjad, Muhammad,Nasir, Mansoor,Ullah, Fath U Min,Muhammad, Khan,Sangaiah, Arun Kumar,Baik, Sung Wook Elsevier science 2019 Information sciences Vol.479 No.-
<P><B>Abstract</B></P> <P>Facial expression recognition is an active research area for which the research community has presented a number of approaches due to its diverse applicability in different real-world situations such as real-time suspicious activity recognition for smart security, monitoring, marketing, and group sentiment analysis. However, developing a robust application with high accuracy is still a challenging task mainly due to the inherent problems related to human emotions, lack of sufficient data, and computational complexity. In this paper, we propose a novel, cost-effective, and energy-efficient framework designed for suspicious activity recognition based on facial expression analysis for smart security in law-enforcement services. The Raspberry Pi camera captures the video stream and detects faces using the Viola Jones algorithm. The face region is pre-processed using Gabor filter and median filter prior to feature extraction. Oriented FAST and Rotated BRIEF (ORB) features are then extracted and the support vector machine (SVM) classifier is trained, which predicts the known emotions (Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise). Based on the collective emotions of the faces, we predict the sentiment behind the scene. Using this approach, we predict if a certain situation is hostile and can prevent it prior to its occurrence. The system is tested on three publically available datasets: Cohen Kande (CK+), MMI, and JAFEE. A detailed comparative analysis based on SURF, SIFT, and ORB is also presented. Experimental results verify the efficiency and effectiveness of the proposed system in accurate recognition of suspicious activity compared to state-of-the-art methods and validate its superiority for enhancing security in law enforcement services.</P>
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