Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)meth...Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.展开更多
In this paper, we consider constrained denumerable state non-stationary Markov decision processes (MDPs, for short) with expected total reward criterion. By the mechanics of intro- ducing Lagrange multiplier and using...In this paper, we consider constrained denumerable state non-stationary Markov decision processes (MDPs, for short) with expected total reward criterion. By the mechanics of intro- ducing Lagrange multiplier and using the methods of probability and analytics, we prove the existence of constrained optimal policies. Moreover, we prove that a constrained optimal policy may be a Markov policy, or be a randomized Markov policy that randomizes between two Markov policies, that differ in only one state.展开更多
Under the current long-term electricity market mechanism,new energy and thermal power face issues such as deviation assessment and compression of generation space.The profitability of market players is limited.Simulta...Under the current long-term electricity market mechanism,new energy and thermal power face issues such as deviation assessment and compression of generation space.The profitability of market players is limited.Simultaneously,the cooperation model among various energy sources will have a direct impact on the alliance’s revenue and the equity of income distribution within the alliance.Therefore,integrating new energy with thermal power units into an integrated multi-energy complementary system to participate in the long-term electricity market holds significant potential.To simulate and evaluate the benefits and internal distribution methods of a multi-energy complementary system participating in long-term market transactions,this paper first constructs a multi-energy complementary system integrated with new energy and thermal power generation units at the same connection point,and participates in the annual bilateral game as a unified market entity to obtain the revenue value under the annual bilateral market.Secondly,based on the entropy weight method,improvements are made to the traditional Shapley value distribution model,and an internal distribution model for multi-energy complementary systems with multiple participants is constructed.Finally,a Markov Decision Process(MDP)evaluation system is constructed for practical case verification.The research results show that the improved Shapley value distribution model achieves higher satisfaction,providing a reasonable allocation scheme for multi-energy complementary cooperation models.展开更多
Dear Editor,This letter introduces a novel approach to address the bearings-only target motion analysis(BO-TMA)problem by incorporating deep reinforcement learning(DRL)techniques.Conventional methods often exhibit bia...Dear Editor,This letter introduces a novel approach to address the bearings-only target motion analysis(BO-TMA)problem by incorporating deep reinforcement learning(DRL)techniques.Conventional methods often exhibit biases and struggle to achieve accurate results,especially when confronted with high levels of noise.In this letter,we formulate the BO-TMA problem as a Markov decision process(MDP)and process it within a DRL framework.Simulation results demonstrate that the proposed DRL-based estimator achieves reduced bias and lower errors compared to existing estimators.展开更多
基金supported by theHubei Provincial Technology Innovation Special Project and the Natural Science Foundation of Hubei Province under Grants 2023BEB024,2024AFC066,respectively.
文摘Underwater images frequently suffer from chromatic distortion,blurred details,and low contrast,posing significant challenges for enhancement.This paper introduces AquaTree,a novel underwater image enhancement(UIE)method that reformulates the task as a Markov Decision Process(MDP)through the integration of Monte Carlo Tree Search(MCTS)and deep reinforcement learning(DRL).The framework employs an action space of 25 enhancement operators,strategically grouped for basic attribute adjustment,color component balance,correction,and deblurring.Exploration within MCTS is guided by a dual-branch convolutional network,enabling intelligent sequential operator selection.Our core contributions include:(1)a multimodal state representation combining CIELab color histograms with deep perceptual features,(2)a dual-objective reward mechanism optimizing chromatic fidelity and perceptual consistency,and(3)an alternating training strategy co-optimizing enhancement sequences and network parameters.We further propose two inference schemes:an MCTS-based approach prioritizing accuracy at higher computational cost,and an efficient network policy enabling real-time processing with minimal quality loss.Comprehensive evaluations on the UIEB Dataset and Color correction and haze removal comparisons on the U45 Dataset demonstrate AquaTree’s superiority,significantly outperforming nine state-of-the-art methods across five established underwater image quality metrics.
基金the National Natural Science Foundation of China !19901038by Natural Science Foundation of Guangdong Province and by Found
文摘In this paper, we consider constrained denumerable state non-stationary Markov decision processes (MDPs, for short) with expected total reward criterion. By the mechanics of intro- ducing Lagrange multiplier and using the methods of probability and analytics, we prove the existence of constrained optimal policies. Moreover, we prove that a constrained optimal policy may be a Markov policy, or be a randomized Markov policy that randomizes between two Markov policies, that differ in only one state.
文摘Under the current long-term electricity market mechanism,new energy and thermal power face issues such as deviation assessment and compression of generation space.The profitability of market players is limited.Simultaneously,the cooperation model among various energy sources will have a direct impact on the alliance’s revenue and the equity of income distribution within the alliance.Therefore,integrating new energy with thermal power units into an integrated multi-energy complementary system to participate in the long-term electricity market holds significant potential.To simulate and evaluate the benefits and internal distribution methods of a multi-energy complementary system participating in long-term market transactions,this paper first constructs a multi-energy complementary system integrated with new energy and thermal power generation units at the same connection point,and participates in the annual bilateral game as a unified market entity to obtain the revenue value under the annual bilateral market.Secondly,based on the entropy weight method,improvements are made to the traditional Shapley value distribution model,and an internal distribution model for multi-energy complementary systems with multiple participants is constructed.Finally,a Markov Decision Process(MDP)evaluation system is constructed for practical case verification.The research results show that the improved Shapley value distribution model achieves higher satisfaction,providing a reasonable allocation scheme for multi-energy complementary cooperation models.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(LZ23F030006)the National Natural Science Foundation of China(62173299,U23B2060)+1 种基金the Joint Fund of Ministry of Education for Pre-Research of Equipment(8091B022147,8091B032234,8091B042220)the Fundamental Research Funds for Xi’an Jiaotong University(xtr072022001).
文摘Dear Editor,This letter introduces a novel approach to address the bearings-only target motion analysis(BO-TMA)problem by incorporating deep reinforcement learning(DRL)techniques.Conventional methods often exhibit biases and struggle to achieve accurate results,especially when confronted with high levels of noise.In this letter,we formulate the BO-TMA problem as a Markov decision process(MDP)and process it within a DRL framework.Simulation results demonstrate that the proposed DRL-based estimator achieves reduced bias and lower errors compared to existing estimators.
