This paper investigates the optimal control problem for an unknown linear time-invariant (LTI) system. To solve this problem, a novel composite policy iteration (CPI) algorithm based on adaptive dynamic programming is...
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In many service systems, especially those in healthcare, customer waiting times can result in increased service requirements. Such service slowdowns can significantly impact system performance. Therefore, it is import...
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We look at the long-standing problem of segmenting unlabeled speech into word-like segments and clustering these into a lexicon. Several previous methods use a scoring model coupled with dynamic programming to find an...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
We look at the long-standing problem of segmenting unlabeled speech into word-like segments and clustering these into a lexicon. Several previous methods use a scoring model coupled with dynamic programming to find an optimal segmentation. Here we propose a much simpler strategy: we predict word boundaries using the dissimilarity between adjacent self-supervised features, then we cluster the predicted segments to construct a lexicon. For a fair comparison, we update the older ES-KMeans dynamic programming method with better features and boundary constraints. On the five-language ZeroSpeech benchmarks, our simple approach gives similar state-of-the-art results compared to the new ES-KMeans+ method, while being almost five times faster. Project webpage: https://***/prom-seg-clus.
This work addresses the problem of jointly optimizing the binary offloading decision making and the communication resource allocation for a system using time-division multiple access (TDMA). The goal is to minimize th...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
This work addresses the problem of jointly optimizing the binary offloading decision making and the communication resource allocation for a system using time-division multiple access (TDMA). The goal is to minimize the cost of the energy expended by all devices, while meeting individual deadlines and power budget constraints. Prior work addressing this problem proposed a suboptimal solution. We show that the problem can be solved globally optimally using an efficient dynamic programming algorithm.
We consider a broad class of dynamic programming (DP) problems that involve a partially linear structure and some positivity properties in their system equation and cost function. We address deterministic and stochast...
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This study introduces an adaptive optimal control system for voltage regulation of unknown DC loads connected to photovoltaic systems, addressing the uncertain parameters of DC-DC converters under varying environmenta...
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ISBN:
(数字)9798331541125
ISBN:
(纸本)9798331541132
This study introduces an adaptive optimal control system for voltage regulation of unknown DC loads connected to photovoltaic systems, addressing the uncertain parameters of DC-DC converters under varying environmental conditions. The controller's primary function is to maintain stable voltage at the load terminals despite environmental fluctuations, specifically solar irradiation and temperature changes. To achieve optimal control with unknown system dynamics, we implement a Policy Iteration (PI) algorithm based on an adaptive dynamic programming (ADP) technique. This approach is particularly effective for handling uncertainties in both system dynamics and environmental conditions. To validate the proposed method, extensive simulations are conducted under different scenarios. The results demonstrate robust voltage regulation capabilities across diverse weather conditions.
We introduce a novel method for handling endpoint constraints in constrained differential dynamic programming (DDP). Unlike existing approaches, our method guarantees quadratic convergence and is exact, effectively ma...
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With the intensification of the global energy crisis and environmental pollution, the research and development of intelligent connected new energy vehicles have become a hot topic. This study focuses on the hierarchic...
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ISBN:
(数字)9798331506797
ISBN:
(纸本)9798331506803
With the intensification of the global energy crisis and environmental pollution, the research and development of intelligent connected new energy vehicles have become a hot topic. This study focuses on the hierarchical optimization control method of intelligent connected new energy vehicles, aiming to achieve efficient energy management and utilization through dynamic programming algorithms. The study first constructed the dynamic model and energy $\text{flow}$ model of intelligent connected new energy vehicles, providing a theoretical basis for hierarchical optimization control. Subsequently, dynamic programming algorithms were used to optimize the energy management strategy, and the Bellman principle of reverse optimization was employed to search for the global optimal solution, in order to achieve rational allocation and efficient utilization of energy. The simulation experiment results show that the hierarchical optimization control method based on dynamic programming can significantly improve the fuel economy and emission performance of intelligent connected new energy vehicles, providing strong support for the intelligent and efficient development of new energy vehicles. This study not only has important theoretical value, but also provides useful reference for the practical application and promotion of intelligent connected new energy vehicles.
This paper introduces a novel multi-stage decision-making model that integrates hypothesis testing and dynamic programming algorithms to address complex decision-making ***,we develop a sampling inspection scheme that...
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As the importance of optimizing resource management systems continues to grow, this paper focuses on the economic optimization of integrated systems through advanced computational models. First, we analyzed the econom...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
As the importance of optimizing resource management systems continues to grow, this paper focuses on the economic optimization of integrated systems through advanced computational models. First, we analyzed the economic feasibility of resource management systems without storage solutions, identifying that the configuration of power generation methods and dependence on external grids are key factors influencing economic outcomes. Subsequently, we introduced a dynamic programming model to optimize storage-integrated systems. By adjusting system constraints and solving linear programming projections, we significantly reduced the typical daily cost to 15,658 yuan. The optimization process, including charging and discharging strategies and the State of Charge (SOC) curve, was thoroughly visualized. Furthermore, considering fluctuations in demand and return on investment, a new coordinated configuration scheme was developed to optimize storage and resource acquisition under varying pricing conditions over time. This study highlights the crucial role of advanced optimization algorithms in enhancing the economic efficiency and reliability of future resource management systems, providing essential insights for the sustainable utilization of resources.
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