Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application *** and effective TR early warning technology is urgently required considering the current fire safet...
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Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application *** and effective TR early warning technology is urgently required considering the current fire safety situation of *** this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high *** in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF *** mechanisms corresponding to the features at specific frequencies were ***,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse *** technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.
Good distribution of samples and weights can improve the computational accuracy and efficiency in the stochastic response analyses of aerospace problems with uncertain *** work proposes a new Generalized L2 Discrepanc...
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Good distribution of samples and weights can improve the computational accuracy and efficiency in the stochastic response analyses of aerospace problems with uncertain *** work proposes a new Generalized L2 Discrepancy based on a General Point(GL2D-GP)for generating samples and their corresponding *** proposed GL2D-GP is an extension of the existing discrepancy by introducing the non-same weights and a smaller box to measure probability *** the GL2D-GP can yield a weight optimization formula that generates a set of optimal non-identical weights for a given sample *** minimizing the GL2D-GP assigned to the set of optimal non-same weights,a new sample and weight generation method is *** the proposed method,the samples can be easily generated in terms of the generalized Halton formula with a series of optimal permutation vectors which are found by the intelligent evolutionary *** the sample set is obtained,the optimal weights can be generated in terms of the weight optimization *** numerical examples are presented to verify the high accuracy,efficiency,and strong robustness of the proposed sample generation method based on GL2D-GP.
Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimizati...
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Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.
The rapid development of DNA synthesis and sequencing technologies is making the ultra-high-density storage medium DNA to meet the rising demand for enormous data storage. The block storage interface, which is massive...
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AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and...
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Sign language translation aims to transform continuous sign language videos into text, thereby mapping sign language actions onto natural language. Current research primarily focuses on the Transformer's encoder-d...
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In visual tasks such as image classification, the presence of domain shift often renders deep neural network models trained solely on specific datasets unable to generalize to new domains. In practical applications, d...
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In cooperative multi-agent reinforcement learning(MARL), where agents only have access to partial observations, efficiently leveraging local information is critical. During long-time observations, agents can build awa...
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In cooperative multi-agent reinforcement learning(MARL), where agents only have access to partial observations, efficiently leveraging local information is critical. During long-time observations, agents can build awareness for teammates to alleviate the restriction of partial observability. However, previous MARL methods usually neglect awareness learning from local information for better collaboration. To address this problem, we propose a novel framework, multi-agent local information decomposition for awareness of teammates(LINDA), with which agents learn to decompose local information and build awareness for each teammate. We model the awareness as stochastic random variables and perform representation learning to ensure the informativeness of awareness representations by maximizing the mutual information between awareness and the actual trajectory of the corresponding agent. LINDA is agnostic to specific algorithms and can be flexibly integrated with different MARL methods. Sufficient experiments show that the proposed framework learns informative awareness from local partial observations for better collaboration and significantly improves the learning performance, especially on challenging tasks.
Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging *** pull-based development model,as the state-of-art collaborative development mec...
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Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging *** pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'***,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this *** not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant *** this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission *** a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing *** then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change *** evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.
With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application ***,the issue of classifying data streams based on mobile sensors can be formalized as a multi-...
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With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application ***,the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple *** incremental learning methods are often single-task single-view,which cannot learn shared representations between relevant tasks and *** adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges,utilizing the idea of multi-task multi-view ***,the attention mechanism is first used to align different sensor data of different *** addition,MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental *** reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.
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