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Underwater polarization de-scattering method with dynamic contrast constraint and adaptive genetic a*algorithm

OPTICS AND LASER TECHNOLOGY 2025年 183卷

作者： Li, Yafeng Sun, Xudong Zhang, Jiqing Chen, Yuehan Li, Yudong Fu, Xianping Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China

Polarization imaging effectively suppresses backscattering and has attracted widespread attention. However, existing methods struggle to capture the brightest and darkest polarization images and rely on manual selection of background regions to estimate degree of polarization (DoP) of backscattering. To address these challenges, this study proposes an underwater polarization de-scattering method with dynamic contrast constraint and adaptive genetic a*algorithm. First, we propose an automatic method to compute the brightest and darkest polarization images by taking derivatives of polarization intensity based on the Stokes vector and finding the extreme values to obtain the results directly. Second, we designed dynamic contrast constraint conditions based on image enhancement characteristics and employed an adaptive genetic a*algorithm to estimate the DoP of backscattering without relying on background regions. Finally, qualitative and quantitative results demonstrate that our method exhibits superior image quality and higher robustness in various turbid water environments, making it suitable not only for underwater targets of different materials but also for underwater media of different turbidity.

关键词： Polarization imaging Underwater polarization de-scattering Dynamic contrast constraint Adaptive genetic a*algorithm

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Sea ice concentration inversion based on data screening and ASI a*algorithm

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MEASUREMENT 2025年 244卷

作者： Wang, Xingdong Sun, Zehao Guo, Zhi Zhao, Yanchuang Wang, Yuhua Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China

To address the issue of low accuracy in sea ice concentration (SIC) inversion caused by external factors affecting the 89 GHz AMSR-2 data used in the Artist Sea Ice (ASI) a*algorithm, a SIC inversion method based on data screening and ASI a*algorithm is proposed (SASI). The SASI a*algorithm is according to the correlation between high- frequency data and low-frequency data, and constructs a screening model for high-frequency data affected by external factors interference. Based on the radiation transfer model, the screened interfered 89 GHz data was corrected, and the entire SIC in Arctic was obtained using the ASI a*algorithm. We compared the SASI a*algorithm with traditional ASI a*algorithms and conducted local validation using Landsat-8 data. The results showed that the SASI a*algorithm improved the accuracy of SIC inversion.

关键词： Arctic Sea ice concentration ASI a*algorithm AMSR-2 Weather filter

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Peer-to-Peer Multi-Energy Trading Among Heterogeneous Building Prosumers via Asynchronous Distributed a*algorithm

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IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 1590-1603页

作者： Jin, Xiaolong Wang, Xiaoyu Jia, Hongjie Mu, Yunfei Wu, Qiu-Wei Wei, Wei Tianjin Univ Key Lab Smart Grid Minist Educ Key Lab Smart Energy & Informat Technol Tianjin Mu Tianjin 300072 Peoples R China StateGrid Jibei Elect Power Co Ltd Res Inst Dept Smart Grid & New Energy Beijing 100045 Peoples R China

Smart buildings in the integrated community energy system (ICES) are normally equipped with distributed energy resources (DERs), thereby creating building prosumers with both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers can bring higher economic benefits for them. Therefore, a P2P multi-energy trading scheme among building prosumers is proposed, which fully explores the flexibility of buildings' heating loads based on the thermal dynamics of buildings with different thermal insulation properties. Each building prosumer is heterogeneous in terms of its computation and communication infrastructures. This results in a heavy computation burden with the traditional centralized method. To improve the computational efficiency for P2P trading among heterogeneous building prosumers, an asynchronous distributed a*algorithm based on alternating direction method of multipliers (ADMM) is developed to enable each prosumer to trade energy asynchronously instead of waiting for the trading information from others with poor infrastructure. This asynchronous procedure integrated with the prediction and anomaly detection steps can further accelerate the convergence speed of P2P trading. Simulation results verify the effectiveness of the proposed trading scheme and the feasibility and solution optimality of the proposed a*algorithm.

关键词： Buildings Heating systems Resistance heating Water heating Optimization Indexes Insulation Electricity Distributed a*algorithms Convex functions P2P multi-energy trading buildings' heating loads heterogeneous asynchronous distributed a*algorithm ADMM

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Alternating minimization differential privacy protection a*algorithm for the novel dual-mode learning tasks model

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Zhao, Pengfei Zhang, Kaili Zhang, Haibin Chen, Haibin Beijing Univ Civil Engn & Architecture Sch Civil & Transportat Engn Beijing 102616 Peoples R China Beijing 2 Middle Sch Beijing 100010 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Beijing Univ Technol Dept Operat Res & Informat Engn Beijing 100124 Peoples R China Qufu Normal Univ Sch Management Sci Rizhao 276800 Shandong Peoples R China Ctr Intelligent Multidimens Data Anal Shatin Hong Kong Sci Pk Hong Kong Peoples R China

The privacy-protected a*algorithm (PPA) is pivotal in the realm of machine learning, especially for handling sensitive data types, such as medical and financial records. PPA enables two distinct operations: data publishing and data analysis, each capable of functioning independently. However, the field lacks a unified framework or an efficient a*algorithm to synergize these operations. This deficiency inspires our current research endeavor. In this paper, we introduce a novel dual-mode empirical risk minimization (D-ERM) model, specifically designed for integrated learning tasks. We also develop an alternating minimization differential privacy protection a*algorithm (AMDPPA) for implementing the D-ERM model. Our theoretical analysis confirms the differential privacy and accuracy of AMDPPA. We validate the a*algorithm's efficacy through numerical experiments using real-world datasets, demonstrating its ability to effectively balance privacy with learning efficiency.

关键词： Differential privacy protection Dual-mode learning Dual-mode empirical risk minimization model Alternating minimization a*algorithm

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Improving river surface flow velocity measurement by coupling adaptive flow direction search a*algorithm with space-time image velocimetry

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JOURNAL OF HYDROLOGY 2025年 653卷

作者： Zhou, Nie Chen, Hua Xu, Chong-Yu Liu, Bingyi Yang, Jing Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Hubei Peoples R China Univ Oslo Dept Geosci N-0316 Oslo Norway Natl Inst Water & Atmospher Res NIWA Christchurch 8011 New Zealand

Space-time image velocimetry (STIV) is a video-based technique for measuring river surface flow velocities and is widely used owing to its simplicity, efficiency, and safety. However, a key limitation of traditional STIV is its reliance on a preset velocity measurement line, which can lead to significant errors when the actual flow direction deviates from this predefined line. To overcome this limitation, a novel adaptive flow direction search a*algorithm based on Hough transform is proposed. The a*algorithm dynamically adjusts the measurement line direction in real time based on the flow conditions at the measurement point, thereby enabling more accurate surface flow velocity measurements. The proposed method was verified using synthetic videos, and its applicability for surface flow velocity was confirmed through comparisons with the traditional fixed-direction method and an acoustic Doppler current profiler (ADCP) both in artificial channels and natural rivers. The results revealed that the proposed method effectively identified and adapted to different flow directions in synthetic videos with a mean absolute error less than 1.0 degrees. Furthermore, with the adaptive flow direction search a*algorithm, the measurement accuracy of the surface flow velocity estimation was significantly improved compared with traditional fixed direction method, with the relative error in the cross-sectional average flow velocity being less than 5 % under both artificial channel and natural river conditions. The proposed adaptive flow direction search a*algorithm can help in enhancing the stability of the STIV technique under complex flow conditions, and provides more precise and reliable river surface flow velocity measurement.

关键词： STIV Flow velocity detection Adaptive flow direction search a*algorithm Hough transform

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Modified affine projection sign a*algorithm for power quality enhancement and metaheuristic-based MPPT in grid-interactive solar PV system: an experimental analysis

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ELECTRICAL ENGINEERING 2025年第2期107卷 1863-1883页

作者： Morey, Meghraj Gupta, Nitin Garg, Man Mohan Kumawat, Manoj Malaviya Natl Inst Technol Jaipur Dept Elect Engn JLN Marg Jaipur 302017 Rajasthan India Natl Inst Technol Delhi Dept Elect Engn GT Karnal Rd Delhi 110036 India

The integration of large amounts of small-scale distributed energy resources such as solar photovoltaics complicated the stability and quality issues in the existing electrical power system. For an electric grid to be sustainable, grid-integrated solar photovoltaic power-generation conversion systems (SPCS) must provide ancillary services including improving power quality, capturing actual power, and maintaining high conversion efficiency. This paper proposes a combine affine projection sign (CAPS) a*algorithm for performing power quality (PQ) ancillary services in a grid-interactive SPCS. The principle objective of the proposed method is to provide steady and reliable operation while mitigating the impact of power quality issues. The proposed CAPS a*algorithm efficiently extracts the active and non-active harmonic free fundamental weight components of the contaminated grid/load currents. These weights enhance reference signal estimation accuracy and provide switching signals for the grid-solar PV integrating voltage source inverter. Furthermore, the characteristics of the solar PV strings exhibit many peaks when exposed to non-uniform irradiation making it difficult to track the global peak power. Therefore, a metaheuristic JAYA-based maximum power point tracking a*algorithm is proposed to overcome this problem. This a*algorithm offers an extremely fast dynamic response with high accuracy and minimal noise in the steady state in contrast to conventional methods, especially under partial shading conditions. The experimental validation is carried out, and results support the effectiveness of the proposed a*algorithms for enhancing PQ functionality under varying scenarios. Additionally, implementing the proposed CAPS controller significantly reduces the harmonic contamination of the grid current, from 23.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgre

关键词： Affine projection sign a*algorithm JAYA a*algorithm Maximum power point tracker Power quality Partial shading condition

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An improved distance vector hop a*algorithm and A* a*algorithm with modified supernova optimizer for 3-dimensional localization in wireless sensor networks

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WIRELESS NETWORKS 2025年第3期31卷 2827-2846页

作者： Muthusamy, Sudha Ruby, Erode Dhanapal Kanmani Arunachalam, Rajesh Chinnappa, Kalamani SNS Coll Engn Dept Elect & Commun Engn Coimbatore 641107 Tamilnadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Elect & Commun Engn Chennai 600062 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 Tamilnadu India Hindusthan Inst Technol Dept Elect & Commun Engn Coimbatore Tamilnadu India

Localization is crucial for accurate data interpretation in a wireless sensor network (WSN). The goal of localization is to locate the nodes with their respective coordinates acquired from anchor nodes. It helps to transfer the information via several nodes in WSN. In WSN, accurate node localization provides diverse benefits and enables large applications. Tracking the accurate position or location of the sensor nodes maximizes the system performance in WSN. However, attaining better localization in WSN is critical, because of the dynamic behavior of wireless communication in networks. Several localization a*algorithms and deep learning (DL) techniques have been developed to enhance the localization accuracy in WSN. These localization a*algorithms face challenges in several networks, particularly indoor communication;they consume more power. Evaluating the optimal value of anchor nodes, identifying scalability, and maximizing node localization in WSN are complex tasks. Distance Vector Hop (DV-Hop) is referred to as the non-ranging-aided 3D positioning approach with more errors and less positioning accuracy. Focusing on these difficulties, a framework for the 3D localization of DV-Hop (3D-DV-Hop) in the WSN is recommended in this work. Hence in this paper, the DV-Hop a*algorithm and A* a*algorithm are introduced to resolve the above-mentioned issues. With the implementation of WSN, the experiments of 3-dimensional (3D) node localization provide significant outcomes. The ideology of the A* a*algorithm and DV-Hop a*algorithm are integrated to enhance the node localization in WSN. The developed model consumes low power, low data-rate communication solutions, and minimal cost. The multi-objective optimization is carried out by Modified Random Value in Supernova Optimizer (MRV-SO) to locally optimize the node coordinates. This optimization process reduces the average localization error to improve its effectiveness. The comparative analysis of the developed MRV-SO model shows 89.971,

关键词： Wireless sensor networks 3D localization Distance vector hop a*algorithm A* a*algorithm Modified random value in supernova optimizer

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A high-order, high-efficiency adaptive time filter a*algorithm for shale reservoir model based on coupled fluid flow with porous media flow

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2025年 146卷

作者： Li, Jian Chen, Lele Qin, Yi Chen, Zhangxin Shaanxi Univ Sci & Technol Sch Math & Data Sci Xian 710021 Shaanxi Peoples R China Eastern Inst Technol Ningbo Inst Digital Twin Ningbo 315200 Peoples R China

In this paper, a third-order time adaptive a*algorithm with less computation, low complexity is provided for shale reservoir model based on coupled fluid flow with porous media flow. This a*algorithm combines a method of three-step linear time filters for simple post-processing and a second-order backward differential formula (BDF2), is third-order accurate in time, and provides no extra computational complexity. At the same time, the time filter method can also be used to damp non-physical oscillations inherent in the BDF2 method, ensuring stability. We prove the a*algorithm's stability of the constant stepsize second-order backward differential formula plus time filter (BDF2-TF) and the third-order convergence properties of the fluid velocity u and hydraulic head phi in the L2 norm. In numerical experiments, this adaptive a*algorithm automatically adjusts a time step in response to the varying characteristics of different models, ensuring that errors are maintained within acceptable limits. The a*algorithm addresses the issue that high-order a*algorithms may select inappropriate time steps, resulting in instability or reduced accuracy of a numerical solution, and thereby it enhances calculation accuracy and efficiency. We perform three-dimensional numerical tests to examine the BDF2-TF a*algorithm's effectiveness, stability, and third-order convergence. Simultaneously, a simplified model is employed to simulate the process of shale oil extraction from reservoirs, further demonstrating the a*algorithm's practical applicability.

关键词： Time filters Adaptive a*algorithm Third-order Shale reservoir

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Linear Convergence of Asynchronous Gradient Push a*algorithm for Distributed Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 2147-2159页

作者： Li, Huaqing Cheng, Huqiang Lu, Qingguo Wang, Zheng Huang, Tingwen Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

This article focuses on multiagent distributed asynchronous optimization over directed networks where each agent can only access its individual local function, and the aggregate aim is to minimize the cumulative sum of all local functions. Considering the asynchrony among the agents, we develop an a*algorithm in which agents compute and communicate individually, without any form of synchronized coordination. Agents perform their local updates by local communication with their immediate neighbors, and this may involve the use of stale information. Since asynchrony naturally leads to latency or packet loss, an asynchronous robust gradient tracking mechanism is developed to guarantee estimating the average of agents' gradients precisely. Moreover, it employs uncoordinated step-sizes which are more flexible and general than constant or decaying step-size. When the global objective is strongly convex and the local objectives have Lipschitz-continuous gradients, we prove that each agent executing the asynchronous a*algorithm linearly converges to the consensus optimal point at an O(lambda(k)) rate, where lambda is an element of (0, 1) is convergence factor and k represents the iteration number, with a step satisfying a tight explicit upper bound. Numerical experiments demonstrate that our a*algorithm has better advantages over the state-of-the-art asynchronous a*algorithms.

关键词： Convergence Optimization Protocols System recovery Linear programming Electronic mail Delays Vectors Synchronization Fasteners Asynchronous a*algorithm delay distributed optimization linear convergence

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Low-carbon berth allocation: An analysis of the effectiveness of an enhanced multi-objective artificial bee colony a*algorithm based on a case study

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OCEAN & COASTAL MANAGEMENT 2025年 261卷

作者： Ma, Xiaomeng Pu, Xujin Jiangnan Univ Sch Business Wuxi 214122 Peoples R China

Marine terminals are essential components of international trade networks and global markets. To guarantee the rapid and consistent growth in maritime trade, managers must employ suitable techniques to handle operational challenges and meet market needs. One of the critical decisions in operational planning is the allocation of berths. A well-designed berth allocation plan can greatly boost the productivity and competitiveness of seaports. Despite the extensive research on berth allocation, there remains a notable gap in studies focusing on low-carbon berth allocation. As energy shortages and global warming intensify, low-carbon has increasingly become key terms across various sectors. Under the circumstances, this work addresses a multi-objective stochastic berth allocation problem for minimizing the average carbon emission and total service time. Firstly, a stochastic programming method is employed to formulate the uncertain arrival time and operation time of vessels, then a multi-objective chance-constrained programming model is constructed to formulate the studied problem. Secondly, an enhanced multi-objective artificial bee colony a*algorithm incorporating stochastic simulation (EMOABC) is specially designed. Finally, a large number of comparison experiments between EMOABC and nondominated sorting genetic a*algorithm II (NSGA-II) are performed. Through observing and analyzing the experimental results, two conclusions are acquired as follows: (i) EMOABC obtains the smaller IGD values and larger HV values than NSGA-II on all the test instances, indicating that it has better performance than NSGA-II for solving the considered problem;and (ii) EMOABC uses less running time in dealing with test problems of different scales compared to NSGA-II, suggesting that it has lower computational complexity than NSGA-II.

关键词： Berth allocation Carbon emission Multi-objective optimization Artificial bee colony a*algorithm Marine pollution

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