Institution
Rajasthan Technical University
Education•Kota, Rajasthan, India•
About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.
Papers
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TL;DR: The proposed design analysis of parallel robots together with the multiobjective optimization and subsequent fuzzy-based ranking can be generalized with modest efforts for the development of all of the classes of parallel Robots.
Abstract: This paper describes the design analysis and optimization of a novel 3-degrees of freedom (DOF) wearable parallel robot developed for ankle rehabilitation treatments. To address the challenges arising from the use of a parallel mechanism, flexible actuators, and the constraints imposed by the ankle rehabilitation treatment, a complete robot design analysis is performed. Three design stages of the robot, namely, kinematic design, actuation design, and structural design are identified and investigated, and, in the process, six important performance objectives are identified which are vital to achieve design goals. Initially, the optimization is performed by considering only a single objective. Further analysis revealed that some of these objectives are conflicting, and hence these are required to be simultaneously optimized. To investigate a further improvement in the optimal values of design objectives, a preference-based approach and evolutionary-algorithm-based nondominated sorting algorithm (NSGA II) are adapted to the present design optimization problem. Results from NSGA II are compared with the results obtained from the single objective optimization and preference-based optimization approaches. It is found that NSGA II is able to provide better design solutions and is adequate to optimize all of the objective functions concurrently. Finally, a fuzzy-based ranking method has been devised and implemented in order to select the final design solution from the set of nondominated solutions obtained through NSGA II. The proposed design analysis of parallel robots together with the multiobjective optimization and subsequent fuzzy-based ranking can be generalized with modest efforts for the development of all of the classes of parallel robots.
58 citations
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TL;DR: In the onlooker bee phase of ABC, to maintain a proper harmony amid exploration and exploitation capabilities, beer froth phenomenon inspired position update is incorporated and the proposed algorithm is named as Beer froth artificial bee colony algorithm (BeFABC).
56 citations
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TL;DR: In this paper, the effect of temperature, thermal and flow parameters of air, geometrical and material properties of pipe, different modes of operation on the performance of earth air tunnel heat exchanger systems have been critically reviewed.
54 citations
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TL;DR: In this paper, the perovskite solar cell (PSC) model with novel inverted architecture Glass / FTO / NiO / FASnI3/C60/Au using device simulation tool.
54 citations
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TL;DR: In this article, a new binary Salp Swarm Algorithm (bSSA) was proposed for selecting the best feature set from transformed datasets, which first transforms the original data-set using Principal Component Analysis (PCA) and fast independent component analysis (fastICA) based hybrid data transformation methods; next, a binary salp swarm optimizer is used for finding the best features.
Abstract: Feature selection is a technique commonly used in Data Mining and Machine Learning. Traditional feature selection methods, when applied to large datasets, generate a large number of feature subsets. Selecting optimal features within this high dimensional data space is time-consuming and negatively affects the system’s performance. This paper proposes a new binary Salp Swarm Algorithm (bSSA) for selecting the best feature set from transformed datasets. The proposed feature selection method first transforms the original data-set using Principal Component Analysis (PCA) and fast Independent Component Analysis (fastICA) based hybrid data transformation methods; next, a binary Salp Swarm optimizer is used for finding the best features. The proposed feature selection approach improves accuracy and eliminates the selection of irrelevant features. We validate our technique on fifteen different benchmark data sets. We conduct an extensive study to measure the performance and feature selection accuracy of the proposed technique. The proposed bSSA is compared to Binary Genetic Algorithm (bGA), Binary Binomial Cuckoo Search (bBCS), Binary Grey Wolf Optimizer (bGWO), Binary Competitive Swarm Optimizer (bCSO), and Binary Crow Search Algorithm (bCSA). The proposed method attains a mean accuracy of 95.26% with 7.78% features on PCA-fastICA transformed datasets. The results show that bSSA outperforms the existing methods for the majority of the performance measures.
54 citations
Authors
Showing all 739 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dinesh Kumar | 69 | 1333 | 24342 |
Seema Agarwal | 52 | 309 | 12325 |
Vikas Bansal | 43 | 184 | 23455 |
Rajeev Gupta | 33 | 231 | 3704 |
Harish Sharma | 24 | 139 | 1963 |
Basant Agarwal | 21 | 66 | 1386 |
Ajay Verma | 20 | 189 | 1554 |
Sunil Dutt Purohit | 20 | 94 | 1228 |
Durga Prasad Mohapatra | 18 | 186 | 1293 |
Prashant K. Jamwal | 17 | 62 | 1267 |
Dhanesh Kumar Sambariya | 16 | 49 | 693 |
Girish Parmar | 14 | 82 | 665 |
Vikas Bansal | 13 | 17 | 1015 |
Sandeep Kumar Parashar | 13 | 22 | 339 |
Mithilesh Kumar | 12 | 103 | 734 |