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Research Square 2023年

作者： Gatto, Elaine Cecília Ferrandin, Mauri Cerri, Ricardo Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil

Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once, and local, which divides the problem into many binary problems. In both approaches, learning label correlations is still an open issue. In this paper, we propose a method to cluster the label space and find partitions of disjoint correlated labels called hybrid partition, which can be considered in-between the local and global, combining the benefits of both. Our proposal entails: i) clustering the label space based on label correlations;ii) generating and validating the resulting hybrid partitions;iii) selecting the optimal partitions between them;and iv) evaluating their performances. Since we are also interested in analyzing how hybrid partitions are close to the best possible partitions, we compared our results with the ones obtained using partitions found by an oracle, an exhaustive, and a random search. The oracle and exhaustive strategies generate all possible label partitions for a dataset. While the oracle chooses the best one based on the test set alone, the exhaustive choices are based on validation. Experiments on eight well-known multi-label datasets revealed that both our proposal and the random partitions achieve superior or competitive results compared with the others. This indicates that the conventional global and local approaches may not accurately account for label correlations, requiring significant improvements. In this case, clustering the label space can be a solution. © 2023, CC BY.

关键词： Classification (of information)

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IoT-based Wireless Sensor Networks for Monitoring Drinking Water Treatment Plants

IoT-based Wireless Sensor Networks for Monitoring Drinking W...

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International Workshop on Secure Internet of Things (SIoT)

作者： Luiz Octávio Martini Lima Lucas Carvalho Goncalves Gustavo Campos Menezes Lucas Silva de Oliveira Gleb Wataghin Institute of Physics UNICAMP Campinas SP Brazil Master Degree Program in Automation and Systems CEFET-MG Leopoldina MG Brazil Department of Electronics and Computing CEFET-MG Contagem MG Brazil Department of Mechatronics Engineering CEFET-MG Divinópolis MG Brazil

ISBN: (数字)9798331507466

ISBN: (纸本)9798331507473

The increasing need for efficient monitoring of parameters in challenging industrial environments, such as basic sanitation, has led to the search for integrated and user-friendly solutions that can address specific challenges of this setting. This project aims to develop a solution to meet monitoring requirements in water treatment plants. The proposed solution involves the development of both hardware and software for collecting data from the water treatment process using sensors and transmitting this information to a server structure, database, and communication interface with the cloud-based user. The effectiveness of the proposed device is being evaluated in six water treatment units located in the states of Alagoas, Mato Grosso, Paraná, Rio de Janeiro, and São Paulo. Preliminary results indicate that the proposed solution is capable of monitoring the variables involved in the water flocculation stage and generating a comprehensive set of information about the process over time.

关键词： Cloud computing Chemical sensors Wireless sensor networks User interfaces Hardware Software Sensors Social Internet of Things Servers Monitoring

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LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging...

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IEEE Symposium on Intelligent Vehicle

作者： Weiyi Xiong Jianan Liu Tao Huang Qing-Long Han Yuxuan Xia Bing Zhu School of Automation Science and Electrical Engineering Beihang University Beijing P.R. China Vitalent Consulting Gothenburg Sweden College of Science and Engineering James Cook University Cairns QLD Australia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving [1] . Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, sampling has been applied in a few image-based detectors and shown to outperform the widely applied depth-based splatting proposed in Lift-Splat-Shoot (LSS) [2] , even without image depth prediction [3] . However, the potential of sampling is not fully unleashed. As a result, this paper investigates the sampling strategy on the camera and 4D imaging radar fusion-based 3D object detection. In the proposed LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird’s eye view (BEV) features, respectively. They are sent to the core of LXL, called radar occupancy-assisted depth-based sampling , to aid image view transformation.

关键词： Performance evaluation Point cloud compression Solid modeling Three-dimensional displays Laser radar Radar detection Object detection

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis

Complex Permeability Determination for EE Core Based on Flex...

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China Power Supply Society Electromagnetic Compatibility Conference (CPEMC)

作者： Huamin Jie Zhenyu Zhao Yongqi Chang Changdong Wang Shiqi Jiang Richard Xian-Ke Gao Kye Yak See School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China A*STAR Institute of High Performance Computing Singapore Singapore

ISBN: (数字)9798331509552

ISBN: (纸本)9798331509569

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz.

关键词： Magnetic flux Accuracy Flexible printed circuits Magnetic cores Fixtures Electromagnetic interference Permeability measurement Length measurement Permeability Inductors

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Predicting Central Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Multimodal Data 8th

Predicting Central Lymph Node Metastasis in Papillary Thyroi...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Shao, Rui Ben, JiangYuan Lin, Pengcheng Ge, Shu Wu, ChengGang Zhang, Kun He, Ying School of Electrical Engineering and Automation Nantong University Nantong China Department of Ultrasound Tumor Hospital Affiliated to Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong China Nantong Institute of Technology Jiangsu Nantong China

ISBN: (纸本)9789819601875

During thyroid cancer surgery, the debate continues regarding whether to perform routine prophylactic central lymph node dissection on the same side as the lesion. Central lymph node metastasis has been proven to be clinically significant in guiding treatment decisions. Therefore, accurately predicting central lymph node metastasis preoperatively is an significant step in the diagnosis and treatment of thyroid cancer. In clinical practice, ultrasound examination is commonly used to assess thyroid cancer. However, due to the low contrast and high noise of ultrasound images, the accuracy of ultrasound in identifying lymph node metastasis is limited. This study proposes a preoperative prediction of lymph node metastasis based on MFF-Resnet, which is based on Resnet50. This paper constructs multimodal data by combining deep learning with radiomics and clinical factors, and uses auxiliary branches to incorporate original information and low-level features of ultrasound images for prediction tasks. The AUC of MFF-Resnet reaches 0.8576, with an accuracy of 0.8018, and an F1 score of 0.8281. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Domain-adaptive state of health prediction of vehicle batteries powered by deep learning

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Cell Reports Physical Science 2025年第5期6卷

作者： Li, Minghe Fei, Zicheng Yang, Luoxiao Zhang, Zijun Tsui, Kwok-Leung Department of Data Science College of Computing City University of Hong Kong Hong Kong Hong Kong School of Future Science and Engineering Soochow University Suzhou China College of Automation and Information Engineering Xi'an University of Technology Xi'an China Department of Industrial Manufacturing and Systems Engineering at the University of Texas-Arlington Arlington TX United States

State of health (SOH) estimation of battery packs in electric vehicles (EVs) is essential for transportation electrification safety and reliability. The noise and complexity of EV battery pack data hinder the effectiveness of various data-driven SOH estimation methods using lab data. To address these challenges and achieve more effective data-driven EV battery pack SOH predictions, this study develops a comprehensive deep-learning-based SOH modeling framework for EV batteries. The framework begins with a two-stage mode decomposition (TSMD) method designed to effectively identify neat SOH degradation patterns better representing noisy field data. Next, an endogenous and exogenous multibranch network structure with a hierarchically fused attention mechanism (EEMB-HiFA) is developed for real-time prediction of EV battery pack SOH. Computational experiments leveraging datasets from seven EVs are conducted to validate the accuracy and adaptiveness of the proposed EEMB-HiFA. The results show that the EEMB-HiFA can achieve a 96.49% improvement in accuracy compared to strong benchmarks considered. © 2025 The Author(s)

关键词： data feature fusion data-driven model deep learning domain adaptation electric vehicles lithium-ion battery signal denoising state of health

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Code Converters Based on a Dynamic Optical RAM for Information System Facilities

Code Converters Based on a Dynamic Optical RAM for Informati...

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IEEE International Conference on Electronics and Nanotechnology (ELNANO)

作者： Andriy Semenov Serhii Tsyrulnyk Olena Semenova Anton Khloba Andrii Rudyk Serhii Baraban Faculty of Information Electronic Systems Vinnytsia National Technical University Vinnytsia Ukraine Department of Computer Science and Economic Cybernetics Vinnytsia National Agrarian University Vinnytsia Ukraine Department of Automation Electrical Engineering and Computer-Integrated Technologies National University of Water and Environmental Engineering Rivne Ukraine Faculty of Computing and Telecommunications Poznan University of Technology Poznan Poland

ISBN: (数字)9798350368178

ISBN: (纸本)9798350368185

In this paper, the authors propose a new dynamic optical RAM based on a fiber-optic line. This device significantly increases the speed of information flows by reducing the time required to operate the RAM by 10. The proposed optical RAM increases the productivity and efficiency of information exchange between slow and fast devices of information and communication systems without software costs, which reduces the probability of failure by two times with the same buffer memory capacity. The authors proposed variants of the practical application of the new dynamic optical RAM. On its basis, a shift register, a serial-to-parallel code converter, a parallel-to-serial code converter, and a TDM switch for information system facilities were created. The time characteristics of signals in code converters based on a dynamic optical RAM are presented.

关键词： Optical fibers Codes Optical switches Optical buffering Random access memory Time division multiplexing Shift registers Optical fiber devices High-speed optical techniques Information systems

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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Two-Operation Buffering based on Dynamic Optical RAM

Two-Operation Buffering based on Dynamic Optical RAM

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International Conference on Modern Problems of Radio engineering, Telecommunications and Computer Science

作者： Andriy Semenov Serhii Tsyrulnyk Serhii Baraban Olena Semenova Andrii Rudyk Anton Khloba Faculty of Information Electronic Systems Vinnytsia National Technical University Vinnytsia Ukraine Department of Computer Sciences and Digital Economy Vinnytsia National Agrarian University Vinnytsia Ukraine Faculty of Computing and Telecommunications Poznan University of Technology Poznan Poland Department of Automation Electrical Engineering and Computer-Integrated Technologies National University of Water and Environmental Engineering Rivne Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

The authors of this paper propose a block diagram for a dual-operating buffer on a dynamic optical RAM. The buffer consists of two dynamic optical RAMs, each controlled separately to allow for simultaneous writing and reading of data, thereby improving performance. This setup enables data to be read from the buffer while new data is being written to it. Implementing a dual-operating buffer on a dynamic optical RAM has the potential to enhance the performance of high-speed data transmission in telecommunications networks. The authors conducted numerical calculations to compare failure probabilities between single-operation and dual-operation buffers on dynamic optical RAM. The results indicated that using a dual-operating buffer significantly reduces the probability of failure with the same buffer memory capacity compared to a single-operating buffer. Additionally, the authors analyzed the effectiveness of the proposed dual-operating buffer. The analysis confirmed that exploiting dynamic optical memory for buffering enhances data transfer speed and efficiency due to the inherent properties of optical data transfer, such as high bandwidth and low latency.

关键词： Optical buffering Random access memory Probability Optical fiber networks Writing Data transfer Software Telecommunications High-speed optical techniques Capacity planning

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