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检索条件"主题词=approximation algorithms"

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Analysis of Blockchain Assisted Energy Sharing algorithms with Realistic Data Across Microgrids

Analysis of Blockchain Assisted Energy Sharing Algorithms wi...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Abdulrezzak Zekiye Ozan Sina Bankaoglu Ouns Bouachir Öznur Özkasap Moayad Aloqaily Department of Computer Engineering Koç University İstanbul Türkiye College of Technological Innovation (CTI) Zayed University UAE Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

With escalating energy demands, innovative solutions have emerged to supply energy affordably and sustainably. Energy sharing has also been proposed as a solution, addressing affordability issues while reducing consumers’ greed. In this paper, we analyse the feasibility of two energy sharing algorithms, centralized and peer-to-peer, within two scenarios, between microgrids within a county, and between microgrids across counties. In addition, we propose a new sharing algorithm named Selfish Sharing, where prosumers take advantage of consumers’ batteries in return for letting them consume part of the shared energy. The results for sharing between microgrids across counties show that the dependency on the grid could be reduced by approximately 5.72%, 6.12%, and 5.93% using the centralized, peer-to-peer and selfish sharing algorithms respectively, compared to trading only. The scenario of sharing between microgrids within a county has an average decrease in dependency on the grid by 5.66%, 6.0%, and 5.80% using the centralized, peer-to-peer and selfish algorithms respectively, compared to trading without sharing. We found that trading with batteries and the proposed sharing algorithms prove to be beneficial in the sharing between microgrids case. More specifically, the case of trading and sharing energy between microgrids across counties outperforms sharing within a county, with P2P sharing appearing to be superior.

关键词： Energy consumption Machine learning algorithms Smart contracts Energy resolution Microgrids Production approximation algorithms Batteries Peer-to-peer computing Blockchains

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Identifying Bottlenecks of NISQ-Friendly HHL algorithms

Identifying Bottlenecks of NISQ-Friendly HHL Algorithms

引用

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Marc Andreu Marfany Alona Sakhnenko Jeanette Miriam Lorenz Fraunhofer Institute for Cognitive Systems IKS Munich Germany Ludwig-Maximilian University Munich Germany Technische Universität München Munich Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum computing promises enabling solving large problem instances, e.g. large linear equation systems with HHL algorithm, once the hardware stack matures. For the foreseeable future quantum computing will remain in the so-called NISQ era, in which the algorithms need to account for the flaws of the hardware such as noise. In this work, we perform an empirical study to test scaling properties and directly related noise resilience of the the most resources-intense component of the HHL algorithm, namely QPE and its NISQ-adaptation Iterative QPE. We explore the effectiveness of noise mitigation techniques for these algorithms and investigate whether we can keep the gate number low by enforcing sparsity constraints on the input or using circuit optimization techniques provided by Qiskit package. Our results indicate that currently available noise mitigation techniques, such as Qiskit readout and Mthree readout packages, are insufficient for enabling results recovery even in the small instances tested here. Moreover, our results indicate that the scaling of these algorithms with increase in precision seems to be the most substantial obstacle. These insights allowed us to deduce an approximate bottleneck for algorithms that consider a similar time evolution as QPE. Such observations provide evidence of weaknesses of such algorithms on NISQ devices and help us formulate meaningful future research directions.

关键词： Performance evaluation Quantum computing Prevention and mitigation Noise Logic gates approximation algorithms Hardware Iterative algorithms Noise measurement Resilience

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Ranking Schools with Multiple Features: An Integrated Approach using Data Analytics and Algorithm

Ranking Schools with Multiple Features: An Integrated Approa...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Kunal Singh Divyanshu Srivastava Nidhi Mishra Department of Computer Science and Engineering Amity University Uttar Pradesh Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper develops a method to rate schools by using an algorithm which measures their impact and attention devoted to them in their respective field. We use the school network we built for a particular area for linking schools with similar features as a metric for our calculation because the number of schools involved with a particular requirement helps us to effectively count and predict the importance of the school. We modify the simple iterative PageRank algorithm to count the number of importance related to the school effectively to approximate the relative probability of a user selecting a particular school for admission. We introduced a multiplication factor which takes care of the time factor involved with the foundation year of school. We introduced the concept of importance and link vector to effectively connect the nodes in a school network in the form of one-to-one relation. We implemented our algorithm on a processed dataset and calculated the normalized scores involved with each node of the school network which is representing a school and using these scores we ranked the schools in a particular area concerned. For quantitative values of various theoretical features, we processed the data to obtain further relation involved with the school and various factors in the school network.

关键词： Measurement Computers Data analysis Prediction algorithms approximation algorithms Vectors Iterative algorithms Time factors

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DNN Partitioning and Assignment for Distributed Inference in SGX Empowered Edge Cloud

DNN Partitioning and Assignment for Distributed Inference in...

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International Conference on Distributed Computing Systems

作者： Yuepeng Li Deze Zeng Lin Gut Song Guo Albert Y. Zomaya School of Computer Science China University of Geosciences Wuhan China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science The University of Sydney Australia

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Distributed Deep Neural Network (DNN) inference is a promising technology to explore the distributed resources in edge cloud to realize edge intelligence. Meanwhile the inherent resource sharing nature of edge cloud infrastructure also raises serious concerns on security and privacy. Software Guard Ex-tensions (SGX) emerges as a potential hardware-level solution but its limited secure memory (i.e., enclave page cache) imposes new challenges, especially in contrast to memory-hungry DNN models. A task's performance will be severely affected when its memory footprint is beyond the enclave page cache size, due to expensive secure page swapping. In this case, how to appropriately partition a DNN model and assign the partitions to distributed edge servers to efficiently utilize edge resources for fast secure inference becomes a challenging problem. In this paper, we first show that this problem is NP-hard. We further propose a MEmory -aware Distributed Inference Acceleration (MEDIA) algorithm, whose guaranteed approximation ratio is also formally analyzed. We have implemented a prototype system and applied some well-known representative DNN models to evaluate MEDIA's performance. Through extensive experiments, we verify the efficiency of MEDIA by the fact that it reduces the inference time by 19.5%-38.1 % in comparison with state-of-the-art approaches.

关键词： Computational modeling Prototypes Artificial neural networks Media approximation algorithms Inference algorithms Software

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An Optimized Speed Curve Generation Algorithm for Trains Based on CNN

An Optimized Speed Curve Generation Algorithm for Trains Bas...

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IEEE International Conference on Control Science and Systems Engineering (CCSSE)

作者： Jingjing Li Xubin Sun Zemin Yao School of Automation and Intelligence Beijing Jiaotong University Beijing China New H3C Technologies Co. Ltd Hangzhou China

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

With the continuous development of train control and communication technologies, trains are also developing towards automatic control and autonomous control. The train speed curve generation is one of the key research contents. Currently, some promising optimization algorithms of the train speed curve failure to meet the punctuality requirements in practical applications. For reducing the time of generating speed curve and improving the punctuality of the speed curve generation algorithm, an optimized speed curve generation algorithm for trains on the basis of Convolutional Neural Network(CNN) is proposed in this paper. This algorithm can learn the speed curves produced by different optimization algorithms or the speed curves of trains driven by excellent drivers, on the premise of ensuring energy-saving, punctuality, and other performance indexes of the curve. Based on the existing speed curve optimization algorithms, taking the Rolling Optimization Algorithm of Train Speed Curve(ROATSC) as an example without loss of generality, this paper uses the speed curve produced by this algorithm to train the CNN model, and uses the freight train as an example for simulation. Simulation results are achieved with different train weights and lengths, the CNN model speed curve fits the ROATSC algorithm speed curve with an approximation accuracy index 98%, which is higher than that of the Multilayer Perceptron(MLP) and Long Short-Term Memory network(LSTM) models. After testing, the speed curve produced by this method can not only decrease the generating time of speed curve, but also maintain the ROATSC algorithm performance. As seen in simulation results, the presented algorithm provides an additional choice to apply some algorithms of train speed curve generating in Automatic Train Control (ATO) system with the real-time requirement.

关键词： Accuracy Simulation approximation algorithms Control systems Data models Real-time systems Performance analysis Convolutional neural networks Optimization Testing

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Research on Distribution Job Shop Scheduling and Order Allocation Based on Improved Genetic Algorithm

Research on Distribution Job Shop Scheduling and Order Alloc...

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Computer Science and Blockchain (CCSB), International Conference on

作者： Li Zhang School of systems Science Beijing Jiaotong University Beijing China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Distributed manufacturing, known for its cost-effectiveness and efficiency, is a growing trend in the industry. However, existing research on job shop scheduling often neglects machine quantity and workshop-specific requirements. To address this gap, this study proposes a two-stage model that addresses both aspects. The first phase develops a multi-workshop order allocation model, while the second phase constructs a spinning frame assignment model based on actual production needs. An enhanced genetic algorithm, combined with an exact algorithm, is utilized for problem-solving. The exact algorithm ensures precise machine allocation, while the genetic algorithm broadens search scope. A practical case study validates the model and algorithm's effectiveness, showing a capacity increase of approximately 10%, 94.61% higher than manual scheduling, while satisfying factory-specific constraints.

关键词： Job shop scheduling Processor scheduling Conferences Production Manuals approximation algorithms Resource management Spinning Genetic algorithms Textiles

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A Value Set Approach for Stability Region Determination for Time Delayed Power Systems with Multiple Uncertain Parameters

A Value Set Approach for Stability Region Determination for ...

引用

China International Electrical and Energy Conference (CIEEC)

作者： Xiubao Shi Hua Ye Lei Gao Kaiyuan Hou Kefei Wang Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Power System Department China Electric Power Research Institute Beijing China Dispatch and Communication Center Northeast Branch State Grid Cooperation of China Shenyang China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

This paper develops a stability region computation method for time delayed power systems based on the value set method. It is designed to accommodate systems with multiple uncertain parameters and time delays. This method serves to analyze the small signal stability margin of power systems and offer guidance for system parameter design. Different from many previous works, this method achieves relatively high efficiency without any conservativeness. First, it processes polynomials with exponentials through polynomial transformations. Second, by continuously subdividing and discriminating the uncertain space, the parameter stability region of the system is obtained. Finally, the effectiveness and accuracy of the proposed method are validated through simulation examples in MATLAB/Simulink.

关键词： Uncertainty Accuracy Delay effects Power system stability approximation algorithms Stability analysis Polynomials

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Optimizing Cyber-Resilience in Critical Infrastructure Networks

Optimizing Cyber-Resilience in Critical Infrastructure Netwo...

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Simulation Winter Conference

作者： Ranjan Pal Rohan Xavier Sequeira Sander Zeijlmaker Michael Siegel MIT Sloan School of Management Massachusetts Institute of Technology USA Electrical and Computer Engineering University of Southern California USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

With the expanding cyber-risk terrain spanning business processes in digitally driven enterprises with critical infrastructure, it is inevitable in time that system process continuity (SPC) will be affected (e.g., via ransomware) for certain inter-dependent processes of such an enterprise, and hamper business continuity. We are interested in the question: how should managers of such enterprises optimize cyber-resilience (i.e., the ability to maintain SPC via absorbing and adapting to an adverse cyber-incident) for any complex networked critical infrastructure (CI) (sub-)system with multiple process functionality components (PFCs)? We prove via an algorithmic graph-theoretic approach that optimizing or approximately optimizing cyber-resilience within a pre-specified enterprise cyber-protection budget in any CI with networked and inter-dependent PFCs is NP-hard. Consequently, we propose a computationally tractable graph-based Monte-Carlo simulation framework to ‘optimize’ (boost) cyber-resilience within any PFC network by allocating a constrained cyber-protection budget among PFCs in accordance with their Katz centralities in the PFC network.

关键词： Monte Carlo methods approximation algorithms Critical infrastructure Ransomware Business continuity Business

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On the Hardness of Decentralized Multi-Agent Policy Evaluation Under Byzantine Attacks

On the Hardness of Decentralized Multi-Agent Policy Evaluati...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Hairi Minghong Fang Zifan Zhang Alvaro Velasquez Jia Liu Dept. of Computer Science University of Wisconsin-Whitewater Dept. of Computer Science and Engineering University of Louisville Dept. of Computer Science North Carolina State University Dept. of Computer Science University of Colorado Boulder Dept. of Electrical and Computer Engineering The Ohio State University

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

In this paper, we study a fully-decentralized multi-agent policy evaluation problem, which is an important sub-problem in cooperative multi-agent reinforcement learning, in the presence of up to $f$ faulty agents. In particular, we focus on the so-called Byzantine faulty model with model poisoning setting. In general, policy evaluation is to evaluate the value function of any given policy. In cooperative multi-agent system, the system-wide rewards are usually modeled as the uniform average of rewards from all agents. We investigate the multi-agent policy evaluation problem in the presence of Byzantine agents, particularly in the setting of heterogeneous local rewards. Ideally, the goal of the agents is to evaluate the accumulated system-wide rewards, which are uniform average of rewards of the normal agents for a given policy. It means that all agents agree upon common values (the consensus part) and furthermore, the consensus values are the value functions (the convergence part). However, we prove that this goal is not achievable. Instead, we consider a relaxed version of the problem, where the goal of the agents is to evaluate accumulated system-wide reward, which is an appropriately weighted average reward of the normal agents. We further prove that there is no correct algorithm that can guarantee that the total number of positive weights exceeds $\vert \mathcal{N}\vert -f$ , where $\vert \mathcal{N}\vert$ is the number of normal agents. Towards the end, we propose a Byzantine-tolerant decentralized temporal difference algorithm that can guarantee asymptotic consensus under scalar function approximation. We then empirically test the effective of the proposed algorithm.

关键词： Wireless networks Reinforcement learning approximation algorithms Function approximation Optimization Multi-agent systems Convergence

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Enhanced Unscented Kalman Filter for Accurate State of Charge Estimation in Aerial Drone Lithium-Ion Batteries

Enhanced Unscented Kalman Filter for Accurate State of Charg...

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Industrial Cyber-Physical Systems (ICPS)

作者： Islam Md Monirul Li Qiu Ahmad Ali College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Accurate state of charge (SOC) estimation is a fundamental aspect of lithium-ion batteries (LIBs) used in aerial drones. This estimation significantly affects both the control accuracy and the reliability of the energy storage system embedded in the LIB power supply. The Unscented Kalman filter (UKF) algorithm is used to estimate the SOC in LIBs. However, the algorithm’s ability to accurately monitor rapid fluctuations in pulse current is compromised, resulting in suboptimal tracking performance and non-negligible estimation errors. This paper presents an improved UKF to address this challenge. The results show that a two-order PNGV (Partnership for a New Generation of Vehicles) model effectively limits the maximum estimation error to less than 0.2 V. Even under significant voltage and current variations, the UKF estimation error can reach up to 2%. However, the use of the enhanced UKF algorithm maintains remarkable stability with only a 1% estimation error. This represents a 0.8% improvement in estimation accuracy compared to conventional methods. These results provide a robust theoretical basis for improving energy management in LIBs for aerial drones.

关键词： Lithium-ion batteries Estimation error Adaptation models Accuracy approximation algorithms Stability analysis State of charge

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