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Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Zhehan Zhong Jixue Wang Jinxia Zhang Bing Liu Heng Liang Xiaolei Li Shuo Yang Zhenyu Gai Zheng Li Electric Power Planning & Engineering Institute Beijing China Department of Energy and Power Engineering Tsinghua University Beijing China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

This study focuses on the synergistic use of electricity and cooling energy within data centers, integrating photovoltaic, wind power, air conditioning, electricity and cold storage. Firstly, a model for renewable energy based electricity and cold energy supply has been established, using the economic index as the objective function, with energy and power balances serving as constraints, and incorporating actual data from Qingyang of China as boundary conditions. Secondly, the model is solved by the mixed-integer linear programming (MILP) in Gurobi to determine the minimum net present value of cost and the optimal scales of its components. Finally, a comparative analysis reveals that compared to configuring electricity or cold storage alone, the coordinated configuration of both of them can significantly improve economic performance. This proposed scheme outperforms the current schemes in Qingyang, with a cost per kilowatt-hour of electricity of 0.3529 yuan $/ \mathrm{k W h}$.

关键词： Photovoltaic systems Air conditioning Data centers Costs Electricity Atmospheric modeling Wind power generation linear programming Boundary conditions Mixed integer linear programming

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A High-Dimensional Clustering Algorithm and Its Application in the Siting of New Energy Facilities

A High-Dimensional Clustering Algorithm and Its Application ...

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2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS)

作者： Yuxuan Liu Senyuan Chen Chuan’an Li Mengying Niu Huiying Zhou Housu Zhao School of Computer Engineering Jiangsu University of Technology Changzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The paper proposes a high-dimensional aggregation algorithm for analyzing the objective function in multi-objective location, which is applied to solve the problem of locating new energy facilities. The algorithm consists of three steps: Firstly, all facilities within the area are defined as decision variables and their corresponding sub-objective functions are calculated. These decision variables and sub-objective functions are then projected onto facility points in a high-dimensional space, where each dimension corresponds to a sub-objective function value. The Euclidean distance between these high-dimensional facility points reflects the differences among different facilities in the multi-objective optimization environment. Secondly, an n-DFPS (n-dimensional facility point seasonal algorithm) is designed to find the Pareto point in this high-dimensional space by minimizing the sum of Euclidean distances between that point and all other high-dimensional facility points. The facilities are weighted based on their Euclidean distances from both the high-dimensional facility point and the high-dimensional Pareto median point. Finally, all facility points are projected onto weighted facility points on a two-dimensional plane according to their latitude, longitude, and weight information; 2-PGSA (2-dimensional facility point seasonal algorithm) is used to identify Steiner points on this plane as reasonable locations for new energy facilities. To validate its effectiveness, an industrial and commercial energy storage station location case study is conducted using simulation experiments comparing it with classical location algorithms; results demonstrate superiority of our proposed algorithm.

关键词： Steiner trees Accuracy Simulation Software algorithms Clustering algorithms Network security linear programming Stability analysis Optimization Software engineering

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A Local Search Algorithm Leveraging the Reasoning Abilities of Large Language Models for Solving Urban Charging Station Location Problem

A Local Search Algorithm Leveraging the Reasoning Abilities ...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Wenhan Shao Yan Jiang Feng Zhao Yanbin Jiao Jun Zhang Yong Su Yiping Zhou Xiaowei Zhang State Grid Information & Telecommunication Group Co. Ltd. Beijing China Beijing SGITG Accenture Information Technology Co. Ltd. Beijing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

To enhance the charging experience for electric vehicle users and facilitate the widespread adoption of electric vehicles, the paper introduces the urban charging station location problem (UCSLP), which has significant practical implications in the field of electric power. The UCSLP involves two conflicting objective functions: minimizing user waiting time and maximizing the revenue of charging stations. To comprehensively evaluate the solution quality in this multi-objective context, we propose a local search algorithm that leverages the reasoning abilities of large language models. Experimental results demonstrate that large language models can effectively understand the multiple objective functions of the problem and thoroughly assess solution quality. The reasoning abilities of the large language model improve the effectiveness of the local search algorithm in solving the UCSLP and have potential applications for other multi-objective optimization problems.

关键词： Electric potential Large language models Charging stations Search problems Electric vehicles linear programming Cognition Smart grids Power systems Optimization

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Analysis of Crop Planting Strategies 24

Analysis of Crop Planting Strategies

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Yunmei Wang Yuhan Yu Junxi Chen Changyue Wu Weiwei Jiang Xin Gao Yunnan agricultural university Kunming Yunnan China

ISBN: (纸本)9798400711831

In most mountainous areas of China, cultivated land resources are scarce, and natural conditions such as climate and topography have a great impact on crop cultivation. In order to improve the economic benefits of cultivated land in mountainous areas, it is particularly important to optimize planting strategies to cope with complex farming conditions and restrictions. Based on the arable land area, crop types and planting seasons of open air and greenhouse in a village in the mountains of North China, as well as the planting situation, per mu yield, planting cost and sales unit price in 2023, this paper studies the maximum utilization of land resources, establishes a planting strategy optimization model, and uses dynamic programming, Monte Carlo, robust optimization, sensitivity test and other methods. To solve the problems of scientific land management, excess sale, multi-factor time fluctuation, crop substitution, complementarity and correlation, so as to improve the agricultural production efficiency in mountainous areas and provide a scientific basis for the modernization and sustainable development of rural economy.

关键词： linear programming

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Model-Free Robust Height Trajectory Tracking Control for Nonlinear LAVs

Model-Free Robust Height Trajectory Tracking Control for Non...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Jiaxing Li Hailiang Wang East China University of Science and Technology Shanghai China Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Faced with the nonlinear low altitude vehicle (LAV) trajectory tracking control problem, in order to track the desired trajectory more accurately and respond to the changes of inputs faster, this paper proposes a model-free adaptive control method under the data-driven framework, which solves the problem of controlling the flight altitude of low altitude vehicles under the scenario of Dos network attack. In this paper, the data mapping of LAV is established based on the tight-format dynamic linear programming technique under the compressed mapping criterion. Firstly, the simulation of normal flight control is carried out for low altitude vehicles with different flight parameters, and then the simulation of cyber-attacks under three different scenarios, namely, momentary communication interruption, temporal communication interruption, and probabilistic interruption of communication is carried out, and it is verified that the model has a faster trajectory speed, a more accurate trajectory precision and a better stability.

关键词： Adaptation models Accuracy Trajectory tracking Stability criteria linear programming Data models Trajectory Dynamic programming Vehicle dynamics Aerospace control

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RPL Protocol Performance Analysis Across Multiple IoT Topologies

RPL Protocol Performance Analysis Across Multiple IoT Topolo...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Suneeta Mishra Vidushi Sharma Vimlesh Kr School of ICT Gautam Buddha University Greater Noida India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The Internet of Things (IoT) network topology model is categorized as a Low-Power and Lossy Network (LLN). With the advent of IoT., the need for specialized protocols to address the unique challenges of LLNs, such as constrained nodes (e.g., battery-powered sensors), unreliable and dynamic wireless links, and scalability requirements for dense networks, became increasingly apparent. In 2012, the IETF ROLL (Routing Over Low Power and Lossy Networks) working group officially standardized RPL in RFC 6550, titled “RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks.” RPL is designed to provide efficient routing solutions for LLNs across various application domains, including industrial automation, smart cities, and home automation. It employs objective functions, such as OF0 (Objective Function Zero) and MRHOF (Minimum Rank with Hysteresis Objective Function), to determine routes from source nodes to destination nodes based on metrics like ETX and energy. Previous studies on RPL were limited in their exploration of topological variations, which constrained the understanding of RPL's adaptability to different network structures. Additionally, earlier works lacked detailed analysis of hybrid topologies and did not adequately address the balance between energy consumption and routing efficiency. Performance assessments were frequently confined to a narrow set of metrics, neglecting crucial factors such as control traffic overhead, end-to-end delay and throughput, which are essential for practical IoT deployments. This study bridges these gaps by evaluating RPL under diverse topologies, proposing a novel hybrid topology, and incorporating a broader range of performance metrics for a holistic analysis. The proposed work introduces a hybrid star-tree topology, offering an innovative approach to optimize routing efficiency, by combining the hierarchical structure of the tree topology with the robustness of the star topology, the hybrid model achieves improved scalabili

关键词： Measurement Wireless communication Wireless sensor networks Network topology Scalability Routing linear programming Routing protocols Topology Internet of Things

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ETS: Efficient Tree Search for Inference-Time Scaling

arXiv

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arXiv 2025年

作者： Hooper, Coleman Kim, Sehoon Moon, Suhong Dilmen, Kerem Maheswaran, Monishwaran Lee, Nicholas Mahoney, Michael W. Shao, Sophia Keutzer, Kurt Gholami, Amir UC Berkeley United States ICSI LBNL

Test-time compute scaling has emerged as a new axis along which to improve model accuracy, where additional computation is used at inference time to allow the model to think longer for more challenging problems. One promising approach for test-time compute scaling is search against a process reward model, where a model generates multiple potential candidates at each step of the search, and these partial trajectories are then scored by a separate reward model in order to guide the search process. The diversity of trajectories in the tree search process affects the accuracy of the search, since increasing diversity promotes more exploration. However, this diversity comes at a cost, as divergent trajectories have less KV sharing, which means they consume more memory and slow down the search process. Previous search methods either do not perform sufficient exploration, or else explore diverse trajectories but have high latency. We address this challenge by proposing Efficient Tree Search (ETS), which promotes KV sharing by pruning redundant trajectories while maintaining necessary diverse trajectories. ETS incorporates a linear programming cost model to promote KV cache sharing by penalizing the number of nodes retained, while incorporating a semantic coverage term into the cost model to ensure that we retain trajectories which are semantically different. We demonstrate how ETS can achieve 1.8× reduction in average KV cache size during the search process, leading to 1.4× increased throughput relative to prior state-of-the-art methods, with minimal accuracy degradation and without requiring any custom kernel implementation. Code is available at: https://***/SqueezeAILab/ETS. © 2025, CC BY.

关键词： linear programming

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Optimization of Crop Planting Based on Genetic Algorithm and Monte Carlo Simulation

Optimization of Crop Planting Based on Genetic Algorithm and...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Wanhua Zhang Jie Chen Xin Jiang Harbin University of Commerce Harbin China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This study focuses on the optimization analysis of crop cultivation in mountainous villages using genetic algorithm and Monte Carlo simulation, aiming to formulate the optimal crop cultivation scenarios for the period of 2024–2030 through mathematical modeling and optimization algorithms. The study first considered two possible scenarios (stagnant sales and price reductions) and developed a genetic algorithm-based total return maximization model to solve the optimal planting scheme for different sales scenarios in the next five years. Next, the uncertainty of various economic factors is analyzed under an uncertainty environment, and the Monte Carlo simulation algorithm is used to solve the optimal planting scheme. Finally, the optimal planting plan is further optimized by linear programming methods, considering the complementarity and substitutability between crops. The research in this paper not only provides a feasible optimization path for agricultural cultivation in a specific region, but also provides a valuable solution to cope with the uncertainty and risk in agricultural production.

关键词： Power engineering Monte Carlo methods Uncertainty Crops Optimization methods Production linear programming Mathematical models Optimization Genetic algorithms

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Enlarging the DA of Nonlinear Polynomial Controlled Systems via Jaya Optimization

Enlarging the DA of Nonlinear Polynomial Controlled Systems ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Messaoud Aloui Faiçal Hamidi Houssem Jerbi Mohamed Aoun Mohamed Naceur Abdelkrim Laboratory “Modélisation Analyse et Commande des Systèmes” University of Gabes Gabes Tunisia Laboratory of Information Communication and Knowledge Sciences and Techniques University of Western Brittany Lorient France Department of Industrial Engineering University of Ha'il College of Engineering Hail KSA

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The last few years marked remarkable progress in developing stability analysis techniques directed at nonlinear systems. In this context, this work presents a Lyapunov-based method to estimate the region of stability of nonlinear systems. Based on LMI techniques and JAYA optimization, this work provides a maximized volume of the DA. The LMI technique is used to get the largest estimation of the DA from a given Lyapunov function and input controller. The JAYA algorithm is introduced to attain a maximized volume of the DA through an optimal selection of the Lyapunov function's parameters and input controller coefficients.

关键词： Estimation linear programming Control systems Stability analysis Polynomials Nonlinear systems Optimization Lyapunov methods

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Ai-Powered Pencil Strokes: Face Sketch Synthesis Using Cyclegan and Style Transfer

Ai-Powered Pencil Strokes: Face Sketch Synthesis Using Cycle...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Sighakolli Dheeraj Venkata Sai Simma Sathwik Solleti Venkata Dhiraj Peddi Deekshith C. R Lekshmi Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

This paper introduces an AI-driven framework for face sketch synthesis using Cycle Generative Adversarial Networks (CycleGAN), enhanced with a style transfer module to improve the perceptual quality of generated sketches. Unlike conventional edge-detection or handcrafted filter-based techniques, our method leverages adversarial learning to capture intricate details, shading, and texture while maintaining structural coherence. By incorporating perceptual and edgeaware constraints into the objective function, the model enhances sketch quality. Qualitative analysis reveals progressive refinement over training epochs, while quantitative evaluation using the Structural Similarity Index (SSIM) confirms improved structural and perceptual similarity. The stable convergence of training loss further validates the model's effectiveness. Experimental results demonstrate that our approach generates highly realistic and visually appealing sketches, surpassing traditional methods in both fidelity and perceptual quality. Future work will focus on enhancing fine details and improving generalization across diverse datasets.

关键词： Training Computational modeling Computer architecture Generative adversarial networks linear programming Adversarial machine learning Robustness Indexes Faces Convergence

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