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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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M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary Semantic Segmentation 2

M-segclip: Enhancing SegCLIP with RM-MLP for Open-Vocabulary...

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2nd International Conference on Algorithm, Image processing and Machine Vision, AIPMV 2024

作者： Zhang, Yuhang Che, Yanqiu Li, Shanshan Wang, Ruofan School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798350390254

With the emergence of large-scale visual language models represented by CLIP, we have broken the great divide between vision and language. It has become possible to project features from visual models to semantic space consisting of a large amount of text. At the same time, semantic segmentation has opened the door to transferring learned visual knowledge to open vocabulary semantic segmentation. In this paper we improve and optimize a model with open vocabulary segmentation capability based on the CLIP architecture, we design an RM-MLP module and insert this module after the semantic group module in the image encoder, which is used to optimize the semantic regions aggregated by the semantic group module, and then used to generate the final segmentation results. The final experimental results show that the optimized model achieves higher segmentation accuracy on PASCAL VOC 2012 (+1.14% mIoU), and COCO (+0.43% mIoU). © 2024 IEEE.

关键词： Semantic Segmentation

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An End-to-End Training Method to Ensemble Bi-Linear Models for Knowledge Graph Completion

An End-to-End Training Method to Ensemble Bi-Linear Models f...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Cen, Si Liu, Han Liu, Chao Zou, Xiaoying Dai, Guoquan Wang, Xizhao College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303780

The study of ensemble learning in knowledge graph embedding (KGE) shows that combining multiple individual KGE models can perform better on knowledge graph completion. However, existing KGE ensemble methods ignore the creation of model diversity because these methods independently train individual models, which are short of training interaction. To create rich model diversity, we propose a novel training method for ensemble bilinear models (EBM) for the problem of knowledge graph completion. EBM uses a weighted loss to allow individual KGE models to interact during training. In this way, the relations in the knowledge graph can be automatically modeled by the most appropriate model from the KGE individual ones. The experiments on knowledge graph completion show that EBM has richer diversity and performs better than the single bilinear model and the ensemble methods without interaction. © 2023 IEEE.

关键词： Knowledge graph

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Towards Multi-Label Open Set Diagnosis for Medical Images 22

Towards Multi-Label Open Set Diagnosis for Medical Images

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Pang, Shijie Zhao, Yuze Zhao, Rui-Wei Feng, Rui Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798331520526

In real clinical scenarios, diagnosis algorithms may face the complex concurrent presence of multiple common, rare, or even unknown disease conditions. In particular, rare disease categories may be underrepresented or even absent from the class-imbalanced training set, leaving safe and trustworthy diagnostic decision-making extremely challenging. In this work, we first formalize the medical image diagnosis as a multi-label open set recognition under class-imbalanced training data and propose a novel Multi-label Open set Diagnosis (MOSD) solution to address this problem. Our MOSD first employs a Balanced Multi-label Data Augmentation (BMDA) module to augment multi-label image data to alleviate the class imbalance in model training more effectively. Then it also boasts a Multi-label Evidential Deep Learning (MEDL) module with tailored learning objectives to better estimate the uncertainty for multi-label open set prediction. Experimental results on the ODIR-5K benchmark successfully demonstrate its superiority in both close set known class classification and open set unknown class detection. © 2025 IEEE.

关键词： Mammography

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Design and Simulation Analysis of Finger Rehabilitation Robot Training control System 2022

Design and Simulation Analysis of Finger Rehabilitation Robo...

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2nd International Conference on Robotics and control Engineering, RobCE 2022

作者： Han, Yu Zhang, Qiuyue Guo, Tinghang Qin, Zhuanping Qie, Tongtong Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education China

ISBN: (纸本)9781450395854

Abstract: For cerebral palsy, stroke or other causes of impaired hand movement, hand rehabilitation training is crucial in the early stages of illness or injury. In order to promote the function recovery of hand rehabilitation patients and improve their quality of life, Finger Rehabilitation Robot Training control System is designed. The system consists of mechanical frame, finger rehabilitation robot, motor, cable, sensor, control module and so on. Motors are used to drive finger joint linkage training. Angles of finger joint movement are collected, to monitor the joint movement state, return movement data, and make reasonable rehabilitation training program. The system is reasonable in structure, stable in operation, safe and reliable, and easy to operate. It accords with the biological characteristics of fingers. It can complete finger rehabilitation training with MCP(metacarpophalangeal), PIP (Proximal Interphalangeal), and DIP (Distal Interphalangeal) joints well. The joint angle parameters are reasonable and can meet the normal rehabilitation needs. © 2022 ACM.

关键词： control systems

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Design of control Circuit for Active Metasurface Based on Varactor Diode

Design of Control Circuit for Active Metasurface Based on Va...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Zhang, Yue Liu, Yuefei Yang, Guohui Zhang, Kuang Li, Yingsong School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9781733509657

A control circuit for active metasurface based on varactor diode is designed in this paper. By varying the reverse bias voltage on the varactor diode, the designed circuit can regulate the additional phase of the active meta-atoms. The circuit adopts a form of one master and multiple slaves, which can simultaneously control the bias voltage of 960 meta-atoms. Each slave can control up to 64 channels. The control accuracy is 0.04V. The power consumption of a single slave is 160mW. Though experiment verification, the circuit has a good effect in controlling the beam deflection of the metasurface and generating OAM beams. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Timing circuits

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Architecture Design of Equipment Warehouse Scheduling System Based on Software Definition 5th

Architecture Design of Equipment Warehouse Scheduling System...

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5th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Zhang, Xue Ting Pan, Yan Peng Yang, Li Jie Xue, Chen Chen Zhang, Fu Quan China Academy of Launch Vehicle Technology Beijing100076 China School of Automation Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9789819908479

The technology of software-defined changing all areas of life, but the equipment storage system in the field of application is still in its infancy. At present, the traditional equipment warehousing scheduling system is difficult to meet the new needs of rapid scheduling and flexible maintenance of large quantities of equipment. Based on the development and application characteristics of software definition technology, this paper integrates the traditional equipment storage scheduling system and presents the research on the architecture design technology of equipment storage scheduling system under software definition technology. This paper clarifies the key application technologies of the system, realizes the design of new open system architecture based on software-defined technology and the optimization design of system center service platform architecture, and improves the architecture design ability of automation, configuration ability, and easy maintenance of the existing equipment storage scheduling system. It solves the new needs of rapid deployment, intelligent upgrade, flexible configuration, and convenient service of large quantities of equipment warehouse scheduling system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Application programs

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Sliding Mode control of Overhead Crane Based on RBF Neural Network 5

Sliding Mode Control of Overhead Crane Based on RBF Neural N...

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5th IEEE International Conference on Power, intelligent Computing and Systems, ICPICS 2023

作者： Li, Xinqi Zhang, Yongli Shi, Lanting Pang, Dangfeng Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China School of Information Technology Beijing Institute of Technology Zhuhai Zhuhai China

ISBN: (纸本)9798350333442

Aiming at the problems of trolley positioning and load anti-swing of bridge crane, the controller was designed in the framework of sliding mode control, and RBF neural network was introduced to solve the uncertainty of the bridge crane model. The stability of the designed control system was determined by using LaSalle invariance theorem. The simulation experiments of the controller were realized with MATLAB software. The experimental results shown that the presented control method can realize the rapid positioning of trolley and bridge and the load anti-swing, and has a good control effect. © 2023 IEEE.

关键词： Sliding mode control

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An Expansion-based Document Ranking Framework Incorporated with Core Concern Capturing 5

An Expansion-based Document Ranking Framework Incorporated w...

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5th International Conference on intelligent Autonomous Systems, ICoIAS 2022

作者： Qi, Yinqiao Xin, Jinghao Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15. © 2022 IEEE.

关键词： Semantics

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