Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the...
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Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the apparent volatility and chaotic nature of the non-ferrous metal price sequence, forecasting it remains a difficult challenge. While prior research employed a variety of methodologies to forecast metal prices, they overlooked the critical role of chaos feature analysis and the necessity of error analysis, severely limiting prediction accuracy. This paper designs a novel non-ferrous metal price ensemble prediction system that incorporates data decomposition, phase space reconstruction, multi-objectiveoptimization, point prediction, and interval prediction. A combined kernel extreme learning machine based on the improved multi-objective lion swarm optimizationalgorithm is developed and theoretically explained to improve prediction accuracy and reliability. Additionally, the appropriate creation of the prediction interval based on the best-fit distribution of the point prediction error enabled the examination of various levels of uncertainty. In an empirical experiment using copper and aluminum prices from the London Metal Exchange, the proposed system demonstrated benefits in point and interval prediction, providing decision makers with useful prediction references.
For the large scale services with high-dimensional QoS attributes and distributed environment, traditional service selection approaches are faced with unprecedented challenges in terms of efficiency and performance of...
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For the large scale services with high-dimensional QoS attributes and distributed environment, traditional service selection approaches are faced with unprecedented challenges in terms of efficiency and performance of QoS. To address these challenges, we propose a three-phase large scale Skyline service selection framework for service composition in clouds. This framework adopts distributed parallel Skyline computation with MapReduce to prune redundant candidate services, and employs parallel multiobjectiveoptimizationalgorithm based on MapReduce to select Skyline services from the tremendous amount of Skyline services warehouse for composing single service into a set of more powerful Skyline composite services, then applies Top-k query processing technology or multiple attribute decision making support method to select k Skyline composite services from the set of Skyline composite services. Through theoretical analysis, the framework can efficiently solve the service selection problem with large scale services, high-dimensional QoS in cloud computing environment, and quickly generate better composite services with the global optimal QoS.
This paper presents an economic comparison between HVAC and HVDC transmission systems for an offshore wind farm with 500 MW installed power. Both transmission systems will be compared in terms of energy losses and tot...
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ISBN:
(纸本)9781467319706
This paper presents an economic comparison between HVAC and HVDC transmission systems for an offshore wind farm with 500 MW installed power. Both transmission systems will be compared in terms of energy losses and total investment costs. The main objective is to obtain optimal tradeoffs between these criteria when different distances to shore are considered.
The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order t...
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The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjectiveoptimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.
Wafer acceptance testing (WAT) is a process that is used to assess the quality and reliability of manufactured wafers. This technique for the early detection and screening of chips allows for improvements in their rel...
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Wafer acceptance testing (WAT) is a process that is used to assess the quality and reliability of manufactured wafers. This technique for the early detection and screening of chips allows for improvements in their reliability and performance during the manufacture of semiconductor devices. The automatic test equipment (ATE) used for processing millions of wafers is susceptible to a number of issues, including the absence of data values, the presence of redundant parameters, and categorical imbalance. These issues increase the cost of data processing, and impede an investigation into the relationship between WAT and feature diagnostics. In this study, we propose a method with a low test escape rate based on a multi-objective optimization algorithm to reduce the cost of testing and minimize the number of defective dice that go undetected. The proposed method retains outliers, dynamically selects the range of the neighborhood to reduce the cost of testing, and uses Shapley values to analyze a WAT dataset to determine the importance of features of the data. The multi-objective optimization algorithm ranks features by their importance, and applies an adaptive method to eliminate features with a low overall correlation, thereby reducing the risk that defective dice are undetected.
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