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Featured researches published by Ashish Mani.


Archive | 2019

Simultaneous Placement and Sizing of DG and Capacitor to Minimize the Power Losses in Radial Distribution Network

G. Manikanta; Ashish Mani; H. P. Singh; D. K. Chaturvedi

In the present power system scenario, minimization of power losses in the distribution network is one of the interesting areas of research and modern challenges in the research community. Distribution network has large and complex structure; it produces more power losses as compared to transmission system. High power losses and poor voltage regulation are occurring at each bus when it moves away from the substation node to end node. Many methods have been implemented to minimize the power losses in the distribution system. Sitting and sizing of Distributed Generation (DG) and capacitors are new approaches used in the distribution system to minimize the power losses. However, capacity and location of DG and capacitors in the distribution system are considered independently. Improved voltage profile, increased overall energy efficiency, and reduced environmental impacts are some benefits produced by DG and capacitors. Consumer is also benefited from DG and capacitors optimization in terms of improved quality of power supply at lower cost. Sitting and sizing of DG and capacitor is a combinatorial optimization problem, and hence metaheuristics are used. An Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA) approach is used for the optimization of DG and capacitors. In this paper, a new approach is considered by simultaneous placement and sizing of Distributed Generation (DG) and capacitor to minimize power losses. The effectiveness of the proposed algorithm is tested on 85-bus system. The experimental results show that AQiEA has better performance as compared with some existing algorithms.


Quantum Information Processing | 2018

An all-pair quantum SVM approach for big data multiclass classification

Arit Kumar Bishwas; Ashish Mani; Vasile Palade

In this paper, we discuss a quantum approach for the all-pair multiclass classification problem. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are k(k − 1)/2 classifiers for a k-class classification problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speedup due to quantum computing. The quantum all-pair algorithm can also be used with other classification algorithms, and a speedup gain can be achieved as compared to their classical counterparts.In this paper we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are k (k-1)/2 classifiers for a k-class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.


international conference on contemporary computing | 2016

Big data classification with quantum multiclass SVM and quantum one-against-all approach

Arit Kumar Bishwas; Ashish Mani; Vasile Palade

In this paper, we have proposed a quantum approach for multiclass support vector machines to handle big data classification. To achieve this goal, we have also developed and implemented a quantum version of the one-against-all algorithm. The proposed approach demonstrates that the big data multiclass classification can be implemented with quantum multiclass support vector machine in logarithmic time complexity on a quantum computer, compared to the classical multiclass support vector machines that can be implemented with polynomial time complexity. Hence, our proposed approach exhibits an exponential speed up in time complexity for big data multiclass classification.


innovative applications of computational intelligence on power energy and controls with their impact on humanity | 2016

Solving Economic Load Dispatch problem with valve loading effect using adaptive real coded quantum-inspired evolutionary algorithm

Deepika Joshi; Anjali Jain; Ashish Mani

Economical dispatch of power is a significant real time optimization problem in the operation of power system, which allocates the loads on the committed generating units to reduce the generation cost, while meeting all the inequality and equality constraints. Traditionally, economic load dispatch problems have been formulated as convex optimization problem and various classical methods such as gradient methods, base-point participation factor method, etc. have been used for solving them. The traditional formulation is a quadratic input-output curve or piece wise linear cost curve, which are continuous and convex, however, real generating units have valve loading effects, which make the cost curve discontinuous and non-convex and hence the economic dispatch problem is no longer simple to handle. Thus, the classical methods cannot be employed for non-convex problems. The non-convex, non-linear and discontinuous economic dispatch (ED) problem can be better solved by metaheuristic approaches. Such approaches often require tuning of their respective evolutionary parameters. Although, solution to such type of problem have already been solved by different metaheuristic optimization techniques in previous studies as well, however, the algorithm employed in this paper to solve Power Economic Load Dispatch (PELD) problem is adaptive real coded quantum-inspired evolutionary algorithm, which does not require tuning of evolutionary parameters and performs better than some of the existing techniques on standard 6-bus 3 machine system and IEEE 14 bus 5 unit system.


2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) | 2016

Placing distributed generators in distribution system using adaptive quantum inspired evolutionary algorithm

G. Manikanta; Ashish Mani; H. P. Singh; D. K. Chaturvedi


arXiv: Learning | 2018

Big Data Quantum Support Vector Clustering.

Arit Kumar Bishwas; Ashish Mani; Vasile Palade


ieee pes asia pacific power and energy engineering conference | 2017

Minimization of power losses in distribution system using symbioitic organism search algorithm

G. Manikanta; Ashish Mani; H. P. Singh; D. K. Chaturvedi


arXiv: Networking and Internet Architecture | 2017

Efficient clear air turbulence avoidance algorithms using IoT for commercial aviation

Amlan Chatterjee; Hugo Flores; Bin Tang; Ashish Mani; Khondker S. Hasan


Archive | 2017

An All-Pair Approach for Big Data Multiclass Classification with Quantum SVM.

Arit Kumar Bishwas; Ashish Mani; Vasile Palade


Archive | 2017

Gaussian Kernel in Quantum Paradigm.

Arit Kumar Bishwas; Ashish Mani; Vasile Palade

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D. K. Chaturvedi

Dayalbagh Educational Institute

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H. P. Singh

Dayalbagh Educational Institute

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Amlan Chatterjee

California State University

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Hugo Flores

California State University

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Khondker S. Hasan

University of Houston–Clear Lake

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