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检索条件"机构=Computer Engineering and Control Systems Dept."

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility

Reinforcement Learning for Unrelated Parallel Machine Schedu...

引用

Simulation Winter Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

关键词： Metalearning Adaptation models Heuristic algorithms Stochastic processes Reinforcement learning Parallel machines Dynamic scheduling Size measurement Dispatching Graph neural networks

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An Introduction to Power Electronics Design Methodology

An Introduction to Power Electronics Design Methodology

引用

Design Methodologies Conference (DMC), IEEE

作者： SM Sajjad Hossain Rafin Hossam Hussein Osama A. Mohammed Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Florida USA

The development of effective and dependable electronic systems for a variety of applications, from renewable energy systems and electric vehicles to industrial automation and consumer electronics, depends heavily on power electronics design techniques. Moreover, the power electronics design technique offers a methodical way to create high-performance, dependable, and efficient electronic systems, which helps scholars and engineers address the unique requirements of varied applications while taking thermal management, electromagnetic interference, and safety considerations by integrating theoretical analysis, simulation tools, prototyping, and testing. The effectiveness of this methodology is further increased by the ongoing development of power semiconductor and design methodologies, which makes it possible to realize cutting-edge and environmentally friendly power electronic solutions for the modern world. Considering above mentioned importance, this paper is written as an initial guideline for scholars and engineers to design power electronic systems.

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Code Design for Duplex Read Sequencing

Code Design for Duplex Read Sequencing

引用

IEEE International Symposium on Information Theory

作者： Adrian Vidal V. B. Wijekoon Emanuele Viterbo Dept. Electrical & Computer Systems Engineering Monash University Clayton VIC Australia EEE Institute University of the Philippines Diliman Quezon City Philippines

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Motivated by duplex read sequencing developed by Oxford Nanopore Technologies, this paper proposes a concatenated coding scheme for nanopore sequencers where DNA sequences are decoded from noisy reads of the template and reverse-complement strands from the same DNA molecule, that is, a duplex read. First, we show that the double-strand pairwise error probability (PEP) bound is multiplicative with respect to the single-strand PEP bounds of the template and reverse-complement codebooks, thus giving duplex decoders significantly lower error rates compared to simplex decoders which only use the template strand. Then, we propose a decoder for multiple concatenations of short codebooks designed using the double-strand PEP bound.

关键词： Sequential analysis Codes Pairwise error probability Error analysis Design methodology DNA Encoding

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BirDrone: A Novel Dataset for Enhanced Drone and Bird Detection Using YOLOv9 10

BirDrone: A Novel Dataset for Enhanced Drone and Bird Detect...

引用

10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Zamri, Fatin Najihah Muhamad Gunawan, Teddy Surya Yusoff, Siti Hajar Mustafah, Yasir Mohd. Kartiwi, Mira Yusoff, Nelidya Md International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Mechatronics Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur51400 Malaysia

ISBN: (纸本)9798350365283

Drones present substantial detection challenges due to their capacity to operate in various conditions, including low lighting, harsh weather, and similar objects like birds. Existing datasets frequently fail to address all of these challenges comprehensively. The BirDrone dataset, specifically designed to improve the accuracy of distinguishing between drones and birds, is introduced to address this issue, with a particular emphasis on small-scale objects. The dataset comprises images with intricate backgrounds and lighting conditions to enhance detection reliability. By utilizing the YOLOv9 model to assess the dataset, we achieved a high level of accuracy and significantly reduced the number of false alarms. The BirDrone dataset's development process, data augmentation methodologies, and performance outcomes of YOLOv9 are all detailed in this paper, which serves as a testament to its efficacy in practical applications. © 2024 IEEE.

关键词： Drones

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Novel PM-Assisted Model of the Two-Layer Sub-Harmonic Synchronous Machines

Novel PM-Assisted Model of the Two-Layer Sub-Harmonic Synchr...

引用

International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： S M Sajjad Hossain Rafin Qasim Ali Osama A. Mohammed Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Florida USA Dept. of Electrical Engineering Sukkur IBA University Sukkur Pakistan

In this paper, a new pm-assisted model has been developed for the already introduced two-layer sub-harmonic synchronous machine. This work aims to increase the torque-producing capability of the brushless wound rotor machines while keeping the use of rare earth magnets to a minimum. A 2D finite element analysis has been performed to validate the proposed model and compare the performance with the reference model. The results in the paper demonstrate that the proposed machine's average torque has increased.

关键词： Analytical models Torque Rotors Transportation Production Computational electromagnetics Topology

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Workload Balancing Model for Heterogeneous IoT Nodes Architectures

Workload Balancing Model for Heterogeneous IoT Nodes Archite...

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Electrical engineering and Automatic control (ICEEAC), International Conference on

作者： Mustafa Kh Aswad Fozia Al-Badree Beetro Ayad Ali Keshlaf Sintific commtiee Research Dept. Libyan Center for Electronic Systems Programming and Aviation Research Tripoli Libya Faculty Of Engineering University of Zawia Zawia Libya Computer Engineering and IT Dept. Faculty of Engneering Sabrahta Univeristy Sabrahta Libya

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

Heterogeneous IoT architectures are evolving rapidly and different challenged are faced with the traditional IoT architectures including the performance time of real-time IoT application. Parallel computing programming technique could enhance the performance and efficiency for distributed systems and multicore processors as well as the IoT systems. However, parallel computing, presents certain difficulties and constraints, including synchronization, communication, security concerns, and load balancing. In this regard, a novel IoT workload balancing model for heterogeneous IoT architectures is presented in this paper. This model is intended to reduce the execution time of large systems by redistributing part of their functions to other involved IoT nodes. An experiment has been conducted to evaluate the actual real load for each IoT node and tried to rebalance the load using the proposed model. The results were encouraging as the performance time was reduced by about one third on two cores.

关键词： Program processors Computational modeling computer architecture Parallel processing Programming Real-time systems Internet of Things

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Benchmarking Convolutional Neural Network Inference on Low-Power Edge Devices

Benchmarking Convolutional Neural Network Inference on Low-P...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Oscar Ferraz Helder Araujo Vitor Silva Gabriel Falcao Instituto de Telecomunicações Dept. of Electrical and Computer Engineering University of Coimbra Portugal Institute of Systems and Robotics

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s.

关键词： Performance evaluation Benchmark testing Signal processing Throughput Hardware Convolutional neural networks Internet of Things

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Boosting Speech Enhancement with Clean Self-Supervised Features Via Conditional Variational Autoencoders

Boosting Speech Enhancement with Clean Self-Supervised Featu...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yoonhyung Lee Kyomin Jung Dept. of Electrical and Computer Engineering Seoul National University Republic of Korea Automation and Systems Research Institute Seoul National University Republic of Korea

Recently, Self-Supervised Features (SSF) trained on extensive speech datasets have shown significant performance gains across various speech processing tasks. Nevertheless, their effectiveness in Speech Enhancement (SE) systems is often suboptimal due to insufficient optimization for noisy environments. To address this issue, we present a novel methodology that directly utilizes SSFs extracted from clean speech for enhancing SE models. Specifically, we leverage the clean SSFs for latent space modeling within the Conditional Variational Autoencoder (CVAE) framework. Consequently, we enable our model to fully leverage the knowledge existing in the clean SSFs without the interference of noise. In experiments, our approach yields clear improvements over existing methods that use SSFs across six evaluation metrics. Furthermore, we provide comprehensive analyses to validate the effectiveness of 1) incorporating clean SSFs within the CVAE framework and 2) the training techniques used to achieve optimal performance from our approach in SE systems. Code and audio samples are available at https://***/YoonhyungLee94/SSFCVAE

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High-Speed, Low-Power, and Small-Area Optical Receiver in 65-nm CMOS

High-Speed, Low-Power, and Small-Area Optical Receiver in 65...

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International Conference on ASIC

作者： Akira Tsuchiya Toshiyuki Inoue Keiji Kishine Daisuke Ito Yasuhiro Takahashi Makoto Nakamura Dept. Electronic Systems Engineering The University of Shiga Prefecture Hikone-shi Shiga Japan Dept. Electrical Electronic and Computer Engineering Gifu University Gifu-shi Gifu Japan

This paper proposes optical receiver for CPO (Co-Packaged Optics). Receiver IC for CPO have to achieve high-speed, low-power (high energy efficiency), and small area. For high-speed operation, bandwidth enhancement techniques are required. By employing multi-layer inductors, we realized small area and low power bandwidth enhancement. The proposed circuit is fabricated in a 65-nm CMOS and measurement results verified 32 Gbps bit-rate, 0.71 pJ/bit energy efficiency on 0.066 mm 2 /channel area.

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System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

System Identification-informed Transparent and Explainable M...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Hua-Liang Wei Dept. Automatic Control and Systems Engineering Energy Institute Centre of Machine Intelligence Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield UK

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.

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