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Deep-Reinforcement-Learning-Based Continuous Workflows Scheduling in Heterogeneous Environments

IEEE Internet of Things Journal 2025年第10期12卷 14036-14050页

作者： Wang, Zhi Zhan, Wenhan Duan, Hancong Min, Geyong Huang, Hualong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Exeter College of Engineering Mathematics and Physical Sciences Department of Computer Science ExeterEX4 4QF United Kingdom

Workflow scheduling plays a critical role in optimizing completion time and throughput in distributed cloud environments, leveraging the parallelism of heterogeneous computing resources. However, existing workflow scheduling algorithms often fall short due to heuristic limitations and the challenges in adaptability within heterogeneous settings, leading to suboptimal scheduling solutions. In this article, we present a novel deep reinforcement learning (DRL) framework tailored for continuous workflow scheduling in heterogeneous environments. First, we propose an intelligent scheduler that updates the policy network through interactions with a multitenant environment, triggered by scheduling events. Next, the framework incorporates a graph attention network (GAT) and a self-attention multilayer perceptron (MLP) to preprocess the workflow topology and embed dynamic features of ready tasks and available processors into the state input at each scheduling step. Additionally, a k-dimensional tree-based k-nearest neighbors (kNN) algorithm is employed to map the output action vector to a pair of executed ready task and processor, facilitating the transition from continuous to discrete action spaces and addressing challenges associated with dynamic action spaces. Experimental results demonstrate that our method converges effectively in continuous workflow scheduling scenarios and significantly outperforms the best-known methods in terms of average makespan and load balancing efficiency. © 2014 IEEE.

关键词： Deep reinforcement learning

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ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models 24

ADO-LLM: Analog Design Bayesian Optimization with In-Context...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Yin, Yuxuan Wang, Yu Xu, Boxun Li, Peng Department of Electronic and Computer Engineering University of California Santa BarbaraCA United States

ISBN: (纸本)9798400710773

Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine-learning-based optimization strategy, has been leveraged to automate analog design given its applicability across various circuit topologies and technologies. Traditional BO methods employ black-box Gaussian Process surrogate models and optimized labeled data queries to find optimization solutions by trading off between exploration and exploitation. However, the search for the optimal design solution in BO can be expensive from both a computational and data usage point of view, particularly for high-dimensional optimization problems. This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization. ADO-LLM leverages the LLM’s ability to infuse domain knowledge to rapidly generate viable design points to remedy BO’s inefficiency in finding high-value design areas specifically under the limited design space coverage of the BO’s probabilistic surrogate model. In the meantime, sampling of design points evaluated in the iterative BO process provides quality demonstrations for the LLM to generate high-quality design points while leveraging infused broad design knowledge. Furthermore, the diversity brought by BO’s exploration enriches the contextual understanding of the LLM and allows it to more broadly search in the design space and prevent repetitive and redundant suggestions. We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness. © 2024 Copyright is held by the owner/author(s).

关键词： Integrated circuit layout

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A Gate Driving Scheme for GaN GIT with Enhanced Short Circuit Capability for Motor Drive Application 14

A Gate Driving Scheme for GaN GIT with Enhanced Short Circui...

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14th Annual IEEE Applied Power electronics Conference and Exposition, APEC 2025

作者： Zhou, Zongjie Cheng, Yan Chen, Kevin J. The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798331516116

Short circuit capability in power switching devices is highly desired for motor drive application, and it is a challenging issue for GaN power high-electron-mobility transistor (HEMT) devices because of concentrated heat generation and inefficient thermal dissipation that partially stems from much smaller die size. This paper proposes a current mode gate driving scheme with a shunt resistor in the gate loop for a GaN gate injection transistor (GIT) to prolong short circuit duration time and thus improve the system robustness and reliability. The shunt resistor not only can limit the short circuit peak current as a result of the source negative feedback but also can be used as a current sensing element that is indispensable for cost-sensitive motor control. The experiments demonstrate a greatly increased short circuit capability and no obvious compromise on other performances in motor drive application with the proposed gate driving scheme. © 2025 IEEE.

关键词： Traction motors

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A Scalable Multi-Hop IoT Network Using ESP-NOW for Remote Zone Monitoring Applications

A Scalable Multi-Hop IoT Network Using ESP-NOW for Remote Zo...

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2025 International Conference on electronics, Information, and Communication, ICEIC 2025

作者： Uddin, Raihan Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan44610 Korea Republic of

ISBN: (纸本)9798331510756

This paper presents the development of a scalable multi-hop Internet of Things (IoT) network utilizing ESP-NOW for remote zone monitoring applications. The proposed network employs ESP32 microcontrollers, where the source node is equipped with a DHT22 sensor to collect temperature and humidity data, which is then transmitted every 2 seconds through multiple relay nodes to a gateway node. The gateway node forwards data to a Node-RED server via Message Queuing Telemetry Transport (MQTT), enabling remote access to monitor the visualized data. The network design emphasizes scalability and reliability, addressing the challenges of long-distance data transmission without an internet connection by using ESP-NOW's peer-to-peer communication. The core features of this work include the implementation of relay nodes to extend the communication range, integration with Node-RED for real-time monitoring, and practical demonstration with environmental data. Experimental results validate the network's efficacy, high-lighting low latency and zero packet loss, even in complex environments. This approach provides a robust solution for remote monitoring in areas lacking traditional network infrastructure. © 2025 IEEE.

关键词： Gateways (computer networks)

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A Parallel Color Image Encryption Algorithm Based on a 2D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Ho-Ching Iu, Herbert Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong The School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China School of Electrical Electronic and Computer Engineering The University of Western Australia Perth6009 Australia School of Mathematical Sciences Dalian University of Technology Dalian116024 China Department of Computer Engineering Faculty of Engineering Karamanoglu Mehmetbey University Karaman70200 Turkey Department of Artificial Intelligence and Data Engineering Engineering Faculty Ankara University Ankara06830 Turkey Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This paper aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2D Logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multi-timescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a two-dimensional framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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Mitigating Power System Oscillations in PMSGWind Integrated Grids Using Power electronics and Inertia Control: A Comprehensive Review 16

Mitigating Power System Oscillations in PMSGWind Integrated ...

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16th Annual Power electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sabo, Aliyu Jibia, Kabir Garba Shahinzadeh, Hossein Hayati, Mohammad Mohsen Abapour, Mehdi Gharehpetian, Gevork B. Department of Electrical and Electronic Engineering Malaysia Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tabriz Iran Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331533946

The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularly those using permanent magnet synchronous generators (PMSG), are among the most widely adopted renewable energy solutions. Integrating WECS into traditional grids introduces challenges, including power system oscillations, especially during disturbances or faults. Inertia control and power electronics converters are pivotal in mitigating these oscillations and ensuring grid stability. This review investigates the design and optimization of damping controllers, emphasizing their integration with excitation systems to enhance the dynamic performance of PMSG-based wind integrated grids. Additionally, the role of power electronics in control strategies and inertia emulation for stable grid operation is discussed. The findings highlight the critical importance of advanced control measures in addressing the inherent uncertainties of renewable energy systems integrated with conventional grids. © 2025 IEEE.

关键词： Synchronous generators

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An encoder-decoder network for crowd counting based on multi-scale attention mechanism

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Multimedia Tools and Applications 2025年第3期84卷 1187-1210页

作者： Chuang, Hao-Hsiang Chen, Yi-Cheng Lin, Chang Hong Department of Electronic and Computer Engineering National Taiwan University of Science and Technology No. 43 Keelung Rd. Da’an Dist. Taipei Taiwan

Crowd counting is a challenging computer vision task, which is widely used in video surveillance and public safety applications. With the increase of camera resolution and the complexity of crowd image, it becomes an important problem to predict crowd density and crowd count accurately. Recent CNN-based density estimation methods have shown effectiveness in densely populated scenes. In this paper, we present a novel approach to crowd counting through the development of an Encoder-Decoder Multi-Scale Attention Network. Our approach leverages the robust U-net architecture as the backbone network, strengthened by the strategic integration of an attention mechanism. We adopt a multi-scale attention method to each different layers in the U-net backbone to make the network extract features which focus on the crowds, instead of the background in the images. The attention mechanism and the skip-connections can adjust the weights of feature maps while maintaining features at different scales. Extensive experiments on ShanghaiTech Part_A & B and UCF-QNRF dataset demonstrate that our network can achieve better performances with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values outperforming existing methodologies: ShanghaiTech Part_A (MAE/RMSE: 60.0/104.9), Part_B (MAE/RMSE: 7.8/13.8), and UCF-QNRF (MAE/RMSE: 98.6/179.7). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decoding

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Elevated-temperature dry sliding wear behaviors of dual-cathode HiPIMS TiN and TiZrN films

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Emerging Materials Research 2025年第1期14卷 98-106页

作者： Lin, Yu-Tse Liu, Wen-Chung Kuo, Chin-Chiuan Department of Power Mechanical Engineering National Formosa University Huwei Yunlin County Taiwan Department of Mechanical and Computer Aided Engineering National Formosa University Huwei Yunlin County Taiwan Graduate Institute of Aeronautical and Electronic Engineering National Formosa University Huwei Yunlin County Taiwan

Titanium–zirconium nitride films with three Zr/Ti ratios (16/35, 22/29, and 25/26) were deposited using Ti and Zr dual-cathode reactive high-power impulse closed-field magnetron sputtering by adjusting the average power ratio of two cathode materials. The sliding wear resistances of the obtained TiZrN films at different temperatures were compared. The results revealed that the ternary TiZrN film exhibited a higher hardness (approximately 35 GPa) and lower elastic modulus (approximately 360–380 GPa) than those of the binary TiN film. This contributed to the lower friction coefficient and wear resistance at room temperature. However, the addition of Zr became disadvantageous with an increase in wear temperature. Above 4008C, the surface hardness of the TiZrN film decreased dramatically with an increase in the Zr fraction. This is attributed to the increased vulnerability of the film to oxidation. Structural discontinuity induced by the inhomogeneous oxidation (a multilayer structure with voids, embedded oxides, and oxynitrides formed in the surface layer) roughened the film surface and reduced the surface strength, which accelerated the wear of TiZrN films at elevated temperature in an ambient atmosphere. Such ternary nitride coatings should not be applied in oxidative high temperature environments without obstructing the oxidation through designed composition and structure. © 2025 Emerald Publishing Limited: All rights reserved.

关键词： Vacuum deposited coatings

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An Anomaly Detection Model for RISC-V in Automotive Applications: A Domain-Specific Accelerator Perspective 33

An Anomaly Detection Model for RISC-V in Automotive Applicat...

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Vinciguerra, Elio Russo, Enrico Palesi, Maurizio Ascia, Giuseppe University of Catania Department of Electrical Electronic and Computer Engineering Catania Italy

ISBN: (纸本)9798331524937

Early anomaly detection in automotive systems is crucial for enhancing user safety and enabling timely corrective actions, thereby minimizing the risks associated with system malfunctions. This paper presents an approach for implementing Artificial Intelligence (AI)-based algorithms for anomaly detection in the automotive domain, leveraging the RISC-V architecture in conjunction with Domain-Specific Accelerators (DSAs). By exploiting the efficiency of DSAs, the proposed system aims to achieve faster anomaly detection compared to traditional processing methods. A detailed comparison is conducted between the performance of executing the AI-based anomaly detection algorithm on the RISC-V core versus offloading it to an optimized hardware accelerator tailored to the specific AI model. The goal of this work is to provide valuable insights into the potential of RISCV and DSAs to enhance AI-driven safety mechanisms, contributing to the development of more reliable automotive systems. © 2025 IEEE.

关键词： Anomaly detection

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Development of Intelligent Remote Sensing Payloads for CubeSat Applications

Development of Intelligent Remote Sensing Payloads for CubeS...

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2024 Small Satellites Systems and Services Symposium, 4S 2024

作者： Juang, Jyh-Ching Lee, Pei-Jun Department of Electrical Engineering National Cheng Kung University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

ISBN: (纸本)9781510688940

The paper describes the development of a remote sensing payload for CubeSat applications. The inclusion of artificial intelligence into remote sensing payload has been pursued in the IRIS (Intelligent Remote sensing and Internet Satellite) and Lilium CubeSat programs. The paper discusses the development of a series of intelligent remote sensing payloads for CubeSats to address the limitations on computational resources, power, and communication bandwidth. From 2018 to 2021, under the IRIS program, an optical remote sensing payload that contains a high focal-length telescope, a CMOS detector, and the data operation board was developed. An edge intelligence for landslide detection and cloud segmentation is investigated by using MobileNet V2 network. The design has been implemented and tested by using an airborne platform. The design is further refined in the Lilium program with enhanced resolution and frame rate. Functions including auto-focus, automatic white balance, and gain control are also realized in the payload. To account for low-illumination scenario, a zero DCE (zero reference deep curve estimation) is utilized. Further, a light-weight U-net is implemented in the FPGA to achieve cloud detection and segmentation and a YOLO-SPD is implemented for object detection. This self-developed payload has been prototyped and is under tests. The payload will be integrated into the 6U Lilium-2 CubeSat and is planned to be launched in 2025. © 2025 The Authors.

关键词： Proximity sensors

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