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Heuristic and random search algorithm in optimization of route planning for Robot's geomagnetic navigation

COMPUTER COMMUNICATIONS 2020年第0期154卷 12-17页

作者： Xu, Yan Guan, Guofei Song, Qingwu Jiang, Chao Wang, Lihui Jiangsu Frontier Elect Technol Co Ltd Nanjing 211102 Peoples R China Southeast Univ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Techno Minist Educ Nanjing 210096 Peoples R China

To improve the efficiency and accuracy, a new combination algorithm for route planning is proposed, by considering underwater geomagnetic matching navigation area and distribution of environmental constraints. Firstly, with geomagnetic navigation matching regions, Dijkstra algorithm can obtain the primary route points. Secondly, the environmental constraints models are built and normalized, and the route planning environment constrained cost model is established. Thirdly, with the relationships between time, function relation, constraint condition and variable in the environment constrained cost model, the particle swarm optimization algorithm is introduced. With the primary route pints, the route planning is transformed into route optimization. Finally, the primary route points are used as the initial input of the particle swarm optimization algorithm, then the methods of selecting the inertia weight of the particle swarm and the particle coding are improved. The optimal route planning of Dijkstra algorithm and particle swarm optimization is realized. Simulation results demonstrate that the particle size of the search space can get a minimized evaluation, more narrowed search range and higher efficient search. The combination algorithm guarantees the global optimal while ensures the local optimal, then, the non-matching navigation areas can be effectively avoided, and efficient route planning functions can be achieved.

关键词： Geomagnetic field navigation Route planning Geomagnetic navigation matching area Environmental constraint modeling optimization algorithm

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Joint DOA and distance estimation via spatial diversity wideband signal synthesis calibrated by distance parameter

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DIGITAL SIGNAL PROCESSING 2025年 157卷

作者： Zhang, Wentao Miao, Chen Wu, Wen Nanjing Univ Sci & Technol Key Lab Near Range RF Sensing ICs & Microsyst Nanjing 210094 Peoples R China Southeast Univ State Key Lab Millimetre Waves Nanjing 210094 Peoples R China

Wideband signal synthesis is a technique designed to achieve large bandwidth detection capabilities by utilizing multiple relatively narrow bandwidth signals. Traditional single-antenna, time-diversity wideband synthesis method is inefficient, which has led to using Multiple-Input Multiple-Output (MIMO) architectures to introduce spatial diversity. However, this method introduces challenges such as frequency-phase inconsistencies in the selfmixing signals from different array elements due to differences in wave travel distances. In previous research, we studied the calibration of frequency and phase for synthesized sub-signals using DOA parameters, which required additional high-precision direction of arrival (DOA) estimation algorithms. In contrast, this paper proposes a novel spatial-diversity wideband synthesis method that utilizes distance parameter calibration for joint DOA and distance estimation (JDDE). A key advantage of this method is the relative simplicity of obtaining distance parameters. To address the issue where the accuracy of distance parameters does not meet the requirements for synthesis, we proposed a grid search synthesis method in this paper. Furthermore, we introduced a search synthesis based on optimization algorithms to reduce computational load and enhance the synthesis performance. Theoretical analysis and simulations confirm the high accuracy of our JDDE method. Under conditions of high signal-to-noise ratios, our method significantly reduces the DOA's root mean square error-approximately halving it compared to the multiple signal classification algorithm within the same wideband self-mixing framework. Additionally, both distance resolution and range accuracy exceed those achieved with presynthesis methods.

关键词： Doa estimation Distance estimation Wideband signal synthesis optimization algorithm

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Optimal energy management system and techno-economic assessment in Urban and Rural AC microgrids

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JOURNAL OF ENERGY STORAGE 2025年 114卷

作者： Montano, Jhon Gonzalez-Montoya, Daniel Candelo-Becerra, John E. Herrera-Jaramillo, Diego A. Inst Tecnol Metropolitano Dept Elect & Telecommun Medellin 050028 Colombia Univ Nacl Colombia Sede Medellin Dept Energia Electr & Automat Medellin 050034 Colombia Inst Univ Envigado Lab Desarrollo Tecnol Sostenible Envigado 055420 Colombia

This paper introduces an energy management system that incorporates a model for managing urban and rural alternating current (AC) microgrids (MGs), integrating renewable energy sources and energy storage systems. The proposed model aims to improve key technical, economic, and environmental performance indicators. It employs a mono-objective optimization framework, focusing on the independent minimization of operating costs, power losses, and CO2 emissions. To solve the optimization problem, seven bio-inspired algorithms are implemented and compared: Black Hole Optimizer (BHO), Crow Search algorithm (CSA), Salp Swarm algorithm (SSA), Equilibrium Optimizer (EO), Generalized Normal Distribution Optimizer (GNDO), Particle Swarm optimization (PSO), and Grasshopper optimization algorithm (GOA). The effectiveness of the proposed model is validated through a comparative analysis against a baseline scenario that represents conventional MG operation without optimization. This baseline scenario includes photovoltaic distributed generators and energy storage systems operating under static dispatch strategies. The results demonstrate that EO, SSA, and GNDO are the most effective algorithms for optimizing the specified objectives. For urban MGs, the proposed model achieves reductions of up to 7.16% in power losses, 0.163% infixed costs, 1.436% invariable costs, and 0.165% in CO2 emissions when compared to the baseline. Similarly, for rural MGs, the proposed approach yields reductions of 10.938% in power losses, 0.095% in energy costs, and 0.145% in CO2 emissions relative to the baseline scenario. These findings confirm the innovation and effectiveness of the proposed energy management model and its optimization algorithms. The study highlights the model's capability to ensure technical efficiency while significantly reducing economic and environmental impacts. Moreover, the adaptability of the model to both urban and rural settings demonstrates its potential as a robust framework

关键词： Energy management system Techno-economic analysis AC MGs optimization algorithm Variable power demand Variable renewable generation

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Development of a Performance-Based Approach for Optimizing Sensor Placement in Building Fire Detection

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FIRE TECHNOLOGY 2025年 1-28页

作者： Fang, Hongqiang Xu, Mingjun Chen, Xiao Lo, S. M. Wan, Man Pun Univ Sci & Technol China State Key Lab Fire Sci Hefei 230026 Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore

Fire detection systems play a vital role in ensuring effective fire protection within buildings. At present, the placement of fire detectors is guided by established codes and standards, which specify maximum coverage areas for each detector. Building engineers typically follow these guidelines, positioning detectors strategically to achieve full coverage. While this approach provides adequate protection, it fails to consider the impact of varying environmental factors in different settings and accurately assess the actual performance of fire detection systems. This limitation is particularly evident in unique spaces like warehouses, where fire types and potential ignition locations may differ significantly from those in conventional environments, necessitating a more customized approach to sensor placement. To address this issue, a fire detection performance-based sensor placement optimization (FDPB-SPO) approach is proposed. This methodology integrates numerical datasets generated from multiple simulated fire scenarios with advanced optimization algorithms to evaluate fire sensor placement performance and identify the optimal arrangement. The optimization process balances effective fire detection with compliance to code requirements, ensuring both enhanced performance and practical applicability. A case study evaluating this proposed approach demonstrates its effectiveness in determining the more appropriate arrangement for fire detection. Additionally, integrating it with the Genetic algorithm (GA) yields an optimized solution that enhances fire detection performance and reliability. These findings highlight the potential of the FDPB-SPO approach in advancing sensor placement strategies and contributing to the development of future fire detection system standards.

关键词： Building fire safety Fire detection Sensor placement Performance-based design optimization algorithm

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Intelligent Network Selection Mechanisms in the Internet of Everything System

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IEEE ACCESS 2025年 13卷 9666-9678页

作者： Singh, Indrasen Munjal, Meenakshi Vellore Inst Technol Sch Elect Engn Dept Embedded Technol Vellore 632014 Tamil Nadu India Chandigarh Univ Univ Inst Engn ECE Dept Sahibzada Ajit Singh Naga 140413 India

In the Internet of Everything (IoE) ecosystem, it is necessary for connected devices to move between different access points seamlessly. As IoE is deployed in diverse environments, such as smart cities, industrial settings, and vehicular networks, ensuring reliable connectivity and uninterrupted services across different networks becomes important. As part of the IoE framework, this research paper focuses on developing and evaluating intelligent handoff decision mechanisms. So, in this work, the optimal handoff decision technique is proposed, which transmits the information in a fast manner. The Fuzzy Analytical Hierarchical Process (FAHP) and Analytical Hierarchical Process (AHP) two-user preference calculation algorithms are used. Then, the user preference is included in the prospect algorithm and the optimal network is selected. After that, the result of the prospect algorithm is compared with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Rational Analysis (GRA), and Multiplicative Exponent Weighting (MEW) algorithms in terms of network scores and the number of handoffs. The advantage of network selection using a prospect algorithm is that it will decrease the failure rate and increase the data transfer and user experience. These advantages deliver seamless connectivity for a diverse range of applications and devices using an IoE infrastructure that is more robust and reliable. The network selected by combining different techniques is the optimal network because it selects the most efficient or effective network.

关键词： SIM card Internet of Things Real-time systems User experience Interference Handover Ecosystems Delays Wireless fidelity WiMAX Handoff heterogeneous network Internet of Everything (IoE) network selection optimization algorithm prospect theory

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Integrating Analytical Simulations With Regression Learning: Advancing Efficiency in Energy and Water Use in Sugar Production

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JOURNAL OF FOOD PROCESS ENGINEERING 2025年第2期48卷

作者： Azizi, Mohammad Mosharaf-Dehkordi, Mehdi Fouladi, Nourbaksh Kazanci, Caner Univ Isfahan Fac Engn Dept Mech Engn Esfahan Iran Univ Georgia Coll Engn Athens GA USA

Energy and water consumption are critically important in the sugar industry. In this context, the heat exchanger network of a target sugar factory has been modeled and optimized, as this sector is the primary consumer of energy and water. A key innovation of this work lies in the coupling of interacting components within the model, leading to a more comprehensive framework compared to previous models in the literature. Some sections of the system are modeled using analytical interpretations, while others are developed through a regression learning process utilizing statistical data. This integration of analytical formulation and data-driven modeling represents another significant advancement in this research. The resulting model demonstrates acceptable accuracy for most measurable parameters, with an average deviation of approximately 4%. The optimization results indicate that certain parameters, such as the cooling pool evaporation rate, exhibit considerable flexibility, allowing optimization algorithms to converge more easily. Conversely, other parameters, such as the vapor fed to the exchangers, are more rigid, which restricts the freedom of the optimization process. Moreover, the effectiveness of the elements within the optimization target function is crucial for identifying the optimal point. Overall, minimizing energy consumption and water usage simultaneously presents a significant challenge, necessitating careful consideration in determining which optimal point is most practical.

关键词： heat exchanger network multiple evaporation optimization algorithm regression learner

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Air quality index prediction through TimeGAN data recovery and PSO-optimized VMD-deep learning framework

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Wang, Kenan Yang, Tianning Kong, Shanshan Li, Mingduo North China Univ Sci & Technol Coll Sci Tangshan 063210 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China

With the rapid development of the economy, air pollution has become increasingly severe. Accurate prediction of the Air Quality Index (AQI) is crucial for safeguarding public health and the environment. However, AQI time series exhibit strong randomness and volatility, posing challenges for traditional forecasting methods to achieve precise AQI predictions. Therefore, we propose a new AQI hybrid prediction model, TG-Hybrid model, which integrates generative artificial intelligence, signal decomposition techniques, artificial intelligence methods, and optimization algorithms. In the proposed model, missing values in the data are handled using generative adversarial networks, effectively addressing the issue of a large number of missing values in time series data. Autoregressive integrated moving average is employed to forecast the linear components of the data, while variational mode decomposition decomposes AQI into multiple modes. Particle swarm optimization is used to combine the prediction results of convolutional neural network combined with bidirectional long short-term memory and extreme gradient boosting. Additionally, AQI prediction experiments were conducted using air pollution data from Tangshan and Beijing, and compared with fifteen other models. The results indicate that the root mean square error for Tangshan and Beijing are 6.407 and 7.485, respectively, significantly outperforming other baseline models.

关键词： Time Generative Adversarial Network Air Quality Index Prediction Deep learning model optimization algorithm Variational Mode Decomposition

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Performance investigation and power prediction of a trifunctional heat pump system

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APPLIED THERMAL ENGINEERING 2025年 266卷

作者： Han, Youhua Zhang, Jili Ma, Liangdong Lu, Shixiang Lv, Hui Gong, Jian Nanchang Hangkong Univ Sch Civil Engn & Transportat Nanchang 330063 Jiangxi Peoples R China Dalian Univ Technol Dept Civil Engn Dalian 116024 Liaoning Peoples R China Zhongyuan Univ Technol Sch Energy & Environm Zhengzhou 450007 Peoples R China

In this work, a trifunctional direct-expansion photovoltaic thermal heat pump system was constructed to provide domestic hot water, photovoltaic power, and chilled water. Experiments and power prediction models of the trifunctional direct-expansion photovoltaic thermal heat pump system were conducted. First, the photovoltaic, heating, and cooling performances were investigated to evaluate the comprehensive performance of the system with a vapor injection cycle. The experimental results revealed that the average electrical power and photoelectric conversion efficiency of the photovoltaic thermal array were 1.01 kW and 14.71 %, respectively. The average heating power, coefficient of performance, equivalent coefficient of performance, cooling power, and energy efficiency ratio of the system were 7.46 kW, 3.64, 6.87, 4.07 kW, and 1.94, respectively. The electrical and heating performance of the system was sensitive to solar irradiation, and the cooling performance was sensitive to the ambient temperature and wind speed. Afterwards, based on the experimental data, a back- propagation neural network model combined with particle swarm optimization, a genetic algorithm, and a time correlation series were proposed to forecast the tri-generation of the direct-expansion photovoltaic thermal heat pump system. The prediction results show that the proposed neural network prediction model has high prediction accuracy and robustness. The normalized root mean square error and mean absolute percentage error of the model were 2.00 % and 2.23 %, respectively, for electrical power prediction;1.03 % and 1.28 %, respectively, for heating power prediction;and 3.34 % and 4.29 %, respectively, for cooling power prediction.

关键词： Photovoltaic thermal heat pump system Tri-generation performance Experimental study Artificial neural network optimization algorithm Prediction accuracy

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Multi-Agent Grouping and Routing with Cooperative Tasks

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第3期22卷 163-176页

作者： Choi, Euihyeon Ahn, Jaemyung Korea Adv Inst Sci & Technol KAIST Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea

This paper introduces a grouping and routing problem of multiple agents for cooperative missions. The introduced problem, referred to as the Vehicle Grouping and Routing Problem with Profits, aims to maximize the total reward obtained by conducting a multi-agent mission (e.g., cooperative reconnaissance) while reducing its makespan by appropriately grouping the agents and determining their routes under operational constraints (e.g., fuel, endurance). A mixed-integer linear programming formulation and a conservative column generation-based solution procedure for the problem are proposed. A case study with homogeneous and heterogeneous agents and numerical experiments involving a cooperative reconnaissance mission with multiple unmanned aerial vehicles demonstrate the validity of the proposed formulation and solution procedure.

关键词： Unmanned Aerial Vehicle Mixed Integer Linear Programming optimization algorithm Flight Dynamics Autonomous Technology Planetary Science and Exploration Aerospace Engineering

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Techno-economic optimization of standalone hybrid power systems in context of intelligent computational multi-objective algorithms

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ENERGY REPORTS 2022年 8卷 11661-11674页

作者： Mohammed, Yekini Suberu Adetokun, Bukola Babatunde Oghorada, Oghenewvogaga Oshiga, Omotayo Nile Univ Nigeria Fac Engn Dept Elect & Elect Engn Abuja Nigeria Fed Polytech Nasarawa Sch Engn Technol Dept Elect Elect Engn Technol Nasarawa Nasarawa State Nigeria

The use of renewable energy (RE) for meeting some load power demand in the present global developmental dealings is realistically unavoidable. However, many challenges conspicuously stand the way of RE penetration into the global power sector. Some of the perceived problems require aggressive research attentions. The utilization of single RE energy structure for the supply of electricity in off-grid isolated communities is usually not a technically dependable system with to regards reliability, security and stability. The core challenge is usually connected to some spontaneous variable weather conditions. It is based on this perspective that the implementation of integrated hybrid RE becomes a promising solution for mitigation of RE intermittent behaviors. In this study, an autonomous hybrid energy system was examined based on simulations for optimal sizing configurations of solar photovoltaic (PV), wind turbine (WT), diesel generator (DG) and battery storage (BS) system. Modern intelligent optimization algorithms of Ant Colony optimization (ACO), Flower Pollination algorithm (FPA), Genetic algorithm (GA) and Particle Swarm optimization (PSO) were applied for providing solutions to the set of selected focal technoeconomic objectives in the framework of this study. Compare with others, FPA provided better results in terms of the net present cost (NPC), cost of energy (COE) and deficit power supply probability (DPSP). The proposed hybrid power systems are configured in four different scenarios: PV/BS, PV/DG/BS, PV/WT/BS and PV/WT/DG/BS. It was consequently established that the configuration of PV/DG/BS with NPC of $85112.08, COE of 0.145 $/kWh and zero DPSP gave the best overall technoeconomic results through the FPA optimization technique.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND

关键词： Battery storage Cost of energy Electricity Hybrid system optimization algorithm Renewable energy

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