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SSRN 2023年

作者： Zhou, Zitao Ye, Fang Li, Yingsong Geng, Xiaoyu Zhang, Sitong Key Laboratory of Advanced Marine Communication and Information Technology College of Information and Communication Engineering Harbin Engineering University Harbin China National Key Laboratory of Underwater Acoustic Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education College of Electronic and Information Engineering Anhui University Hefei China

With the rapid development of the Internet of Things (IoT), computation offloading collaboration has become an important solution to address the data processing challenges faced by resource-constrained devices. However, security and privacy issues have become a major concern in the computation offloading collaboration process. This paper proposes a blockchain-assisted computation offloading collaboration scheme, Blockchain-Assisted Computation Offloading Collaboration (BACOC). The scheme uses blockchain technology to assist computation offloading collaboration under IoT conditions to fortify security and resilience during the computation offloading process. At the same time, a hierarchical game model based on Stackelberg game theory and contract theory is adopted to determine the optimal service pricing strategy. Experimental and simulation results demonstrate the effectiveness and feasibility of the BACOC scheme. © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Deep Reinforcement Learning Visual Navigation Model Integrating Memory-prediction Mechanism

Deep Reinforcement Learning Visual Navigation Model Integrat...

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International Conference on Computer Supported Cooperative Work in design

作者： Qian Xiao Pengfei Yi Rui Liu Jing Dong Dongsheng Zhou Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China Dalian University and Dalian University of Technology Dalian China

ISBN: (纸本)9781728165981

Deep reinforcement learning (DRL) has been widely used in the field of visual navigation. However, due to the lack of adaptability of DRL to the new tasks, the generalization ability of current visual navigation model using DRL is not desired. In order to improve this deficiency, we introduce the memory-prediction mechanism. By enhancing the memory of the scene, and combining the past experience of navigation to predict the next state, a more reasonable action can be obtained. First, we pass the image features extracted during the navigation process to an LSTM, and use LSTM to memorize the scene information in the image features. Then, we combine all the information (including state, target, and action) of each time step in the navigation process, and pass the historical information of multiple time steps to another LSTM to predict the next state. The action performed by the robot is determined by the predicted state. We use the AI2-THOR framework to carry out experiments. The results show that the proposed method can improve the navigation performance of the DRL visual navigation model and improve its adaptability to new tasks.

关键词： Training Visualization Adaptation models Navigation Computational modeling Semantics Reinforcement learning

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Human-robot Interaction Method Combining Human Pose Estimation and Motion Intention Recognition

Human-robot Interaction Method Combining Human Pose Estimati...

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International Conference on Computer Supported Cooperative Work in design

作者： Yalin Cheng Pengfei Yi Rui Liu Jing Dong Dongsheng Zhou Qiang Zhang Ministry of Education Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian China Dalian University and Dalian University of Technology Dalian China

Although human pose estimation technology based on RGB images is becoming more and more mature, most of the current mainstream methods rely on depth camera to obtain human joints information. These interaction frameworks are affected by the infrared detection distance so that they cannot well adapt to the interaction scene of different distance. Therefore, the purpose of this paper is to build a modular interactive framework based on RGB images, which aims to alleviate the problem of high dependence on depth camera and low adaptability to distance in the current human-robot interaction (HRI) framework based on human body by using advanced human pose estimation technology. To enhance the adaptability of the HRI framework to different distances, we adopt optical cameras instead of depth cameras as acquisition equipment. Firstly, the human joints information is extracted by a human pose estimation network. Then, a joints sequence filter is designed in the intermediate stage to reduce the influence of unreasonable skeletons on the interaction results. Finally, a human intention recognition model is built to recognize the human intention from reasonable joints information, and drive the robot to respond according to the predicted intention. The experimental results show that our interactive framework is more robust in the distance than the framework based on depth camera and is able to achieve effective interaction under different distances, illuminations, costumes, customers, and scenes.

关键词： Optical filters Pose estimation Robot vision systems Human-robot interaction Lighting Predictive models Cameras

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Cross-Inhibitor: A time-sensitive molecular circuit based on DNA strand displacement

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Nucleic Acids Research 2020年第19期48卷 10691-10701页

作者： Liu, Chanjuan Liu, Yuan Zhu, Enqiang Zhang, Qiang Wei, Xiaopeng Wang, Bin School of Computer Science and Technology Dalian University of Technology Dalian 116024 China Institute of Computing Science and Technology Guangzhou University Guangzhou 510006 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian 116622 China

designing biochemical systems that can be effectively used in diverse fields, including diagnostics, molecular computing and nanomachines, has long been recognized as an important goal of molecular programming and DNA nanotechnology. A key issue in the development of such practical devices on the nanoscale lies in the development of biochemical components with information-processing capacity. In this article, we propose a molecular device that utilizes DNA strand displacement networks and allows interactive inhibition between two input signals;thus, it is termed a cross-inhibitor. More specifically, the device supplies each input signal with a processor such that the processing of one input signal will interdict the signal of the other. Biochemical experiments are conducted to analyze the interdiction performance with regard to effectiveness, stability and controllability. To illustrate its feasibility, a biochemical framework grounded in this mechanism is presented to determine the winner of a tic-tac-toe game. Our results highlight the potential for DNA strand displacement cascades to act as signal controllers and event triggers to endow molecular systems with the capability of controlling and detecting events and signals. © 2020 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.

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High Transmittance Band-Pass Plasmonic Filter Using Metal-Insulator-Metal Waveguide with Double Split Circular Ring Resonator

High Transmittance Band-Pass Plasmonic Filter Using Metal-In...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Hui Wang Yi-yang Huang San-wang Ma Chun Ni Zhi-xiang Huang Xian-liang Wu Anhui Province Key Laboratory of Target Recognition and Feature Extraction Lu'an China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (纸本)9781665482271

In this paper, a band-pass plasmonic filter based on Metal-Insulator-Metal (MIM) waveguides and its modified structures are designed and investigated theoretically and numerically. The filter consists of two circular ring resonator and its modified structures which has narrow gaps in the middle of resonant rings. To study their performance in different structures, propagation modes of surface plasmon polaritons (SPPs) are investigated. In outer gapped model, we found that the transmission in different resonant modes can be effectively tuned by varying the gap width, and the transmission peak wavelength is nonlinear with the gap width. The results are obtained by high-order symplectic FDTD (SFDTD) algorithm which is non-dissipative and has low dispersion error. The structure with double gaps in both external and internal rings have potential applications in plasmonic integrated circuits, because of its characteristic of a single narrow band-pass with high transmittance. The introduced structures may provide a novel method for the design of devices in highly integrated optical circuits.

关键词： Band-pass filters Optical filters Structural rings Integrated optics Resonator filters Plasmons Optical ring resonators

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The relationship between orthopedic clinical imaging and bone strength prediction

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Medicine in Novel Technology and Devices 2021年第1期9卷 9-17页

作者： He Gong Meng Zhang Shaowei Jia Linwei Lv Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina Department of Engineering Mechanics Nanling CampusJilin UniversityChangchun130025China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin300384China

Pathologic fracture is caused by bone diseases,which is common in middle-aged and elderly *** bone strength is the leading contributor to this type of ***,accurately quantifying bone strength could help clinicians assess individualized fracture risk and develop therapeutic *** clinical imaging including dual X-ray absorptiometry(DXA),computed tomography(CT),and magnetic resonance imaging(MRI)has been an important tool to obtain radiology images of bone,which could be further used for noninvasive assessment of bone strength and related fracture *** the development of biomechanics and computer technology,the combination of finite element analysis with orthopedic imaging is the most advanced method for noninvasive prediction of bone strength in *** this review,an overview is provided for the commonly noninvasive methods based on the radiology images(i.e.,DXA,CT,and MRI)to predict bone strength,which may improve the understanding of the relationship between orthopedic clinical imaging and biomechanics.

关键词： Orthopedic clinical imaging Biomechanics Bone strength Finite element analysis

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Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

arXiv

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arXiv 2025年

作者： Tang, Fenghe Yao, Qingsong Ma, Wenxin Wu, Chenxu Jiang, Zihang Zhou, S. Kevin Anhui Hefei230026 China Suzhou Institute for Advanced Research USTC Suzhou215123 China Stanford University Palo AltoCA94305 United States State Key Laboratory of Precision and Intelligent Chemistry USTC Anhui Hefei230026 China Institute of Computing Technology CAS Beijing100190 China

Medical image segmentation remains a formidable challenge due to the label scarcity. Pre-training Vision Transformer (ViT) through masked image modeling (MIM) on large-scale unlabeled medical datasets presents a promising solution, providing both computational efficiency and model generalization for various downstream tasks. However, current ViT-based MIM pre-training frameworks predominantly emphasize local aggregation representations in output layers and fail to exploit the rich representations across different ViT layers that better capture fine-grained semantic information needed for more precise medical downstream tasks. To fill the above gap, we hereby present Hierarchical Encoder-driven MAE (Hi-End-MAE), a simple yet effective ViT-based pre-training solution, which centers on two key innovations: (1) Encoder-driven reconstruction, which encourages the encoder to learn more informative features to guide the reconstruction of masked patches;and (2) Hierarchical dense decoding, which implements a hierarchical decoding structure to capture rich representations across different layers. We pre-train Hi-End-MAE on a large-scale dataset of 10K CT scans and evaluated its performance across seven public medical image segmentation benchmarks. Extensive experiments demonstrate that Hi-End-MAE achieves superior transfer learning capabilities across various downstream tasks, revealing the potential of ViT in medical imaging applications. The code is available at: https://***/FengheTan9/Hi-End-MAE. Copyright © 2025, The Authors. All rights reserved.

关键词： Computerized tomography

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UWB Non-Line-of-Sight Recognition and Suppression Methodology

UWB Non-Line-of-Sight Recognition and Suppression Methodolog...

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International Conference on Communication Software and Networks, ICCSN

作者： Zhengshuai Ni Haibo Zhou Guozheng Zhang Wangbei Xu National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University Tianjin China

Ultra-Wide Band (UWB) positioning system has become the main research object in indoor positioning field due to its superior positioning performance. Accuracy at the scale of centimeters is achievable in an ideal positioning scenario. Nonetheless, the intricate and changeable indoor surroundings may cause UWB signals to propagate beyond the line-of-sight (LOS) and weaken in signal strength, resulting in a considerable reduction in the precision of positioning. In this paper, methods for identifying non-line-of-sight (NLOS) errors, alleviating NLOS errors, and integrating multi-sensor information are analyzed and studied to provide reference for improving the NLOS positioning accuracy of UWB.

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Thermal Error Prediction Model of High-Speed Motorized Spindle Based on Delm Network Optimized by Weighted Mean of Vectors Algorithm

SSRN

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SSRN 2023年

作者： Dai, Ye Pang, Jian Rui, XuKun Li, WeiWei Wang, Qinghai Li, Shikun Key Laboratory of Advanced Manufacturing Intelligent Technology Ministry of Education Harbin University of Science and Technology Harbin150080 China Institute of Digital Design and Automatic Machinery Product Development Harbin University of Science and Technology Harbin150080 China

In the process of thermal error compensation of high-speed motorized spindle, it is necessary to establish an effective prediction model to predict the deformation of spindle through the model. In this study, firstly, the thermal characteristics of the motorized spindle are tested, and the data of the spindle at different speeds are obtained. After data preprocessing, considering the disadvantage that the number of clusters in traditional clustering methods needs to be specified manually, particle clustering and grey correlation analysis are selected to screen out the best three groups of temperature data to establish the model. In order to avoid the influence of data noise on the prediction results, the depth limit learning machine is used to establish the thermal error prediction model. Considering that the output of the Depth Limit Learning Machine (DELM) is easily influenced by the random input weights and biases, the vector weighted average algorithm (INFO) is chosen to optimize the two influencing factors. By establishing the INFO-DELM thermal error prediction model, and using the screened data to train the INDO-DELM model, the final prediction result is obtained. Finally, the model is compared with the DELM model, which verifies that the INFO-DELM model has better prediction performance. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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