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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Abdul Saboor Evgenii Vinogradov Zhuangzhuang Cui Sofie Pollin Department of Electrical Engineering (ESAT) WaveCoRE KU Leuven Belgium Autonomous Robotics Research Center Technology Innovation Institute UAE

The integration of Non-Terrestrial Networks (NTNs) into 6G networks is one of the most promising ways to achieve significant improvements in capacity, reliability, and global coverage. The design of NTN heavily relies on using channel models. In this paper, we propose two easy-to-use simulators for estimating the Line-of-Sight (LoS) probability PLoS in a 3D urban environment. The first simulator is a 3D city simulator that employs simplified Ray Tracing (RT), while the second one is a lightweight geometry-based simulator that generates only the relevant buildings between users and Unmanned Aerial Vehicles (UAVs). Using these simulators, we assess the accuracy of existing models for PLoS estimation and examine PLoS for different UAV heights, user-UAV distances, and azimuth/elevation angles. Our results show that 1) existing models overestimate PLos, resulting in overoptimistic NTN performance predictions, 2) user locations (including azimuth and elevation angles) are an important factor influencing PLos, however, this influence is not captured by the existing models.

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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IEEE International Conference on robotics and Biomimetics

作者： Tian Xiang Qiwei Meng Ji Zhang Beibei Zhang Wei Song Anhuan Xie Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong HKSAR China Research Center for Intelligent Computing Software Research Institute of Intelligent Computing Zhejiang Laboratory Hangzhou China Electrical and Computer Engineering Dalhousie University Halifax Canada

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model’s partitioning strategy. It also serves as an important metric influencing the model’s suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future.

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Effects of plant protection UAV-based spraying on the vertical distribution of droplet deposition on Japonica rice plants in Northeast China

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International Journal of Agricultural and Biological engineering 2021年第5期14卷 27-34页

作者： Yingli Cao Fenghua Yu Tongyu Xu Wen Du Zhonghui Guo Haiyan Zhang College of Information and Electrical Engineering Shenyang Agricultural UniversityShenyang 110866China Liaoning Engineering Research Center for Information Technology in Agriculture Shenyang 110866China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China

In order to study the regularity of the vertical distribution of droplet deposition on rice plants during pesticide spraying operated by a low-flying multi-rotor plant protection unmanned aviation vehicle(UAV),water-sensitive spray cards were placed on the leaves at the top,in the middle,and at the bottom of rice plants to acquire data on droplet *** this study,a suspension containing tricylazole and hexaconazole was used in the *** water-sensitive spray cards were analyzed by the droplet deposition processing software iDAS to obtain the vertical distribution of the droplets sprayed by the plant protection *** results showed that (1)significant variation was found in the coverage of the droplets in different vertical positions of the rice *** the effective spray width,the best coverage of the droplets was found in the area just below the rotors,whereas the coverage of areas farther away from the rotors was *** the different vertical positions of the rice plants,the overall droplet coverage was 58.38%at the top,33.55%in the middle,and 11.34%at the bottom of the plants;(2)for all vertical positions,the average size of the droplets ranged between 110μm and 140μm,which was suitable for the control of plant diseases and insect *** highest droplet density was found at the top of the plants,and the distribution of the droplet density was similar in the middle and at the bottom of the rice plants;(3)the diffusion ratio at the top of the rice plants(0.84)was better than that in the middle(0.57)and at the bottom(0.37).The overall relative span could meet the requirements for the actual *** for the position in the middle of the plants,the relative span for the other positions of the plants was over 0.67,which is the standard *** study demonstrated the distribution regularity of droplet deposition along with the vertical direction of rice plants during UAV-based spraying,which is of guiding significance for the u

关键词： plant protection UAV droplet deposition Japonica rice droplet size vertical distribution spraying Northeast China

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Probability of Line of Sight Evaluation in Urban Environments using 3D Simulator

arXiv

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arXiv 2023年

作者： Saboor, Abdul Vinogradov, Evgenii Cui, Zhuangzhuang Pollin, Sofie Department of Electrical Engineering KU Leuven Belgium Autonomous Robotics Research Center Technology Innovation Institute United Arab Emirates

The integration of Non-Terrestrial Networks (NTNs) into 6G networks is one of the most promising ways to achieve significant improvements in capacity, reliability, and global coverage. The design of NTN heavily relies on using channel models. In this paper, we propose two easy-to-use simulators for estimating the Line-of-Sight (LoS) probability PLoS in a 3D urban environment. The first simulator is a 3D city simulator that employs simplified Ray Tracing (RT), while the second one is a lightweight geometry-based simulator that generates only the relevant buildings between users and Unmanned Aerial Vehicles (UAVs). Using these simulators, we assess the accuracy of existing models for PLoS estimation and examine PLoS for different UAV heights, user-UAV distances, and azimuth/elevation angles. We conclude that 1) existing models overestimate PLoS, resulting in overoptimistic NTN performance predictions, 2) nodes location (including azimuth and elevation angles) is an important factor influencing PLoS, however, this influence is not captured by the existing models. Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

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ICP Enhancement Algorithm for 6d Object Pose Trackingthm

SSRN

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SSRN 2024年

作者： Hwang, Hyunho Shin, Hyunsoo Bae, Ji-Hun Lee, Sungon Department of Electrical and Electronic Engineering Hanyang University Ansan Korea Republic of Research Institute of Engineering and Technology Hanyang University Ansan Korea Republic of Department of Robotics Hanyang University Ansan Korea Republic of

The current robot application is expanding into the service industry, such as household mobile robots and serving robots. Real-time 6D pose tracking plays an important role in enabling robots to track and grasp household items based on camera images. While most state-of-the-art techniques in 6D pose tracking rely on deep learning networks, incorporating an additional correction step, such as ICP, is essential for reducing residual pose errors. Furthermore ICP has frequently employed in pose tracking scenarios as the primary method due to its proper performance with low resource consumption. However, There are several challenges such as RGB utilization and handling flat or symmetric objects. These challenges let ICP prone to failure in tracking scenarios. In this paper, we introduce ICP enhancement algorithm to effectively utilize ICP for tracking household items. We analyzed three key challenges: inability to handle RGB information, difficulty in dealing with flat or rotationally symmetric objects, and inability to self-recover when tracking fails. We presented practical solutions to these challenges and assessed how they improved the performance of pose tracking. The experimental results demonstrate that our ICP enhancement algorithm can be effectively applied to tracking with only 3D model. © 2024, The Authors. All rights reserved.

关键词： Service industry

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis 5

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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5th International Conference on System Reliability and Safety engineering, SRSE 2023

作者： Li, Ruiqi Wang, Kai Zeng, Peng Cao, Jiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Harbin Institute of Technology Department of Electrical Engineering China

ISBN: (纸本)9798350305944

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed. © 2023 IEEE.

关键词： Failure analysis

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A Bidirectional Solid-State Power Controller for 270V HVDC Aviation Power Supply System 5

A Bidirectional Solid-State Power Controller for 270V HVDC A...

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5th IEEE International electrical and Energy Conference, CIEEC 2022

作者： Fei, Yuqing Pu, Fan Li, Yao Liu, Yuyang Tao, Yufei Li, Weilin Northwestern Polytechnical University School of Automation Department of Electrical Engineering Xi'an China Northwestern Polytechnical University Xi'an China Qingdao Research Institute of Northwestern Polytechnical University Qingdao China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

270V high-voltage direct-current (HVDC) power supply system is the preferred power supply scheme for multi-electric aircraft (MEA). In order to improve the intelligence and reliability of the power supply system, the HVDC solid-state power controller (SSPC) is gradually replacing the traditional mechanical protection device. A 270V/20A bidirectional HVDC SSPC is designed in this paper. Firstly, the modular modeling of SSPC is carried out, and the parameter calculation method is given. Then, the SSPC simulation model is built in Saber to verify its functions of quickly isolating short-circuit fault, supporting the opening and closing of capacitive load and inductive load, and accurate overcurrent protection according to the set inverse time protection curve. According to the simulation results, the parameter selection is optimized. Finally, the prototype test results also verify the effectiveness of the design. © 2022 IEEE.

关键词： Timing circuits

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Learn2Evade: Learning-based generative model for evading PDF malware classifiers

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第4期2卷 299-313页

作者： Bae, Ho Lee, Younghan Kim, Yohan Hwang, Uiwon Yoon, Sungroh Paek, Yunheung Department of Cyber Security EwhaWomans University Seoul03760 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Institute of NewMedia and Communications Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as adversarial examples. Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding adversarial examples against ML-based portable document format (PDF) malware *** deem that our problem is more challenging than those againstMLmodels for image processing because of the highly complex data structure of PDF and of an additional constraint that the generated PDF should exhibit malicious behavior. To resolve our problem, we propose a variant of generative adversarial networks that generate evasive variant PDF malware (without any crash), which can be classified as benign by various existing classifiers yetmaintaining the original malicious behavior. Our model exploits the target classifier as the second discriminator to rapidly generate an evasive variant PDF with our new feature selection process that includes unique features extracted from malicious PDF files. We evaluate our technique against three representative PDF malware classifiers (Hidost 13, Hidost 16, and PDFrate-v2) and further examine its effectiveness with AntiVirus engines from VirusTotal. To the best of our knowledge, our work is the first to analyze the performance against the commercial AntiVirus engines. Our model finds, with great speed, evasive variants for all selected seeds against state-of-The-Art PDF malware classifiers and raises a serious security concern in the presence of adversaries. © 2021 institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Generative adversarial networks

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Enhancing Resource Allocation and Performance in Multi-Layer Industrial IoT Through Adjustable Strategy Integrating Cooperative Communication and Edge Computing

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IEEE Internet of Things Journal 2025年第12期12卷 19732-19744页

作者： Sun, Mingyue Yuan, Yazhou Ma, Kai Chen, Cailian Zhang, Jianmin Luo, Xiaoyuan The Institute of electrical Engineering Yanshan University Qinhuangdao066004 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Automation Department of the Baosteel Research Institute Shanghai200240 China

Reliable bidirectional communication between the control center (CC) and manufacturing devices (MDs) along with efficient resource allocation are critical for the Industrial Internet of Things (IIoT). However, due to the limited network resources of IIoT devices and the intertwined nature of communication and computation resources, achieving efficient optimization of these resources presents significant challenges. In this paper, we propose a multi-layer communication architecture for the IIoT based on edge computing and cooperative communication technologies, where resource-constrained MDs upload data to edge servers (ESs) for processing. To address the issues of resource scarcity and coupling, we establish a bandwidth release model (BRM) to analyze and quantify the relationship between computation and communication resources. This elucidates the interaction mechanisms between ‘transmission-computation’ performance. Furthermore, to prevent excessive data upload to ESs, which could lead to node overload and network congestion, we employ game theory to develop a pricing mechanism for resource allocation, where ESs charge for computation services. Specifically, we construct a Lagrangian framework to determine a resource allocation scheme, aiming to maximize the utility of the factory. Simulation results indicate that compared to common methods employed in existing works, our strategy enhances factory utility by 29.79%. © 2014 IEEE.

关键词： Edge computing

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Detecting Electricity Theft in Smart Grids: A Hybrid CNN-RF Approach 7

Detecting Electricity Theft in Smart Grids: A Hybrid CNN-RF ...

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7th International Youth Conference on Radio Electronics, electrical and Power engineering, REEPE 2025

作者： Bashir, Safia Babikir Farag, Mena Maurice Hamid, Abdul-Kadir Ismail, Ali Ahmed Bansal, Ramesh C. Elnady, A. Sustainable Energy and Power Systems Research Centre Research Institute of Sciences and Engineering (RISE) University of Sharjah Sharjah United Arab Emirates Smart Automation and Communication Technologies Research Center (SACT) University of Sharjah PO Box 27272 Sharjah United Arab Emirates College of Engineering University of Sharjah Department of Electrical Engineering PO Box 27272 Sharjah United Arab Emirates

ISBN: (纸本)9798331531836

Electricity theft poses a significant economic and operational challenge to modern power grids, resulting in substantial financial losses and compromising grid reliability. Traditional detection methods, such as manual inspection and basic statistical analysis, are insufficient for addressing the complexity and scale of theft in smart grids. This study proposes a hybrid machine learning model, CNN-RF, combining convolutional neural networks (CNNs) for feature extraction and random forests (RF) for classification. Using a publicly available dataset spanning 12 months of hourly energy consumption data across 16 consumer categories, the model achieves superior accuracy of 88.84%, precision of 86.55%, recall of 88.84%, and an F1-score of 86.60% compared to standalone CNN, RF, and support vector machine (SVM) models. Specifically, CNN-RF improves accuracy by 1.6% over RF and 0.8% over CNN, demonstrating the advantage of integrating deep feature extraction with robust classification. © 2025 IEEE.

关键词： Deep learning Electricity Theft Detection Energy Consumption Machine Learning Smart Meters

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