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Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)approaches 认领 引用 被引量：7
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作者 Behzad Elhami Asadollah Akram Majid Khanali 《Information Processing in Agriculture》 EI 2016年第3期190-205,共16页
Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related... Energy consumption in agricultural products and its environmental damages has increased in recent centuries.Life cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life cycle.In this study,optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)techniques.Data were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of 2014-2015.The results of optimization revealed that,when applying MOGA,optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique;so that,total energy requirement in optimum situation was found to be 31511.72 and 27570.61 MJ ha^-1 by using DEA and MOGA techniques,respectively;showing a reduction by 5.11%and 17%relative to current situation of energy consumption.Optimization of environmental impacts by application of MOGA resulted in reduction of acidification potential(ACP),eutrophication potential(EUP),global warming potential(GWP),human toxicity potential(HTP)and terrestrial ecotoxicity potential(TEP)by 29%,23%,10%,6%and 36%,respectively.MOGAwas capable of reducing the energy consumption from machinery,farmyard manure(FYM)diesel fuel and nitrogen fertilizer(the mostly contributed inputs to the environmental emissions)by 59%,28.5%,24.58%and 11.24%,respectively.Overall,the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system. 展开更多
关键词 Data envelopment analysis Energy Life cycle assessment Multi objective genetic algorithm
Neural Network Repair With Shapley-Guided Search 认领 引用
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作者 Xiaofu Du Zixiong Zhang +2 位作者 Xuesong Wang Linqiang Liu Junyan Qian 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期816-834,共19页
The deployment of deep neural networks(DNNs)in safety-critical domains is critically hampered by their vulnerability to defects,which can arise from malicious attacks or low-quality data.Therefore,precisely locating t... The deployment of deep neural networks(DNNs)in safety-critical domains is critically hampered by their vulnerability to defects,which can arise from malicious attacks or low-quality data.Therefore,precisely locating the network components responsible for these defects,and subsequently repairing them without compromising overall model performance,presents a significant challenge.To address this,this paper introduces NSRepair,a framework that combines interpretable fault localisation with multi-objective optimisation.Specifically,to accurately attribute blame for a defect,we employ Shapley values to quantify the contribution of each neuron.To systematically manage the trade-off between defect correction and performance preservation,we formulate the repair task as a multi-objective optimisation problem.We conducted extensive experiments across four distinct repair tasks,validating NSRepair on diverse model architectures against seven specialised state-of-the-art methods.The results demonstrate that our unified framework effectively repairs a wide range of defects,demonstrating its potential as a versatile and practical solution for improving DNN dependability.Our code is publicly available at http://gffzzd3cc09b8251d45dfsw0kb6pwpwvko60nn.ffgz.tsg.suse.edu.cn/10.5281/zenodo.17494304. 展开更多
关键词 defect localization multi‐objective genetic algorithm neural network repair Shapley
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