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IEEJ Transactions on Electronics, Information and systems 2024年第9期144卷 894-900页

作者： Hadi, Danang Kumara Song, Zequn Rahmadya, Budi Sun, Ran Takeda, Shigeki Graduate School of Science and Engineering Ibaraki University 4-12-1 Nakanarusawa Ibaraki Hitachi316-8511 Japan Department of Agroindustrial Technology Universitas Muhammadiyah Jember Jember68124 Indonesia Computer System Department Faculty of Information Technology Andalas University Padang25175 Indonesia

This research introduces real-time monitoring and localizing product stock using the First-In-First-Out (FIFO) method with radio frequency identification (RFID) pressure sensing tags. The proposed FIFO system has RFID pressure sensing tags that detect pressure changes, generate sensor codes, and estimate a stock location. Integrating artificial intelligence (AI) improves location estimation accuracy of up-to-date stock visibility. The ability of RFID pressure sensing tags and AI to detect stock from sensor code data enables more effective asset management, optimizes stock management, reduces product waste, and contributes to better efficiency in product inventory management. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Inventory control

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FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection 42

FedMoS: Taming Client Drift in Federated Learning with Doubl...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Xiong Chen, Yuxin Li, Yuqing Liao, Xiaofei Jin, Hai Li, Bo Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Federated learning (FL) enables massive clients to collaboratively train a global model by aggregating their local updates without disclosing raw data. Communication has become one of the main bottlenecks that prolongs the training process, especially under large model variances due to skewed data distributions. Existing efforts mainly focus on either single momentum-based gradient descent, or random client selection for potential variance reduction, yet both often lead to poor model accuracy and system efficiency. In this paper, we propose FedMoS, a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance. Specifically, FedMoS maintains customized momentum buffers on both server and client sides, which track global and local update directions to alleviate the model discrepancy. Taking momentum results as input, we design an adaptive selection scheme to provide a proper client representation during FL aggregation. By optimally calibrating clients' selection probabilities, we can effectively reduce the sampling variance, while ensuring unbiased aggregation. Through a rigid analysis, we show that FedMoS can attain the theoretically optimal O(T - 2/3) convergence rate. Extensive experiments using real-world datasets further validate the superiority of FedMoS, with 58%-87% communication reduction for achieving the same target performance compared to state-of-the-art techniques. © 2023 IEEE.

关键词： Momentum

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UWM-Net: A Mixture Density Network Approach with Minimal Dataset Requirements for Underwater Image Enhancement

UWM-Net: A Mixture Density Network Approach with Minimal Dat...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Jun Huang Zongze Li Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The learning-based underwater image enhancement, which is suitable for batch processing, is a pivotal research direction in underwater image processing. Extensive paired image data are required in existing learning-based methods, which necessitate considerable preprocessing and hinder the application of these methods. To address these limitations, we propose a semi-supervised approach called UWM-Net: firstly, we use a compact dataset of underwater image pairs to train the Mixture Density Network (MDN) with an underwater scene setting; subsequently, U-Net can learn underwater image enhancement more efficiently. The MDN can transform standard images into underwater scenes, reducing the reliance on paired data and making much smaller training datasets. In experimental studies, UWM-Net using only 18 pairs of underwater image data achieves highly competitive results in terms of 3 metrics compared with advanced models.

关键词： Measurement Training Learning systems Image quality Image color analysis Refining Training data

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A Review on the Role of Microrobots and Nanorobots used in Healthcare Domain 6

A Review on the Role of Microrobots and Nanorobots used in H...

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6th Parul International Conference on engineering and technology, PICET 2024

作者： Kumar, Shravan Kumar, Praveen Kumar, Rajan Kumar, Mukesh Kumar, Dhiraj Kumar, Gulshan Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798350369748

Advanced technology such as microrobots and nanorobots have the potential to completely transform the healthcare industry. These tiny robotic devices provide fine control for a range of biological applications since they operate at the micro and nanoscales. This review explores the various healthcare uses, fabrication ideas, and design concepts of microrobots and nanorobots. It specifically looks at their works in tissue engineering, minimally invasive surgery, and targeted drug administration. Aiming to harness the transformative impact of these emerging technologies in healthcare, research and practitioners can recive significant insights from the discussion of challenges and future directions in the sector. design-wise, microrobots and nanorobots display a range of configurations suited to particular uses, such as payload delivery systems, sensing capabilities, and propulsion mechanisms. © 2024 IEEE.

关键词： Microrobots

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An efficient retrieval of relational database information based on knowledge graph

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International Journal of Information and Communication technology 2023年第3期23卷 201-214页

作者： Zhu, Hongqin Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Jiangxi Nanchang330013 China Department of Information Engineering East China University of Technology 418 Guanglan Avenue Jiangxi Province Nanchang330013 China

In order to optimise the effect of database information retrieval and overcome the problem of high index cost of traditional retrieval methods, an efficient retrieval design method of relational database based on knowledge graph is designed in this paper. Firstly, the distribution model of database information retrieval nodes is constructed to analyse the data structure, and the semantic characteristics of stored data of retrieval nodes are fused. Then the knowledge graph model analysis method is used to retrieve the database node attribute clustering and adaptive scheduling to realise the design of efficient information retrieval model for the database. The experimental results show that the index distance cost of this method is always below 5 × 1,010 byte, and the index distance cost is always below 40 s, which has certain application value. © 2023 Inderscience Enterprises Ltd.

关键词： Information retrieval

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From Torch to Projector: Fundamental Tradeoff of Integrated Sensing and Communications

IEEE BITS the Information Theory Magazine

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IEEE BITS the Information Theory Magazine 2024年第1期4卷 73-90页

作者： Xiong, Yifeng Liu, Fan Wan, Kai Yuan, Weijie Cui, Yuanhao Caire, Giuseppe Beijing University of Posts and Telecommunications School of Information and Electronic Engineering Beijing100876 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen518055 China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Technical University of Berlin Berlin10623 Germany

Sensing and communications (S&C) were historically developed in parallel. In the last decade, they have been evolving from separation to integration, giving rise to the integrated sensing and communication (ISAC) paradigm, that has been recognized as one of 6G key usage scenarios. Despite the plethora of research works dedicated to ISAC signal processing, the fundamental performance limits of S&C remain widely unexplored in ISAC systems. In this tutorial paper, we attempt to summarize the recent research findings in characterizing the performance boundary of ISAC systems and the resulting S&C tradeoff from an information-theoretical viewpoint. We begin with a folkloric "torch metaphor" that depicts the subspace tradeoff between S&C. Then, we elaborate on the fundamental capacity-distortion (C-D) theory, indicating the incompleteness of this metaphor. Toward that end, we further elaborate on the S&C tradeoff by discussing a special case within the C-D framework, namely the Cramér–Rao bound-rate region. In particular, S&C have preference discrepancies over both the signal subspace and the adopted codebook, leading to a "projector metaphor" complementary to the torch analogy. We also present two practical design examples by leveraging the lessons learned from fundamental theories. Finally, we conclude the article by identifying a number of open challenges. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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Decomposition-Based Memetic Algorithm for Multi-Objective Fleet Size and Mix Vehicle Routing Problem

Decomposition-Based Memetic Algorithm for Multi-Objective Fl...

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Congress on Evolutionary Computation

作者： Yunpeng Ba Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The heterogeneous fleet vehicle routing problem (HFVRP) is of great significance in logistics and transportation. This paper considers a crucial and challenging HFVRP variant, namely multi-objective fleet size and mix vehicle routing problem with unlimited fleet, fixed cost, and dependent cost (MO-FSMVRPFD). A decomposition-based memetic algorithm called MOEA/D-ALS is proposed to cope with MO-FSMVRPFD. MOEA/D-ALS uses the multi-objective evolutionary algorithm based on decomposition (MOEA/D) as the backbone. Beyond that, it adopts a replacement strategy based on maximal fitness improvement (MFI). In addition, the local search is conducted on difficult subproblems for further refinement. The experimental studies on 18 instances show that MOEA/D-ALS exhibits a significant performance superiority over two other representative algorithms. The effectiveness of both the MFI strategy and the adaptive local search procedure is also validated.

关键词： Memetics Measurement Costs Vehicle routing Transportation Evolutionary computation Logistics

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Towards Lightweight Underwater Depth Estimation

Towards Lightweight Underwater Depth Estimation

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Artificial Intelligence (CAI), IEEE Conference on

作者： Keyu Zhou Jin Chen Shuangchun Gui Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Underwater depth estimation is crucial in the applications of marine robotics. It can provide environment information for target tracking, robot navigation, and 3D reconstruction of underwater terrain. Existing works transform underwater images into in-air conditions to adapt methods that are designed for natural images. However, this may result in expensive computational resources. To overcome this limitation, we propose a lightweight knowledge distillation framework for underwater depth estimation. We utilize a powerful model designed for underwater images as the teacher model and a lightweight CNN model as the student model. We distill global features to enable the student to acquire both local and global information, thereby improving estimation performance. Our framework includes a global transformation module for efficient global feature distillation and a global-local fusion module to combine local and global information for final estimation. Experimental results on the FLSea dataset demonstrate that our student model is lighter than the teacher model while outperforming lightweight in-air models. Our network is 60% lighter than the teacher model and achieves a 3.1% improvement in the δ 1 metric compared to the lightweight in-air model.

关键词： Measurement Adaptation models Three-dimensional displays Target tracking Navigation Computational modeling Estimation

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INTRODUCING FEATURE MATCHING AND SMOOTHING LOSS: A NOVEL GAN FOR IMPROVED SAMPLE GENERATION 13

INTRODUCING FEATURE MATCHING AND SMOOTHING LOSS: A NOVEL GAN...

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13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2023

作者： Yang, Junfeng Liu, Fengchang Mi, Jinhua Yin, Shengjie Bai, Libing School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Center for System Reliability and Safety University of Electronic Science and Technology of China Chengdu China Department of SRM Design Shanghai Space Propulsion Technology Research Institute Shanghai China

ISBN: (纸本)9781839539848

Fault diagnosis of planetary gearboxes constitutes a vital element of rotating machinery. In recent years, fault diagnosis techniques for planetary gearboxes predicated on deep learning have undergone extensive research. However, in scenarios with limited samples, these deep learning methodologies can grapple with data scarcity issues, negatively impacting the model's generalization capacity and diagnostic performance. To mitigate this, Generative Adversarial Networks (GANs) have been integrated into planetary gearbox fault diagnosis to fabricate synthetic data, thereby augmenting the training dataset. Nevertheless, GANs face challenges in effectively learning local feature information from samples, leading to substantial disparities in local features between the generated data and the original data. Furthermore, the sampling of fault vibration signals from planetary gearboxes often exhibits a high frequency, with relatively smooth signal transitions, while the vibration signal samples generated by GANs often fluctuate excessively, demonstrating significant differences from the original vibration signals. To address this issue, this study introduces an enhanced GAN network that incorporates a feature attention matching mechanism in the computation of the loss function. This mechanism aims to amplify the feature similarity between the generated samples and local samples. Concurrently, a smoothness loss is introduced into the loss function to ensure the changes in the generated signal samples are more gradual. Experimental results indicate that this proposed method can generate superior quality samples, and the generated samples can achieve high accuracy on the WD-CNN diagnostic network. © The Institution of engineering & technology 2023.

关键词： Generative adversarial networks

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Student Satisfaction Index in Synchronous e-Learning a Case Study 14

Student Satisfaction Index in Synchronous e-Learning a Case ...

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14th International Conference on eLearning, eLearning 2023

作者： Kasliwal, Prabha Sunil Gunjan, Reena Shete, Virendra School of Engineering MIT Art Design and Technology University Pune India School of Electronics and Telecommunication Engineering MIT Academy of Engineering India Department of Computer Science Engineering School of Engineering MIT Art Design and Technology University Pune India Department of Electronics and Telecommunication School of Engineering MIT Art Design and Technology University Pune India

design-Based Learning (DBL) is an educational teaching pedagogy that combines inquiry-based learning with design thinking. It aims to engage students in real-time situations and hands-on learning experiences where they apply design principles to observe the activities and identify the tasks which can be done or solved by an alternate way. DBL encourages creativity, critical thinking, collaboration, and problem-solving skills. DBL experience fosters student engagement, motivation, and a sense of accomplishment, leading to greater satisfaction with the learning process. Positive emotions, such as excitement, curiosity, and enjoyment, are often associated with higher levels of satisfaction. When students feel motivated, interested, and connected to the course, they are more likely to be satisfied and enjoy their learning experience. Effective teaching practices, supportive e-learning environments, and addressing individual student queries can all contribute to enhancing student satisfaction and overall learning outcomes. In synchronous e-learning, design thinking approach is applied during the instructional design process. In this paper the systematic review and survey aimed to explore the relationship between emotions during design thinking course in synchronous e-learning and Student Satisfaction Index (SSI). The research methodology involved a mixed-methods approach, combining quantitative data through surveys and qualitative data through interviews. The activities were designed to enhance attentiveness in class, student autonomy, active involvement in every task, opportunities for collaboration, and a sense of ownership over the learning process and maximize the learning in synchronous e-learning mode. The case study findings are investigation of activities done in the "design Thinking" course at the First year engineering. The learner’s response to the online survey as individual and as offer valuable insights that activities conducted proved to be an effective pe

关键词： Students

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