In today’s competitive market, product quality holds significant importance for consumers, motivating companies to prioritise high-quality product development and enhance their competitiveness. Acceptance sampling pl...
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This paper considers a well-known two-plan sampling system, Tightened-Normal-Tightened (TNT) sampling scheme, which includes two single sampling plans (SSPs) with regulations for transitioning between tightened and no...
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In recent years, it has become possible to accumulate a large amount of browsing history data from users on websites and it is desirable to make use of such data for marketing purposes. In particular, customer browsin...
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While extant research explores the impact of electric vehicle (EV) incentives on EV market shares, less is known about how such policies and other socioeconomic factors interact that ultimately affect the goal of tran...
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Manufacturers must meet high-quality standards and exceed customer expectations to stay competitive due to significant technological advancements in recent decades. While implementing the yield measure is useful for a...
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Dialogue policy learning(DPL)is a key component in a task-oriented dialogue(TOD)*** goal is to decide the next action of the dialogue system,given the dialogue state at each turn based on a learned dialogue *** learni...
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Dialogue policy learning(DPL)is a key component in a task-oriented dialogue(TOD)*** goal is to decide the next action of the dialogue system,given the dialogue state at each turn based on a learned dialogue *** learning(RL)is widely used to optimize this dialogue *** the learning process,the user is regarded as the environment and the system as the *** this paper,we present an overview of the recent advances and challenges in dialogue policy from the perspective of *** specifically,we identify the problems and summarize corresponding solutions for RL-based dialogue policy *** addition,we provide a comprehensive survey of applying RL to DPL by categorizing recent methods into five basic elements in *** believe this survey can shed light on future research in DPL.
In modern global supply chain system strategies, the importance of risk management to prevent disruption thereto is increasing. One efficient approach for overcoming this problem is a collaboration between manufacture...
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Electric vehicle battery recycling has emerged as a critical process in transitioning to sustainable transportation. As the demand for electric vehicles continues to rise, so does the need to address the end-of-life m...
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To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage a...
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To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage and selection operator (ALASSO) and integrated criterion (IC) is proposed. The ALASSO method is studied for shrinkage of output weights and selection of variables. Furthermore, for the better performance of PIs, composite weighted linear programming (CWLP) is proposed to modify the conventional linear programming cost function of quantile regression (QR), by combining it with Bayesian information criterion (BIC) as an IC to optimize the coefficients of PIs. Then, the multiple fold cross model (MFCM) is utilized to improve the PIs performance. Multistep probabilistic prediction of 15-minute wind speed is performed based on the real wind farm data from the northeast of China. The effectiveness of the proposed approach is validated through the performances' comparisons with conventional methods.
Acceptance sampling, the most widely used technique in practice, provides rules for making decisions on product acceptance with specified quality standards. A quick-switching sampling (QSS) system, which involves a No...
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