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arXiv 2024年

作者： Li, Shuai Ma, Xiaoguang Jiang, Shancheng Meng, Lu The College of Information Science and Engineering Northeastern University Shenyang110819 China The Foshan Graduate School Northeastern University Foshan528311 China The Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The School of Intelligent Systems Engineering Sun Yat-Sen University No. 135 Xingang Xi Road Guangzhou China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China

Remarkable successes were made in Medical Image Classification (MIC) recently, mainly due to wide applications of convolutional neural networks (CNNs). However, adversarial examples (AEs) exhibited imperceptible similarity with raw data, raising serious concerns on network robustness. Although adversarial training (AT), in responding to malevolent AEs, was recognized as an effective approach to improve robustness, it was challenging to overcome generalization decline of networks caused by the AT. In this paper, in order to reserve high generalization while improving robustness, we proposed a dynamic perturbation-adaptive adversarial training (DPAAT) method, which placed AT in a dynamic learning environment to generate adaptive data-level perturbations and provided a dynamically updated criterion by loss information collections to handle the disadvantage of fixed perturbation sizes in conventional AT methods and the dependence on external transference. Comprehensive testing on dermatology HAM10000 dataset showed that the DPAAT not only achieved better robustness improvement and generalization preservation but also significantly enhanced mean average precision and interpretability on various CNNs, indicating its great potential as a generic adversarial training method on the MIC. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistical tests

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Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

arXiv

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arXiv 2022年

作者： Gong, Shimin Wang, Meng Gu, Bo Zhang, Wenjie Hoang, Dinh Thai Niyato, Dusit The School of Intelligent Systems Engineering Shenzhen Campus of Sun Yatsen University China The School of Computer Sciences Minnan Normal University China The School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs’ data, while the UAVs’ network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs’ energy consumption and data queue size. Then, with the fixed network formation, the UAVs’ trajectories are further optimized by using multiagent deep reinforcement learning without knowing the GUs’ traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs’ flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs’ trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs’ cooperation in data offloading, thus improving energy efficiency and delay performance. © 2022, CC BY.

关键词： Reinforcement learning

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PreMatch: A Prediction-based Scheduler for dataflow Asynchronous Processing on the Computing Continuum 25

PreMatch: A Prediction-based Scheduler for Dataflow Asynchro...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Mehran, Narges Najafabadi Samani, Zahra Afzal, Samira Prodan, Radu Pallas, Frank Dorfinger, Peter Faculty of Digital and Analytical Sciences Paris Lodron University of Salzburg Salzburg Austria Intelligent Connectivity Group Salzburg Research Forschungsgesellschaft MbH Salzburg Austria Distributed and Parallel Systems Group University of Innsbruck Tyrol Innsbruck Austria Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Overijssel Enschede Netherlands Institute of Information Technology Alpen-Adria-University of Klagenfurt Carinthia Klagenfurt Austria

ISBN: (纸本)9798400706295

The popularity of asynchronous data exchange patterns has recently increased, as evidenced by 23% of the communication between microservices in an Alibaba trace analysis. Such workloads necessitate methods for reducing dataflow processing and completion time by forecasting the future requirements of their microservices and (re-)scheduling them. Therefore, we investigate a prediction-based scheduling method of asynchronous dataflow processing applications by considering the stochastic changes due to dynamic user requirements. We present a microservice scaling and scheduling method named PreMatch combining a machine learning (ML) prediction strategy based on gradient boosting with ranking and game theory matching scheduling principles. Firstly, PreMatch predicts the number of microservice replicas, and then, the ranking method orders the microservice replicas and devices based on microservice and transmission times. Thereafter, the PreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis of the PreMatch method shows lower completion times on average 13% compared to a related prediction-based scheduling method. Copyright © 2025 held by the owner/author(s).

关键词： data curation

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Performance Investigation of Instantaneous Reactive Power Control for 5 Level-Neutral Point Clamped Grid-Connected Photovoltaic Inverter

Performance Investigation of Instantaneous Reactive Power Co...

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

作者： Oumaymah Elamri Alla Eddine Toubal Maamar Charaf Chermite Moulay Rachid Douiri Mostefa Kermadi Saad Mekhilef LAMIGEP multidisciplinary research laboratory École Marocaine des Sciences de l'Ingénieur (EMSI) of Marrakech Marrakech Morocco Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara of Boumerdes Boumerdes Algeria Physics Department Control and Computer Science for Intelligent Systems and Green Energy Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco School of Engineering University of Leicester Leicester United Kingdom Power Electronics and Renewable Energy Research Laboratory (PEARL) Kuala Lumpur Malaysia Swinburne University of Technology Hawthorn Vic Australia

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The utilization of solar energy in grid-connected photovoltaic installations has become increasingly significant. This article introduces a photovoltaic system seamlessly integrated with a three-phase network. The system employing a five-level Neutral Point Clamped (NPC) voltage source inverter is designed for a single-step connection to enhance power quality in the distribution network. The control technique employed for the efficient functioning of the photovoltaic system is based on the notion of instantaneous reactive power. Notably, this strategy is straightforward, operates the system throughout the day, and eliminates the need for Phase-Locked Loop (PLL) usage. The Pulse-Width Modulated (PWM) Current Controller sends switching signals to the NPC multilevel inverter, which does a great job of dynamic performance under steady-state and transient situations. The proportional-integral controller regulates the voltage of the Uninterruptible Power Supply DC bus capacitors. The proposed system undergoes comprehensive simulation validation under various irradiation levels.

关键词： Photovoltaic systems Reactive power Voltage source inverters Switches Solar energy Steady-state Transient analysis

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Optimizing Carbon Emissions in Green Logistics for Time-Dependent Routing

SSRN

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SSRN 2024年

作者： Liu, Yiming Yu, Yang Roberto, Baldacci Tang, Jiafu Sun, Wei State Key Laboratory of Synthetic Automation for Process Industries Institute of Intelligent Data and Systems Engineering Northeastern University Shenyang110819 China Dalian University of Technology School of Economics and Management Liaoning Dalian116024 China Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation P.O. Box 34110 Doha Qatar College of Management Science and Engineering Dongbei University of Finance and Economics Shahekou Dalian116026 China Business School Liaoning University Shenyang110316 China

This paper considers a green vehicle routing problem termed the time-dependent green vehicle routing problem with time windows (TDGVRPTW). The TDGVRPTW is an extension of the green vehicle routing problem with time windows in green logistics. It considers time-dependent vehicle speed and aims to minimize carbon emissions. Since the travel times and carbon emissions between locations depend on the departure time from the starting location, optimizing carbon emissions requires determining the optimal departure times from both the depot and customers. This paper presents a branch-price-and-cut (BPC) algorithm to solve the TDGVRPTW. The problem is formulated based on a set-partitioning model. To solve the pricing problem associated with the set-partitioning model, we first define backward non-dominated time-dependent arcs and optimal timed routes. We also introduce three types of routes, forward, backward, and patchwork optimal timed routes, and analyze their characteristics. Then, we introduce a method to adjust the times in backward and patchwork optimal timed routes from the latest to the earliest. We prove that this method guarantees correctness and effectively reduces complexity. We propose a bidirectional labeling algorithm to generate routes. The pricing problem employs state-of-the-art techniques, including limited memory subset row cuts (lm-SRCs), ng-route relaxation, and bucket graphs to enhance the algorithm’s efficiency. The BPC algorithm is tested on a set of instances derived from benchmark instances in the literature. The results demonstrate the effectiveness of the proposed algorithm in solving the TDGVRPTW. © 2024, The Authors. All rights reserved.

关键词： Travel time

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Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees

Learning-enhanced robust controller synthesis with rigorous ...

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IEEE Conference on Decision and Control

作者： Christian Fiedler Carsten W. Scherer Sebastian Trimpe Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen Germany Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart Germany University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781665436601

The combination of machine learning with control offers many opportunities, in particular for robust control. However, due to strong safety and reliability requirements in many real-world applications, providing rigorous statistical and control-theoretic guarantees is of utmost importance, yet difficult to achieve for learning-based control schemes. We present a general framework for learning-enhanced robust control that allows for systematic integration of prior engineering knowledge, is fully compatible with modern robust control and still comes with rigorous and practically meaningful guarantees. Building on the established Linear Fractional Representation and Integral Quadratic Constraints framework, we integrate Gaussian Process Regression as a learning component and state-of-the-art robust controller synthesis. In a concrete robust control example, our approach is demonstrated to yield improved performance with more data, while guarantees are maintained throughout.

关键词： Robust control Linear systems Learning systems Uncertainty Systematics Machine learning Control theory

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Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro

Digital Innovations Development: Causal factors and relation...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Ivana Ognjanović Luka Laković Ramo Šendelj Laszlo Bokor Peter A. Kara Christoph Reich Manav Madan Milovan Roganović Emmanouil Zouilas John Mantas Jevto Eraković Tanja Radusinović Nada Rakočević Faculty for Information Systems and Technologies University of Donja Gorica Podgorica Montenegro Humanistic Studies University of Donja Gorica Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Institute of Data Science Cloud Computing and IT Security Furtwangen University Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens Goudi Greece Chamber of Economy of Montenegro Podgorica Montenegro

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts.

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DRL-Based Pricing-Driven for Task Offloading and Dynamic Resource in Vehicle Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Sijun Yang, Liang Li, Junjie Guo, Hongzhi Ahmad, Ishtiaq Da Costa, Daniel Benevides Jiang, Hongbo Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China Czech Technical University in Prague Department of Telecommunication Engineering Faculty of Electrical Engineering Prague166 27 Czech Republic Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and Weighted Centroid Method

SSRN

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SSRN 2023年

作者： Sankaranarayanan, Suresh Rajaram, V. Srividhya, S. Lorenz, Pascal Rodrigues, Joel J.P.C. Department of Computer Science College of Computer Science and Information Technology King Faisal University Al Hofuf Saudi Arabia Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India University of Haute Alsace Colmar France Qingdao266555 China Instituto de Telecomunicações Covilhã6201-001 Portugal

Localization of nodes is critical for acquiring access to diverse nodes that would provide services in remote places. Single-anchor localization techniques suffer from a co-linearity problem, resulting in poor performance. The reliable multiple anchor selection method can be computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. There has been work where researchers have employed nature-inspired algorithms over conventional optimization method towards localization of unknown nodes in WSN. These methods have challenges like longer time and lot of processing power to solve. In addition, these methods are sensitive to parameter selection impacting the ***, we in this research work have employed the nature-inspired crow search algorithm for selecting the suitable anchors from the population thereby reducing errors in identifying the location of unknown nodes. In addition, the weighted centroid method is proposed for the identification of the exact location of an unknown node. This makes the weighted centroid method and the crow search algorithm a more trustworthy and efficient way for node localization in wireless sensor networks. The Crow Search-based Weighted Centroid Localization (CS-WCL) has outperformed WCL and DV-Hop with the use of the best anchor node in terms of Localization Error, Total data packets, and Energy Consumption. This resulted in CSWCL outperforming other nature-inspired algorithms for node localization. © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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Proposal for a Knowledge Map in Programming Education Curriculum for IT Talent Development in Japan

Proposal for a Knowledge Map in Programming Education Curric...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Hiroto Yamakawa Haruki Ueno Kana Ohashi Akihiro Matsuda Osamu Hasegawa Hiroshi Komatsugawa Department of Information Systems Engineering Chitose Institute of Science and Technology Chitose Hokkaido Japan Faculty of Science and Technology Chitose Institute of Science and Technology Chitose Hokkaido Japan Graduate School of Science and Technology Chitose Institute of Science and Technology Chitose Hokkaido Japan Chitose Hokuyou High School Chitose Hokkaido Japan Department of Data Science Musashino University Ariake Koto-Ku Tokyo

This study proposes a knowledge map for standardizing programming education across Japan and focuses on bridging the curriculum gaps between elementary, middle, and higher education. Utilizing a computer adaptive testing system for empirical validation, a pilot implementation in a high school class revealed knowledge gaps in advancing levels of comprehension and application, especially when transitioning to complex elements such as a pseudocode.

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