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Voxel-based 3D MRI volume segmentation system using real-time hardware architecture 21

Voxel-based 3D MRI volume segmentation system using real-tim...

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21st IEEE International conference on Sciences and Techniques of Automatic Control and Computer Engineering, stA 2022

作者： Hamdaoui, Faycal Sakly, Anis Electrical Department Monastir Tunisia

ISBN: (纸本)9781665482615

In this paper, a hard real time and optimized multi-level 3D MRI volume voxel-based Fractional Order Darwinian Particle Swarm optimization (FODPSO) Segmentation algorithm system has been developed. It is an improved hardware design based on Fractional Order PSO to ensure non-convergence to an erroneous desired threshold. The Darwinian variant is used to guarantee a more faithful multi-level segmentation (3-levels for this work). The hardware design was proposed based on the Vivado system Generator (VSG) tool. Co-simulation and experimental results validate the performance of the proposed system based on the BraTS 2013 dataset. Also, resource utilization, execution time, power consumption, efficiency, specificity, and sensitivity prove the quality of the proposed system in terms of architecture and in terms of the performance of the multi-level segmentation of voxel-based 3D MRI volumes. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Optimizing Smart Grid Integration: A Comprehensive Analysis Review of Load Flow and Advanced optimization strategies

Optimizing Smart Grid Integration: A Comprehensive Analysis ...

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Learning and Technology conference (L&T)

作者： Jana Kamal Wafa Alharbi Yara Muqaybil Enfel Barkat Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia

This paper explores the architecture of the smart grid, emphasizing component modeling and the SGAM layers. It introduces optimized load flow equations and investigates algorithmic enhancements for improved power flow efficiency. The study provides valuable insights into optimizing grid performance, thereby supporting the transition to a sustainable smart grid. Emphasizing key layers such as SGAM components, communication, information, function, and business, the research addresses critical aspects of seamless data exchange, economic considerations, and regulatory aspects. The application of optimization algorithms aims at refining energy generation, consumption, and overall grid stability. This study contributes valuable insights to optimize grid performance and facilitate the transition to a sustainable smart grid.

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On Substrates, Blocks and states: Distributed Energy Resources' Diffusion Models, Use Cases and Frontiers in Power system Planning 21

On Substrates, Blocks and States: Distributed Energy Resourc...

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21st IEEE Mediterranean Electrotechnical conference (IEEE MELECON)

作者： Heymann, Fabian Gastelu, Joel Villavicencio Anselmo, Anselmo Rajabo Melo, Joel D. INESC TEC Ctr Power & Energy Syst Porto Portugal Fed Univ ABC Engn Modeling & Appl Social Sci Ctr Santo Andre SP Brazil

ISBN: (数字)9781665442800

ISBN: (纸本)9781665442800

Due to the ongoing adoption of new household energy technologies, there has been an increasing effort to analyze these behavioral patterns and predict future dynamics in space and time. Likewise, power system planners and policy makers aim to assess the effects of large-scale adoption of energy technologies. This paper presents the principal data sources and building blocks for spatiotemporal models that can be used to simulate the large-scale adoption of distributed energy resources. We explain the advantages of the estimates/forecasts obtained by these methods and limitations modelers and decision-makers face during implementation. Finally, we provide an overview over the various use cases for such models in power systems, outlining the advantages and barriers of each model typology for these domains. This comparison shows a trend towards computationally heavier and more complex models-based approaches rooted in Artificial Intelligence, such as Agent-based Modelling, mainly applied to consumer characterization or electricity distribution network planning.

关键词： Diffusion of Innovation Distributed Energy Resources Power systems Planning Spatial Simulation

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optimization and Control strategies for stochastic Power Quality of Distributed Generation

Optimization and Control Strategies for Stochastic Power Qua...

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International conference on Harmonics and Quality of Power

作者： Zhongying Wei Shurong Li Juan Wang Jinghui Zhang Xiangmin Xie College of Electrical Engineering Qingdao University Qingdao China State Grid Hebei Electric Power Co. Ltd. Xiongan New Area Power Supply Company Xiongan China

ISBN: (数字)9798350382563

ISBN: (纸本)9798350382570

To solve power quality problems in distribution networks, this paper presents a distributed generation siting and capacity determination method. The methodology aims to minimize network losses, reduce the total harmonic distortion rate of voltage (THDV), and take into account load and power supply uncertainties. A probabilistic optimization solution algorithm that combines a traceless variation method and a traversal optimization algorithm is proposed, which greatly reduces the computational effort. Simulations are performed to validate it using an IEEE-69 node system. It is demonstrated that this proposed method improves the power quality by significantly reducing network losses.

关键词： Reactive power Uncertainty Computational modeling Power quality Distribution networks Probabilistic logic Market research Distributed power generation optimization Load modeling

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FPGA-Based Hardware modeling on Pigeon-Inspired optimization Algorithm 1

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21st Asia Simulation conference, AsiaSim 2022

作者： Zhao, Yu Zhao, Chun Liu, Yue Beijing Information Science and Technology University School of Computer Science Institution of Smart Manufacturing & Complex System BISTU Beijing100101 China

ISBN: (数字)9789811991981

ISBN: (纸本)9789811991974

By learning behavioral characteristics and biological phenomena in nature, such as birds, ants, and fireflies, intelligent optimization algorithms (IOA) is proposed. IOA shows feasibility in solving complex optimization problems in reality. Pigeon-inspired optimization (PIO) algorithm, which belongs to intelligent optimization algorithms, is proposed by the pigeons homing navigation behavior inspired. PIO is superior to other algorithms in dealing with many optimization problems. However, the performance of PIO processing large-scale complex optimization problems is poor and the execution time is long. Population-based optimization algorithms (such as PIO) can be optimized by parallel processing, which enables PIO to be implemented in hardware for improving execution times. This paper proposes a hardware modeling method of PIO based on FPGA. The method focuses on the parallelism of multi-individuals and multi-dimensions in pigeon population. For further acceleration, this work uses parallel bubble sort algorithm and multiply-and-accumulator (MAC) pipeline design. The simulation result shows that the implementation of PIO based on FPGA can effectively improve the computing capability of PIO and deal with complex practical problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Field programmable gate arrays (FPGA)

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Online optimization of a Dial-a-Ride Problem with the Integral Primal Simplex 1

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21st International conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Amiri, Elahe Legrain, Antoine El Hallaoui, Issmail Polytech Montreal Montreal PQ H3T 1J4 Canada CIRRELT Montreal PQ H3T 1J4 Canada GERAD Montreal PQ H3T 2A7 Canada

ISBN: (数字)9783031605970

ISBN: (纸本)9783031605963;9783031605970

This paper focuses on developing a real-time dispatching system for a ride-sharing service. The primary goal is to address the dynamic dial-a-ride Problem, aiming to minimize waiting times while ensuring service quality by limiting ride duration. We introduce a rolling horizon-based framework, involving the division of the time horizon into small epochs, batching requests within each epoch, and re-optimizing the problem for the batch of requests. Unlike prior studies that restart optimization for each period from scratch, we leverage the strength of integral primal simplex to reuse effectively the previously computed solutions as a warm start, extending current routes with new incoming requests. Moreover, using integral primal methods allows us to provide an algorithm that is tractable in real-time and scales effectively to handle thousands of customers per hour. Experiments using historic taxi trips in New York City, involving up to 30,000 requests per hour, illustrate the efficacy and potential advantages of the method in effectively managing large-scale and dynamic scenarios.

关键词： Real-time dial-a-ride integral column generation integral simplex using decomposition

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Quantum Battery optimization through Quantum Machine Learning Techniques 21

Quantum Battery Optimization through Quantum Machine Learnin...

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21st International system-on-Chip Design conference, ISOCC 2024

作者： Hai, Vu Tuan Minh Kiet, Vo Trung Duong, Le Vu Hoai Luan, Pham Ho, Le Bin Nakashima, Yasuhiko Nara Institute of Science and Technology Nara630-0192 Japan University of Information Technology Vietnam National University Ho Chi Minh Viet Nam Tohoku University Frontier Research Institute for Interdisciplinary Sciences Sendai980-8578 Japan Tohoku University Department of Applied Physics Graduate School of Engineering Sendai980-8579 Japan

ISBN: (纸本)9798350377088

Quantum batteries are a promising technology that could surpass their classical counterparts and play a critical role in the advancement of quantum technologies. In this work, we develop a quantum machine learning algorithm to improve the charging power of quantum batteries. To model an N-cell quantum battery, we use a quantum circuit with N quantum bits. During the charging process, we apply a set of quantum gates with trainable parameters to the circuit. Next, we evaluate the power extraction and optimize it using a classical computer. We find a remarkable maximum power when increasing N. Our work has the potential to create sustainable and scalable energy storage technologies in the future. © 2024 IEEE.

关键词： Quantum computers

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Fine-Grained Annotation Through Multi-Model Fusion and Class-Specific Performance optimization in Object Detection 21

Fine-Grained Annotation Through Multi-Model Fusion and Class...

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21st International Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Hongzhi, Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9798331519254

Automatic annotation is essential for large-scale data labeling in object detection, but traditional single-model methods often lack accuracy and reliability. Multi-model systems address this by leveraging the strengths of multiple detectors but face two key challenges: identifying whether predictions from different models refer to the same target and effectively integrating those predictions. This paper introduces a fine-grained annotation method and a distributed system architecture to address these issues. A target matching mechanism inspired by non-maximum suppression (NMS) consolidates predictions across models by evaluating bounding box overlaps. A dynamic decision-making algorithm assigns adaptive voting weights based on classspecific model performance and uncertainty, ensuring precise and robust label assignments. The distributed architecture, combining a storage layer, search engine, and scheduling servers, efficiently handles largescale annotation tasks. This approach highlights the benefits of multi-model integration, adaptive decision-making, and scalable design for reliable automatic annotation in object detection. © 2024 IEEE.

关键词： Search engines

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Heterogeneous Edge Server and Job Allocation Based on k-means++ and Transfer Probability 21

Heterogeneous Edge Server and Job Allocation Based on k-mean...

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21st IEEE Consumer Communications and Networking conference, CCNC 2024

作者： Ogawa, Kohei Miyata, Sumiko Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo135-8548 Japan

ISBN: (纸本)9798350304572

Mobile Edge Computing (MEC) is attracting attention as a solution to increasing network traffic load. To build an MEC system, edge servers are placed on base stations and the users must be allocated to edge servers. The conventional MEC method places edge servers and allocate user jobs by using extension of k-means to minimize the number of hops between a user and servers. With this method, however, heterogeneous environments are not assumed, which means the performance difference among edge servers. When many jobs arrive at edge servers with low performance, jobs overflow and transfer to the cloud servers. The initial value dependence of this conventional method has also not discussed. We propose a method for initial edge-server placement and job reallocation in order to apply heterogeneous edge servers and initial value dependence. © 2024 IEEE.

关键词： Clustering Edge computing optimization prob-lem Queuing theory Traffic load

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Predicting Forces in the Components of the Human Upper Limb through Multibody Dynamics and optimization Techniques 21

Predicting Forces in the Components of the Human Upper Limb ...

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21st International conference on Applied Mechanics and Mechanical Engineering, AMME 2024

作者： Alhulaifi, Taha Awad, Taher H. Mohamed, Khaled T. Mechanical Department Faculty of Engineering Alexandria University Egypt

An accurate model of the human upper limb is crucial for various applications, including prosthetic development, optimizing ergonomics, and rehabilitation. The novelty of this research is to obtain a model that predicts the forces of muscles, ligaments, and joint reactions under any loading conditions, thereby enabling the prediction of musculoskeletal forces during different daily activities. For this, a multibody three-dimensional dynamic model of the human upper limb is presented to study the upper limb musculoskeletal system. The model comprises 35 muscle elements, 11 ligaments, and 7 joints. Thirty equations of motion with 73 unknowns were obtained using Newton's second law of motion. The Ariel Performance Analysis system (APAS) was utilized to record and analyze the motion of 16 bony landmarks using 8 digital video cameras with a sampling rate of 100 frames per second. The genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve the redundant force problem of the model. The GA and PSO are implemented with two distinct objective functions;the first is the sum of the squared muscles' stresses. The second one is the sum of the muscles' energy consumption. The model was validated through EMG recordings of eight superficial muscles and with the available data in the literature. The results show that the inclusion of the ligaments at the GH and elbow joints in addition to the trapezoid and conoid ligaments in the model led to a more precise prediction of the muscles' force. The PSO shows more accuracy and smoothness of the predicted muscles' force, despite the GA taking more running time and requiring significant memory resources. The model also revealed that there is no great difference in the predicted muscles' force when using any of the two cost functions. © Published under licence by IOP Publishing Ltd.

关键词： Cost functions

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