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Performance evaluation and optimum design strategies of an acid water electrolyzer system for hydrogen production

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2012年第24期37卷 18615-18621页

作者： Zhang, Houcheng Su, Shanhe Chen, Xiaohang Lin, Guoxing Chen, Jincan Xiamen Univ Dept Phys Xiamen 361005 Peoples R China

The performance of a new acid water electrolyzer system for hydrogen production is investigated, based on semi-empirical equations of a phosphoric acid water electrolyzer. The circulating electrolyte concentrations under differently operating temperatures are optimized so that the minimum input voltages of the electrolyzer are determined for other given conditions. The optimum electrochemical characteristics of the electrolyzer are revealed. Moreover, it is expounded that the Joule heat resulting from the irreversibilities inside the electrolyzer is larger than the thermal energy needed in the water splitting process. The general performance characteristics of the phosphoric acid water electrolyzer system are discussed, from which the lower bound of the operating current density is determined. The upper bound of the operating current density is further determined by introducing a multi-objective function including the system efficiency and hydrogen production rate. Consequently, some optimum design strategies of a phosphoric acid water electrolyzer system are obtained and may be chosen according to different practical requirements. (C) Copyright 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Phosphoric acid water electrolyzer Overpotential multi-objective function Performance evaluation Optimum design strategy

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Throughput and Outage Probability-Aware Intelligent Swarm-Based multi-Hop Selection in Vehicular Network

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CYBERNETICS AND SYSTEMS 2024年第1期55卷 157-183页

作者： Kiran, Uppula Kumar, Krishan Lovely Profess Univ Dept Elect & Commun Engn Jalandhar Punjab India Lovely Profess Univ Fac Sch Elect & Elect Engn Jalandhar Punjab India

Cooperation is an emerging paradigm for improving spatial differentiation in Vehicular Ad-Hoc Networks (VANETs). Hence, many network solutions are motivated through cooperative communications for boosting VANET efficiency regarding specific network constraints like energy efficiency, network capacity, and outage probability. The main intention of this paper is to design and develop an optimization model for selecting the minimum number of multi-hops between the source and destination for the cooperative VANET networks. The first phase has a first-time slot, where a signal is transmitted by the source to the cooperative nodes (its relays) and their equivalent destination. The main problem considered here is to optimally select the number of hops or relays that are adaptable for communication, to solve the multi-objective functions concerning the target throughput and outage probability. The adoption of new Dimension-based Cat Swarm Optimization (D-CSO) is the main contribution here to optimally select the multi-hops among source and destination. Through the performance evaluation, it is observed that the designed model has achieved networks-wide fairness performance and a good convergence rate.

关键词： Cooperative communication Dimension-based Cat Swarm Optimization multi-hop selection multi-objective function outage probability throughput Vehicular Ad-Hoc Networks

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An efficient method of disturbance analysis and train rescheduling using MWDLNN and BMW-SSO algorithms

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SOFT COMPUTING 2021年第18期25卷 12031-12041页

作者： Kumar, Neeraj Mishra, Abhishek Natl Inst Technol Delhi Dept Mech Engn Delhi India

The ability to efficiently reschedule the trains when disturbances occur is one among the vital factors in attaining higher punctuality on railway traffic systems. Numerous techniques were developed to carry out train rescheduling, but the existing methodologies don't consider the probability of conflicts (or) disturbance analysis (DA). Thus, in this paper, an efficient method of train rescheduling (TRS) system is proposed utilizing the Brownian motion weighted-based salp swarm optimization (BMW-SSO) algorithm. The proposed technique follows two processes: (i) DA and (ii) rescheduling. Primarily, the track detail is collected as an input, and the disturbances are identified utilizing the modified weight-based deep learning neural network. If any disturbance occurs, then the TRS process is performed utilizing the BMW-SSO. Aimed at this, the current timetable is utilized as an input, and some constraints are extracted from the input utilizing multi-choice mixed integer goal programming. After that, finally, the BMW-SSO chooses and optimizes the best constraints from the extracted constraints as well as reschedules the timetable. Here, the BMW-SSO alternatively chooses the best third rescheduling timetable. Subsequent to generating TRS, the first rescheduled timetable's feasibility is checked centred on the dictionary-centred checking technique. If the first rescheduled timetable is feasible, then it is signified as the optimal timetable. Otherwise, the second-created rescheduled timetable is utilized. And if the second one is unfeasible, then the thirdly created timetable is utilized. At last, the experiential examination confirms the proposed train timetable rescheduling (TTR) system's performance regarding the timetable deviation, operation cost, along with dwell time. The proposed TTR consumes low time while analogized with the scheduled table.

关键词： Timetable rescheduling Dictionary-based checking multi-objective function Brownian motion weighted-based salp swarm optimization (BMW-SSO) Modified weight-based deep learning neural network (MWDLNN)

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PSO based placement of multiple wind DGs and capacitors utilizing probabilistic load flow model

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SWARM AND EVOLUTIONARY COMPUTATION 2014年 19卷 15-24页

作者： Jain, Naveen Singh, S. N. Srivastava, S. C. IIT Dept Elect Engn Kanpur Uttar Pradesh India

Wind Power Distributed Generators (WPDGs) are being increasingly placed in the power system due to their several technical and environmental benefits. In this paper, a Modified Particle Swarm Optimizer (MPSO) based method is proposed for placement of multiple WPDGs and capacitors. Monte Carlo Simulation (MCS) based probabilistic load flow, considering uncertainty in load demand and wind generation, is developed. It is used to modify the WPDGs' and capacitors' sizes utilizing a sensitivity based approach, which maintains branch currents and bus voltages within their prescribed limits. The proposed method is simple, accurate and generic, and it can provide multiple choices to the utilities to place capacitors and WPDGs under various system constraints. Results on three distribution networks demonstrate the effectiveness of the proposed method. The impact of the DG placement on the system voltage profile, line loss, environment, and cost of generation has also been investigated on three distribution systems. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Distributed Generation Monte Carlo simulation multi-objective function Particle Swarm Optimization Radial distribution system

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Iterative-based visualization-oriented fusion scheme for hyperspectral images

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第4期12卷 757-765页

作者： Ablin, R. Sulochana, C. Helen Arunachala Coll Engn Women Vellichanthai India St Xaviers Catholic Coll Engn Nagercoil Tamil Nadu India

This article investigates a novel visualization-based fusion of hyperspectral image bands using an iterative approach. Given a multi-objective function and the pixel-based hyperspectral image fusion method, the optimization process is described as finding the optimal fusion parameters to improve the fusion performance. Accordingly, an iterative-based approach is adopted. In the first step, the fusion process is developed using the pixel-based fusion technique. In the second step, the fused image is produced, and the fusion quality is assessed for multi-objective function construction. For multi-objective formulation, we focus three desired properties of the fused image such as entropy, variance, and smoothness. In the last step, fusion parameters are updated iteratively by examining the objective function. Here, the self-adaptive learning particle swarm optimizer is used to refine the fusion parameters iteratively. Different hyperspectral images, such as Cuprite mining, AVIRIS Indian pines scene, are employed in the evaluation. Quantitative analysis of fused images is carried out through some efficient fusion metrics such as correlation coefficient, entropy, Q-average, ERGAS, SAM, and SID. Experimental results show that the proposed approach outperforms existing methods in terms of both objective function criteria and visual effect.

关键词： Visualization Fusion Hyperspectral image SALPSO Iterative multi-objective function

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A multi-objective and dictionary-based checking for efficient rescheduling trains

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ALEXANDRIA ENGINEERING JOURNAL 2021年第3期60卷 3233-3241页

作者： Kumar, Neeraj Mishra, Abhishek Natl Inst Technol Delhi Dept Mech Engn Sect A-7 Delhi 110040 India

In railway networks, unexpected disruptions happen for numerous reasons, which in-turn induce delays and cancellations that eventually bring about passenger inconvenience. Thus, the Trains Timetable Rescheduling (TTR) is required, but "how to reschedule the railway timetable" is an imperative issue in real train operations. Though the existing research works concentrated on rescheduling the timetable, the delay time along with reliability, still, are not greatly solved. Thus, this paper proposed a multi-objective and dictionary-based checking for effective rescheduling trains. In this proposed system, first, the constraints are extracted, and the MCMIGP metrics are illustrated. Next, the GKACO optimizes the constraints to make optimal rescheduling. In this algorithm, the multi-objective function is viewed as the fitness function, which is the amalgamation of minimization of a train delay, dwell time, timetable deviation, along with the operational cost and augmentation of service reliability. Subsequent to the generation of rescheduling, the rescheduled timetable's feasibility is checked based on the dictionary-based checking technique. If the rescheduled timetable is feasible, then it is denoted as the optimal timetable. Otherwise, the timetable is again rescheduled by using the same GKACO. Lastly, the experimentation's analysis proves the proposed TTR system's performance. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： multi-Choice Mixed Integer Goal Programming (MCMIGP) Gaussian Kernel Ant Colony Optimization (GKACO) Dictionary-based checking multi-objective function

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Optimal energy management of micro grid connected system: a hybrid approach

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第5期13卷 2343-2354页

作者： Roy, Kallol Burdwan Univ Univ Inst Technol Burdwan W Bengal India

This dissertation proposes a hybrid technique for the optimal operation of microgrid (MG) connected sources through minimization of cost and better usage of MG connected sources. The MG connected sources are Wind Turbine (WT), Photovoltaic array (PV), Fuel Cell (FC), Micro Turbine (MT), Diesel Generator (DG) and battery storage. The proposed technique is the hybrid wrapper of Artificial Neural Network (ANN) with Artificial Bee Colony (ABC) and the Firefly Algorithm (FA). The proposed strategy is utilized to manage the power flow between the energy sources and the grid. At first, ANN technique predicts the load demand, based on the predicted load demand, ABC optimizes the MG configuration. Finally, FA technique helps to minimize the fuel cost, operation and maintenance (OM) cost. By then, the proposed technique is implemented in the MATLAB/Simulink working platform and compared with existing techniques such as Online Management (OM), ABC, ABC-ABC and ABC-Gravitational Search (GS). The maximum generated power of PV, WT, MT, FC, DG and battery is 5.9 kW, 5.9 kW, 4 kW, 4 kW, 6 kW and 0.5 kW. The total cost of ABC, ABC-ABC, ABC-GS and proposed technique is about 4.1$/hr, 3.9$/hr, 3.3$/hr and 2.7$/hr. The comparison results demonstrate that the proposed technique has less cost effective based on their load demand.

关键词： multi-objective function ABC ABC-ABC IABC Cost of fuel Cost for operation and maintenance

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A novel approach to combine features for salient object detection using constrained particle swarm optimization

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PATTERN RECOGNITION 2014年第4期47卷 1731-1739页

作者： Singh, Navjot Arya, Rinki Agrawal, R. K. Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

Despite significant amount of research works, the best available visual attention models still lag far behind human performance in predicting salient object. In this paper, we present a novel approach to detect a salient object which involves two phases. In the first phase, three features such as multi-scale contrast, center-surround histogram and color spatial distribution are obtained as described in Liu et al. model. Constrained Particle Swarm Optimization is used in the second phase to determine an optimal weight vector to combine these features to obtain saliency map to distinguish a salient object from the image background. To achieve this, we defined a simple fitness function which highlights a salient object region with well-defined boundary and effectively suppresses the background regions in an image. The performance is evaluated both qualitatively and quantitatively on a publicly available dataset. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of precision, recall, F -measure and area under curve. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Salient object detection Particle swarm optimization multi-objective function

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Directional overcurrent relay coordination considering non-standardized time curves

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ELECTRIC POWER SYSTEMS RESEARCH 2015年第May期122卷 42-49页

作者： Castillo Salazar, Carlos A. Conde Enriquez, Arturo Elisa Schaeffer, Satu Univ Autonoma Nuevo Leon FIME San Nicolas De Los Garza 66451 NL Mexico PITT Monterrey UANL CIIDIT Coordinac Comp Cient Apocada 66600 NL Mexico

In this work, a directional overcurrent relay coordination is carried out considering more than one level of short-circuit current and non-standardized inverse time curves. A genetic algorithm is proposed and shown capable of obtaining adequate protection settings in a reasonable time. The algorithm designs a specific time curve for each relay, improving the results of the coordination process without compromising the compatibility of the curves. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Directional overcurrent relay coordination Non-standardized curves Evolutionary computation Genetic algorithms Two fault levels multi-objective function

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An efficient and secured blind image watermarking using ABC optimization in DWT and DCT domain

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multiMEDIA TOOLS AND APPLICATIONS 2022年第26期81卷 36947-36964页

作者： Garg, Preeti Kishore, R. Rama Guru Gobind Singh Indraprastha Univ Univ Sch Informat Commun & Technol New Delhi India KIET Grp Inst Dept CSE Ghaziabad India

In today's digital world, all content is available over the internet, which anyone can use. Securing this data by adding copyright protection or ownership is called watermarking. This paper proposes a blind watermarking technique based on frequency domain transform to provide robustness and imperceptibility, and it also uses Artificial Bee Colony (ABC) optimization technique for optimization purposes. The ABC algorithm helps in finding the best embedding factor used during the embedding of the watermark. During extraction, only the watermarked image is needed that makes this technique blind watermarking. In this paper, a hybrid method using the logic of 2-level Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is used for embedding and for providing security to the scheme, an encrypted image is embedded in the cover image as the watermark. The proposed technique is applied to several images to prove its robustness and imperceptibility. The proposed scheme is implemented on various images, and its performance is measured by performing several attacks to compare with other existing methods. The experimental results show that Peak Signal to Noise Ratio (PSNR) value greater than 40 is achieved here. Furthermore, normalized Correlation (NC) value is greater than 0.9, proving its robustness against various attacks.

关键词： Digital watermarking multi-objective function DCT DWT ABC

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