Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article...
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This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from ...
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This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.
Load scheduling plays a vital role in the home energy management systems. The main objective of this load scheduling is to balance the power demand and supply power without degrading the performance of the loads and c...
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In Karachi, the transport and building industries have a major impact on carbon dioxide (CO2) emissions and greenhouse gas emissions. This study is innovative in that it examines how air temperature changes over time ...
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Complex nonlinear systems have been very often found to exhibit unpredictable, chaotic behavior as they become extremely sensitive to initial conditions. In electric motor drives, which are highly nonlinear systems, o...
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In modern process industries, maintaining precise weight measurements is critical for ensuring product quality and operational efficiency. Accurate weight measurement systems not only aid in meeting regulatory standar...
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As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** c...
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As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.
With the continuous construction of HVDC, it has gradually become dis-tances and large capacity transmission, as well as one of the main technologies of regional power grid interconnection. The scale of AC-DC hybrid g...
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Predicting productivity in garment manufacturing is important for optimizing workforce management and operational efficiency of garments. The prediction of productivity enables industries to adapt proactively to marke...
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To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries pre...
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To predict the lithium-ion(Li-ion)battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles'degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM)network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.
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