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SSRN 2025年

作者： Wei, Lixin Luo, Yun Zhu, Rongzhe Li, Xin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Province Qinhuangdao066004 China Ginlong Technologies Co. Ltd Zhejiang Province Ningbo City315700 China

To address the challenge of tracking difficulties due to the absence of temporal dynamic information and background clutter interference caused by similar backgrounds, similar objects, target occlusion, and illumination changes during target tracking, this paper proposes a single object tracking algorithm based on spatio-temporal information (SST). The algorithm integrates a Temporal Adaptive Module (TAM) into the backbone network to generate a temporal kernel based on feature maps. This endows the network with the capability to model temporal dynamics, effectively utilizing the temporal relationships between frames to handle complex temporal dynamics such as changes in target motion states and environmental conditions. Additionally, to mitigate background clutter interference, the algorithm employs a Mixed Local Channel Attention (MLCA) mechanism,, which captures channel and spatial information to focus the network on the target and reduce the impact of interfering information. The proposed algorithm was evaluated on OTB100, LaSOT, and NFS datasets. It achieved an AUC score of 70.7\% on OTB, which represents a 1.3\% improvement over the baseline tracker. On LaSOT and NFS datasets, it obtained AUC scores of 65.1\% and 65.9\%, respectively, showing improvements of 0.2\% compared to the baseline tracker. The tracking speed exceeds 80fps. © 2025, The Authors. All rights reserved.

关键词： Clutter (information theory)

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IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks

arXiv

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arXiv 2022年

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei230088 China The Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei230009 China The Intelligent Manufacturing Institute of HeFei University of Technology Hefei230009 China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors, and suffer from the trade-off between accuracy and computational complexity. In contrast, considering the limited learning ability and information loss caused by the limited representational capability of BNNs, we propose IR2Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information, including: 1) information restriction: for a BNN, by evaluating the learning ability on the input information, discarding some of the information it cannot focus on, and limiting the amount of input information to match its learning ability;2) information recovery: due to the information loss in forward propagation, the output feature information of the network is not enough to support accurate classification. By selecting some shallow feature maps with richer information, and fusing them with the final feature maps to recover the feature information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better trade-off between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with ∼10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Recovery

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Collaborative Fall Detection using a Wearable Device and a Companion Robot

Collaborative Fall Detection using a Wearable Device and a C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Fei Liang Ricardo Hernandez Jiaxing Lu Brandon Ong Matthew Jackson Moore Weihua Sheng Senlin Zhang School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA School of Mechanical and Aerospace Engineering Oklahoma State University Stillwater OK USA State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

Older adults who age in place face many health problems and need to be taken care of. Fall is a serious problem among elderly people. In this paper, we present the design and implementation of collaborative fall detection using a wearable device and a companion robot. First, we developed a wearable device by integrating a camera, an accelerometer and a microphone. Second, a companion robot communicates with the wearable device to conduct collaborative fall detection. The robot is also able to contact caregivers in case of emergency. The collaborative fall detection method consists of motion data based preliminary detection on the wearable device and video-based final detection on the companion robot. Both convolutional neural network (CNN) and long short-term memory (LSTM) are used for video-based fall detection. The experimental results show that the overall accuracy of video-based algorithm is 84%. We also investigated the relation between the accuracy and the number of image frames. Our method improves the accuracy of fall detection while maximizing the battery life of the wearable device. In addition, our method significantly increases the sensing range of the companion robot.

关键词： Wearable computers Conferences Senior citizens Robot vision systems Collaboration Sensors Convolutional neural networks

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An Adaptive Data Rate-Based Task Offloading Scheme in Vehicular Networks

An Adaptive Data Rate-Based Task Offloading Scheme in Vehicu...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chaofan Chen Wendi Nie Yaoxin Duan Victor C.S. Lee Kai Liu Huamin Li School of Automation Chongqing University Chongqing China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China Department of Automation Chongqing University of Posts and Telecommunications Chongqing China Institute of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong China College of Computer Science Chongqing University Chongqing China

As an important application of Internet of Things (IoT), Internet of Vehicles (IoVs) can provide various valuable services which may require computation-intensive tasks under strict time constraints. Most traditional vehicles may not be able to process all these computation-intensive tasks locally because of the limitation of computing resources. Therefore, task offloading has been proposed, which allows vehicles to offload computation-intensive tasks to Mobile Edge Computing (MEC) servers. With the arising and development of intelligent vehicles, the concept of Vehicle as a Resource (VaaR) has been proposed as an important supplement to MEC, which enables intelligent vehicles to share computation resources with nearby vehicles. Most studies in VaaR generally assume that the transmission data rate of offloading tasks from one vehicle to another is fixed. However, in VaaR, due to the high mobility of vehicles, the communication distance between vehicles may change over time, resulting in changing data rate. Therefore, it is challenging to make offloading decisions (i.e., selecting proper vehicles as computation resource providers) while considering adaptive data rate. In this paper, we study task offloading in vehicular networks while considering adaptive data rate. We propose an Adaptive Data Rate-based Offloading algorithm named ADRO, which can not only achieve minimum energy consumption while satisfying time constraints, but also take adaptive data rate into consideration. Comprehensive experiments have been conducted to demonstrate the efficiency of the ADRO algorithm.

关键词： Energy consumption Adaptive systems Multi-access edge computing Intelligent vehicles Sensors Time factors Servers

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Resilience centrality in complex networks

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Physical Review E 2020年第2期101卷 022304-022304页

作者： Yongtao Zhang Cunqi Shao Shibo He Jianxi Gao State Key Laboratory of Industrial Control Technology Department of Control Science and Engineering Zhejiang University Hangzhou 310027 China Department of Computer Science Rensselaer Polytechnic Institute Troy New York 12180 USA

Resilience describes a system's ability to adjust its activity to retain the basic functionality when errors or failures occur in components (nodes) of the network. Due to the complexity of a system's structure, different components in the system exhibit diversity in the ability to affect the resilience of the system, bringing us a great challenge to protect the system from collapse. A fundamental problem is therefore to propose a physically insightful centrality index, with which to quantify the resilience contribution of a node in any systems effectively. However, existing centrality indexes are not suitable for the problem because they only consider the network structure of the system and ignore the impact of underlying dynamic characteristics. To break the limits, we derive a new centrality index: resilience centrality from the 1D dynamic equation of systems, with which we can quantify the ability of nodes to affect the resilience of the system accurately. Resilience centrality unveils the long-sought relations between the ability of nodes in a system's resilience and network structure of the system: the capacity is mainly determined by the degree and weighted nearest-neighbor degree of the node, in which weighted nearest-neighbor degree plays a prominent role. Further, we demonstrate that weighted nearest-neighbor degree has a positive impact on resilience centrality, while the effect of the degree depends on a specific parameter, average weighted degree βeff, in the 1D dynamic equation. To test the performance of our approach, we construct four real networks from data, which corresponds to two complex systems with entirely different dynamic characteristics. The simulation results demonstrate the effectiveness of our resilience centrality, providing us theoretical insights into the protection of complex systems from collapse.

关键词： Network resilience Network structure High dimensional systems

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Outage Performance of Uplink Rate Splitting Multiple Access with Randomly Deployed Users

arXiv

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arXiv 2022年

作者： Lu, Huabing Xie, Xianzhong Shi, Zhaoyuan Lei, Hongjiang Zhao, Nan Cai, Jun The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing246011 China The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users' rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user's diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region. Copyright © 2022, The Authors. All rights reserved.

关键词： Wireless networks

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MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization

arXiv

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arXiv 2023年

作者： Wang, Ke Ye, Zanting Xie, Xiang Cui, Haidong Chen, Tao Liu, Banteng College of Information Science and Technology Zhejiang Shuren University Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Computer Science and Artificial Intelligence Changzhou University Changzhou China School of Engineering Newcastle university Newcastle upon Tyne United Kingdom Breast Surgery First Affiliated Hospital Zhejiang University Hangzhou China The Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China

Accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent deep learning advancements in medical image segmentation provide insufficient contribution to practical applications, due to the domain shift resulting from differences in patient postures, individual gland density, and imaging modalities of mammography etc. In this paper, a novel framework named MLN-net, which can accurately segment multi-source images using only single source images, is proposed for clustered microcalcification segmentation. We first propose a source domain image augmentation method to generate multi-source images, leading to improved generalization. And a structure of multiple layer normalization (LN) layers is used to construct the segmentation network, which can be found efficient for clustered microcalcification segmentation in different domains. Additionally, a branch selection strategy is designed for measuring the similarity of the source domain data and the target domain data. To validate the proposed MLN-net, extensive analyses including ablation experiments are performed, comparison of 12 baseline methods. MLN-net enhances segmentation quality of full-field digital mammography (FFDM) and digital breast tomosynthe (DBT) images from the FFDM-DBT dataset, which achieves the average Dice similarity coefficient (DSC) of 86.52% and the average Hausdorff distance (HD) of 20.49mm on the source domain DBT. And it outperforms the baseline models for the task in FFDM images from both the CBIS-DDSM and FFDM-DBT dataset, which achieves the average DSC of 50.78% and the average HD of 35.12mm on the source domain CBIS-DDSM. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and the its segmentation accuracy surpasses state-of-theart methods. Code will be available at https://***/yezanting/MLN-N

关键词： Mammography

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Coordinated operation of electricity and hydrogen blended gas networks via correlation-enhanced joint chance-constrained approach

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International Journal of Hydrogen Energy 2025年 149卷

作者： Sicheng Liu Bo Yang Lun Yang Xu Yang Xin Li Zhaojian Wang Kai Ma Xinping Guan State Key Laboratory of Submarine Geoscience School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory for System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an 710049 China Key Lab of Industrial Computer Control Engineering of Hebei Province and the School of Electrical Engineering Yanshan University Qinhuangdao 066000 China

Blending green hydrogen into natural gas can effectively promote hydrogen consumption and operational flexibility of electricity-gas coupled networks. However, the volatility of renewable energy sources threatens the operation of green hydrogen production and blending, while existing system-level risk management methods either exhibit over-conservatism or are computationally inefficient when constraints are numerous. Therefore, this paper proposes a correlation-enhanced distributionally robust joint chance-constrained (DRJCC) method for the joint scheduling of electricity and hydrogen-enriched compressed natural gas (E-HCNG) networks, aiming to minimize operating cost while limiting joint violation risk under renewable energy uncertainty. Specifically, before DRJCC, an improved hydrogen fraction model is developed for hydrogen blending, refining the representation of fraction variations by incorporating the buffer effect of line packs. Then, for DRJCC solving, the commonly used Bonferroni approximations are overly conservative in allocating joint violation risks to individual chance constraints, especially when constraints are numerous. To mitigate this, by leveraging strongly violation correlations of operational constraints under affine policies, a grouping-based risk allocation strategy is proposed to reduce the over-conservatism. Theoretical derivations confirm the guarantee of joint violation risk via the proposed method. Additionally, customized transformations are developed to address the non-convexities introduced by variable hydrogen fractions. Case studies demonstrate the effectiveness and superiority of the proposed method.

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A Real-Time Mp3 Copyright Protection System for Mobile Terminals:Practicalissues

A Real-Time Mp3 Copyright Protection System for Mobile Termi...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Wu, Zhangqian Su, Zhaopin Wu, Qinfang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei China

ISBN: (纸本)9781728152240

With the development of the Internet and mobile terminals, more and more pirate digital music resources are easily disseminated online. To prevent the illegal copies propagating among mobile devices and protect the right of content providers. In this paper, we designed a practical real-time mp3 Copyright Protection System(MCPS) for mobile terminals which is based on Client/Server model and contains three parts: security channel, server and client. In particular, we established a secure channel based on SSL(Secure Sockets Layer) protocol, defined an identity authentication procedure between the server and the client. In the server, we designed RC4 based encryption algorithm for frame header, and chaotic based watermarking algorithms for frame body, respectively. In the client, we designed a novel mp3 streamd ecoder, where, for each frame, frame header is decrypted, and the embedded information is extracted and compared with its IMEI to decide whether to decode or not. The practical experiments show that the system can effectively protect copyright for digital_music and is of great. © 2020 IEEE.

关键词： Cryptography

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Redundancy reduction penalty term of loss function in deep neural network

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Knowledge-Based Systems 2025年

作者： Xueheng Hu Shuhuan Wen H.K. Lam Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Qinhuangdao 066004 Hebei Province China The Department of Engineering King’s College London London WC2R 2LS United Kingdom

In this paper, we propose a novel regularization algorithm that is introduced as a penalty term to the loss function. Differing from conventional L1 and L2 regularization methods, our approach does not aim to diminish the weights of individual neurons or enforce sparsity by driving certain neurons to zero. Instead, it functions by increasing the differences between neurons and enhancing the diversity of neurons within each layer. Our method incorporates ensemble learning techniques by treating the layer weight matrix as a collective learning model, where each neuron serving as a weak learner within the layer. The proposed algorithm improves the performance of DCNN by simultaneously considering the distance between multiple filters in the same layer. This algorithm reduces the redundancy of the parameter layer filters in DCNN and enhances its robustness. The penalty term proposed by our algorithm dynamically adjusts its value in a cyclical manner, compelling the neural network to navigate away from its current gradient state. In the parameter space, different weights correspond to different locations. The proposed algorithm quantifies the distance between neurons and iteratively increases the distance between neurons during thereby encouraging greater diversity within the network. Experimental evaluations demonstrate the effectiveness of our algorithm in enhancing neural network performance without requiring adjustments to other hyper-parameters.

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