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A Multi-regional Implementation Strategy for Economic Dispatch of Virtual Power Plants

A Multi-regional Implementation Strategy for Economic Dispat...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Fei Wang Chao Mu Ming Fan Qiaorong Zhan State Grid Liaoning Electric Power Co. Ltd Huludao Power Supply Company School of Artificial Intelligence Shenyang University of Technology

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

To improve the efficiency of distributed energy use, a comprehensive optimization strategy for sub-regional virtual power stations is proposed. Firstly, the traditional single virtual power plant model and its application are analysed; Secondly, a multi-region virtual power plant structure map is constructed to finely manage wind energy, photovoltaic and other resources in different geographical areas through virtual power plant partitioning; Moreover, efficient integration of regional energy sources and inter-regional power complementation are achieved through the interconnection of integrated energy systems in each region; After that, by aggregating the joint operation among the sub-virtual power plants, the capacity of renewable energy consumption is improved in order to achieve the goal of maximising the operational efficiency. Next, a complementary strategy for parallel partitioning of virtual power plants based on molecular differential evolution is proposed for high-dimensional nonlinear objective functions, to address the shortcomings of ordinary optimisation algorithms in terms of long computation time and slow convergence. Finally, three regions within the VPP are simulated as examples to verify the effectiveness of the strategy proposed in this paper.

关键词： Photovoltaic systems Renewable energy sources Wind energy System integration Virtual power plants Low carbon economy Linear programming partitioning algorithms Optimization Periodic structures

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Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks

Joint Message Detection, Channel, and User Position Estimati...

引用

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Eleni Gkiouzepi Burak Çakmak Manfred Opper Giuseppe Caire Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Institute of Mathematics University of Potsdam Germany Centre for Systems Modeling and Quantitative Biomedicine University of Birmingham United Kingdom

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We consider unsourced random access (uRA) in user-centric cell-free (CF) wireless networks, where random access users send codewords from a common codebook during specifically dedicated random access channel (RACH) slots. The system is conceptually similar to the so-called 2-step RACH currently discussed in 3GPP standardization. In order to cope with the distributed and CF nature of the network, we propose to partition the network coverage area into zones (referred to as "locations") and assign an uRA codebook to each location, such that users in a certain location make use of the associated codebook. The centralized uRA decoder makes use of the multisource AMP algorithm recently proposed by the authors. This yields at once the list of active uRA codewords, an estimate of the corresponding channel vectors, and an estimate of the active users’ position. We show excellent performance of this approach and perfect agreement with the rigorous theoretical "state evolution" analysis. We also show that the proposed "location-based" partitioned codebook approach significantly outperforms a baseline system with a single non-partitioned uRA codebook.

关键词： Wireless networks Message passing Channel estimation Signal processing algorithms Estimation Standardization Signal processing Vectors partitioning algorithms Decoding

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Task Scheduling Algorithm for Multifunctional Integration System Based on Dynamic Aperture Partition Antenna

Task Scheduling Algorithm for Multifunctional Integration Sy...

引用

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Juan Rong Liyuan Zhu Jiayun Chang Dandi Lao Fei Huang Jing Wang Shanghai Radio Equipment Research Institute Shanghai China Shanghai Aerospace Electronic Technology Institute Shanghai China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Integrated radar and communications (IRC) waveform can be applied to perform radar and communication tasks simultaneously in a multifunctional integration system (MFIS). In order to solve the task scheduling problems of MFIS based on dynamic aperture partition antenna, a novel adaptive task scheduling algorithm based on IRC waveform is proposed in this paper. Firstly, the task scheduling optimization problem is established under the conditions of two-dimensional time and aperture resource constraints after establishing the multi-task models. Secondly, the proposed adaptive task scheduling algorithm makes use of the special IRC waveform characteristics of MFIS, function of time window of task and aperture resource to achieve the different kinds of tasks scheduling effectively. Finally, the simulation results show that the proposed algorithm increases the successful scheduling ratio and resource utilization ratio compared with the traditional multiple task parallel scheduling algorithms.

关键词： Scheduling algorithms Heuristic algorithms Simulation Signal processing algorithms Radar Dynamic scheduling Radar antennas Aperture antennas partitioning algorithms Antenna accessories

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Quantum Computing for Partition Function Estimation of a Markov Random Field in a Radar Anomaly Detection Problem

Quantum Computing for Partition Function Estimation of a Mar...

引用

IEEE International Conference on Radar

作者： Timothé Presles Cyrille Enderli Gilles Burel El Houssain Baghious Thales Defense Mission Systems Thales Elancourt France Lab-STICC CNRS Univ. Brest Brest France

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

In probability theory, the partition function is a factor used to reduce any probability function to a density function with total probability of one. Among other statistical models used to represent joint distribution, Markov random fields (MRF) can be used to efficiently represent statistical dependencies between variables. As the number of terms in the partition function scales exponentially with the number of variables, the potential of each configuration cannot be computed exactly in a reasonable time for large instances. In this paper, we aim to take advantage of the exponential scalability of quantum computing to speed up the estimation of the partition function of a MRF representing the dependencies between operating variables of an airborne radar. For that purpose, we implement a quantum algorithm for partition function estimation in the one clean qubit model. After proposing suitable formulations, we discuss the performances and scalability of our approach in comparison to the theoretical performances of the algorithm.

关键词： Costs Computational modeling Scalability Airborne radar Estimation Probability Logic gates partitioning algorithms Integrated circuit modeling Markov random fields

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Enhanced Complexity Algorithm for Pebble Motion and Multi-Agent Pathfinding in Tree Structures

Enhanced Complexity Algorithm for Pebble Motion and Multi-Ag...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Ramakrishnan Raman Vikram Kumar Biju G. Pillai Apurv Verma Shailesh Rastogi R. Meenakshi Symbiosis International (Deemed University) Pune India SRV Media Department of Management IT & Business Analytics Sri Balaji University Pune India Computer science & Engineering Faculty of Engineering and Technology Parul institute of Technology Parul University Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India Computer Science and Engineering Chennai Institute of technology

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In multi-agent systems, navigating through non-linear pathways within tree structures presents significant challenges, often leading to bottlenecks and conflicts that traditional algorithms struggle to address efficiently. To tackle these issues, we developed an innovative algorithm leveraging advanced heuristic methodologies for pebble motion and multiagent pathfinding (MAPF). Our approach introduces a novel partitioning strategy that divides tree structures into manageable sub-trees, enabling localized pathfinding and reducing the computational overhead associated with global strategies. This partitioning is complemented by an adaptive path optimization technique that dynamically adjusts agent paths based on predictive conflict detection. Together, these strategies significantly enhance pathfinding accuracy and efficiency, particularly in congested networks. The algorithm was rigorously tested across diverse real-world scenarios using both synthetic and actual datasets. Results demonstrate substantial improvements over conventional algorithms like A*, reducing computational time to 75% of the traditional requirement and increasing conflict resolution efficiency to 95%. The enhanced scalability and processing speed make this algorithm suitable for real-time applications ranging from autonomous vehicle navigation to network routing. These findings mark a significant advancement in pathfinding algorithms, offering a promising framework for optimizing multi-agent systems in densely interconnected environments and opening avenues for future research in this field.

关键词： Navigation Heuristic algorithms Scalability Prediction algorithms partitioning algorithms Computational efficiency Robots Optimization Multi-agent systems Testing

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Well Trajectory Design Based on Constrained Many-Objective Optimization algorithms

Well Trajectory Design Based on Constrained Many-Objective O...

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Congress on Evolutionary Computation

作者： Zhaojun Wang Chenwen Ding Wenji Li Yifeng Qiu Jiafan Zhuang Dong Chen Biao Xu Yun Li Zhun Fan Shantou University Guangdong China China University of Petroleum Beijing China Shenzhen Institute for Advanced Study UESTC Guangdong China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In the field of drilling engineering, the design and optimization of well trajectories are crucial. This study focuses on optimizing key aspects such as the length of the well trajectory, drill string torque, the energy of the well-profile, and accuracy in reaching the target. This problem encompasses eleven complex nonlinear constraints and four conflicting objectives, presenting challenges for traditional mathematical programming methods. To tackle this problem, we introduce a novel constrained many-objective optimization algorithm, named PPS-NSGA-III. The proposed algorithm partitions the objective space into subspaces, using NSGA-III to find Pareto optimal solutions in each, enhancing diversity. The push-and-pull search framework is employed to overcome local optima in each subproblem, accelerating overall convergence. Through a comparative analysis with some evolutionary algorithms, PPS-NSGA-III has shown superior performance. It delivers more effective design solutions with lower risk, reduced cost, and a higher drilling encounter rate in the proposed well trajectory optimization model.

关键词： Drilling Torque Optimization models Sociology Evolutionary computation Trajectory partitioning algorithms

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Parallel Collision Detection Algorithm Based on Pipeline Technology

Parallel Collision Detection Algorithm Based on Pipeline Tec...

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Artificial Intelligence, Computer Technology and Control Engineering (ACTCE), International Seminar on

作者： Wei Zhao Huiyan Qu Virtual Reality College of Jilin Animation Institute Changchun China

ISBN: (数字)9798331521714

ISBN: (纸本)9798331521721

This paper presents a parallel collision detection algorithm based on pipeline technology. The partition strategy is adopted to establish the collision detection queue, the processor is allocated by means of cyclic rotation, and the collision queue allocation process is allocated through the pipeline technology. The detection tree is divided in a recursive way, dividing a large problem into sub-problems with the same form as the original problem until no further decomposition is needed, and the task results are incrementally merged with a larger task until no merging is required. The comparative experimental results show that the proposed algorithm can shorten the detection time and improve the detection efficiency.

关键词： Seminars Control engineering Pipelines Merging partitioning algorithms Resource management Collision avoidance Artificial intelligence Optimization

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Toward Privacy-Preserving and Verifiable XGBoost Training for Horizontal Federated Learning

Toward Privacy-Preserving and Verifiable XGBoost Training fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Wei Xu Hui Zhu Chang Xiao Fengwei Wang Dengguo Feng Hui Li School of Cyber Engineering Xidian University Xi’an China State Key Laboratory of Computer Science Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

XGBoost, a widely-used machine learning algorithm, has been applied across various fields. To develop a high-quality model, federated learning is employed, allowing participants to keep their data localized while sharing gradients for global model training. However, recent studies have shown that gradients can leak sensitive information, and even when protected, they might still be vulnerable to certain attacks. While several privacy-preserving and verifiable schemes have been proposed to address this issue, they still face limitations in terms of computational and communication costs in certain scenarios. To solve the problem, we propose a privacy-preserving and verifiable XGBoost training scheme for horizontal federated learning. First, we propose a masking with one-time padding protocol (MOTP) to securely aggregate gradients, supporting both online and offline modes for generating random masks. Both approaches reduce the communication costs and improve the training efficiency. Next, we present a linear aggregation verification protocol (LAVP) to ensure the gradient integrity, which avoids complex computations, thereby improving verification efficiency. Building on MOTP and LAVP, we propose a privacy-preserving and verifiable internal node construction algorithm to train a XGBoost model for horizontal data. Detailed security analysis and extensive experiments demonstrate that our scheme can achieve the privacy-preserving, verifiable, lossless, and efficient XGBoost training for horizontally partitioned data.

关键词： Training Privacy Protocols Costs Machine learning algorithms Federated learning Computational modeling Data models partitioning algorithms Security

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Accelerating Prototype Selection with Spatial Abstraction

Accelerating Prototype Selection with Spatial Abstraction

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Joel L. Carbonera Institute of Informatics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

The growing adoption of machine learning techniques in recent years has led to an unprecedented demand for processing vast amounts of data, driving a tremendous increase in the consumption of computational resources and energy. Prototype selection techniques have been applied to reduce the requirements of computational resources and energy consumption that are needed by these techniques. In this paper, we propose an approach for speeding up existing prototype selection techniques. Firstly, it splits the dataset into a set of spatial partitions. Then, it builds a set of prototypes fo representing the information within each spatial partition. After, some conventional prototype selection algorithms can be applied to the prototypes selected by our approach. The experimental results demonstrate that, overall, our proposed approach maintains accuracy while enhancing the reduction rate of the original prototype selection algorithms and simultaneously reducing their execution times.

关键词： Energy consumption Accuracy Prototypes Machine learning Learning (artificial intelligence) partitioning algorithms

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Integrating Sporadic Events in Time-triggered Systems via Affine Envelope Approximations

Integrating Sporadic Events in Time-triggered Systems via Af...

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Anaïs Finzi Silviu S. Craciunas Marc Boyer TTTech Computertechnik AG Vienna Austria ONERA / DTIS Université de Toulouse Toulouse France

ISBN: (数字)9798350358414

ISBN: (纸本)9798350358421

We introduce a new paradigm for synthesizing time-triggered schedules that guarantees the correct temporal behavior of time-triggered (TT) tasks and the schedulability of sporadic event-triggered (ET) tasks with arbitrary deadlines at design time. The approach first expresses a constraint for the TT task schedule in the form of a maximal affine envelope that ensures that as long as the schedule generation respects this envelope, all sporadic ET tasks meet their deadline. The second step consists of modeling this envelope as a burst limiting constraint (BLC) and building the schedule. The BLC constraint can be added to any existing TT schedule generation method as an additional constraint on the TT slot positioning. Here, we propose an efficient TT schedule generation method that integrates the BLC constraint via simulating a modified Least-Laxity-First (LLF) scheduler. We show via synthetic and real-world test cases that our novel method achieves better schedulability and a faster schedule generation for most use cases compared to other approaches inspired by, e.g., hierarchical scheduling. Moreover, we present an extension to our method that finds the most favorable schedule for TT tasks with respect to ET schedulability, thus increasing the probability that the system remains feasible when ET tasks are later added or changed.

关键词： Schedules Limiting Runtime Real-time systems Calculus partitioning algorithms Resource management

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