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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Zhang Lijun Guo Lixin Ni Xiuchao He Jianhua Wang Rong Zhang The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Department of Radiology The Affiliated Hospital of Medicine School of Ningbo University Ningbo China

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications.

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Mcotm: Mobility-Aware Computation Offloading and Task Migration for Edge Computing in Industrial Iot

SSRN

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SSRN 2023年

作者： Qin, Wei Chen, Haiming Wang, Lei Xia, Yinshui Nascita, Alfredo Pescapè, Antonio Zhejiang Provincial Key Laboratory of Mobile Network Application Technology Ningbo University Zhejiang315211 China The Department of Electrical Engineering and Information Technology University of Napoli Federico II Napoli80125 Italy CSEF University of Naples Federico II Italy

Mobility-aware devices are crucial components of Industrial Internet of Things (IIoT). However, they face limitations in terms of battery capacity and computation power, which restrict their ability to provide services requiring broad bandwidth and strong computation power for computation-intensive tasks. While offloading can strengthen device computation power, ineffective offloading decisions result from device mobility and limited adaptability to changes in environmental resources, or are not applicable to the current mobile edge computing (MEC) environment. In this paper, we address these challenges by proposing a mobility-aware computation offloading and task migration approach (MCOTM) based on trajectory and resource prediction to address this issue of mobility offloading, which minimizes task turnaround time and system energy consumption. Simultaneously, our approach enhances the decision agent continuously to decrease task migration rates. MCOTM uses Lagrange interpolation equations to determine the trajectory of mobile devices, and Long Short-Term Memory (LSTM) to track the time-varying resources characteristics in IIoT. These prediction results will be used to assist Deep Deterministic Policy Gradient (DDPG) for making online computation offloading, task migration and resource allocation decisions. Experimental results show that the proposed MCOTM effectively reduces task turnaround time and system energy consumption while maintaining a low task migration rate, even with an increasing number of tasks. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Battery Management System Design for Industrial Manufacture 25

Battery Management System Design for Industrial Manufacture

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Haodong Zhang, Songwei Qiu, Tie Liu, Hongrui He, Ling Wang, Zimu College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Advance Network Technology and Application Tianjin300350 China San Jose State University San Jose95192 United States State Grid Corporation of China Beijing100031 China State Grid Ecommerce Technology Co. Ltd Tianjin300309 China

ISBN: (纸本)9781665405270

The development of the energy storage industry and the higher electricity prices lead to the continuous increase of users' demand for battery energy storage in industrial manufacture. To monitor the status of the battery and control the running process of the battery, we need a battery management system (BMS) with good performance and complete functions. Previously proposed BMS generally lacks functionality and is not designed for energy storage systems in industrial manufacture. This paper aims to design and implement a BMS for energy storage. The system can collect various data such as battery voltage, temperature, current, smoke, and so on. The functions of calculating status data, detecting faults, passive battery balance, and display are also supported. Moreover, the system optimizes the functions of battery balance and display. Finally, the experimental results show that the BMS achieves a battery voltage measurement error within 2mV, supports a passive balance current of about 100mA, and makes the computing resource allocation more balanced. © 2022 IEEE.

关键词： Battery management systems

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Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV networking

arXiv

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arXiv 2023年

作者： Dong, Zesong Tong, Wei Zhang, Zhiwei Li, Jian Yang, Weidong Shen, Yulong The Hangzhou Institute of Technology Xidian University Hangzhou China The School of Computer Science and Technology Xidian University Xi’an China The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi’an China Shaanxi Key Laboratory of Network and System Security Xi’an China

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain-based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV networking

Blockchain-Based Identity Authentication Oriented to Multi-C...

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IEEE International Conference on Blockchain (Blockchain)

作者： Zesong Dong Wei Tong Zhiwei Zhang Jian Li Weidong Yang Yulong Shen Hangzhou Institute of Technology Xidian University Hangzhou China Shaanxi Key Laboratory of Network and System Security Xi’an China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi’an China School of Computer Science and Technology Xidian University Xi’an China

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption.

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Research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

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第十四届中国卫星导航年会

作者： Dengao Li Danyang Shi Jumin Zhao Fanming Wu Liangquan Yan Ran Feng Xinfang Zhang Jinhua Zhao College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

The zenith tropospheric delay（ZTD） obtained by global navigation satellite system（GNSS） atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3（GPT3） model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3TD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network（TCN） for improving the hourly global pressure and temperature 2（HGPT2） model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2TD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCNTD obtained from the proposed model is higher than GPT3TD and HGPT2TD when using the GNSSTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious.

关键词： GNSS ZTD GPT3 HGPT2 TCN

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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第十四届中国卫星导航年会

作者： Fanming Wu Dengao Li Jinhua Zhao Ran Feng Danyang Shi Xinfang Zhang Jumin Zhao College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

There is a growing interest to use Global Navigation Satellite System(GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is *** sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile,combining GNSS-PWV time domain features(PWV value, PWV increment,PWV decrement, rate of PWV increment, rate of PWV decrement) and colocated meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong’s HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models.

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Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid model

Prediction of Ionospheric TEC based on BLS-LSTM-GRU Hybrid m...

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第十四届中国卫星导航年会

作者： Dengao Li Xinfang Zhang Jumin Zhao Fanming Wu Ran Feng Jinhua Zhao Danyang Shi College of Data Science Taiyuan University of Technology Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Intelligent Perception Engineering Technology Center of Shanxi Shanxi Province Engineering Technology Research Center of Spatial Information Network College of Information and Computer Taiyuan University of Technology

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content(TEC)in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network(LSTM)and the fast training speed of gated recurrent unit network(GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour(HD)are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error(RMSE)of the prediction results of this BLSLSTM-GRU model is decreased by 23.07%,17.48% and 9.46%compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy.

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Adversarial defense based on distribution transfer

arXiv

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arXiv 2023年

作者： Chen, Jiahao Yan, Diqun Dong, Li Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Ningbo315211 China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310027 China Key Laboratory of Mobile Network Application Technology of Zhejiang Province Zhejiang Ningbo315211 China

The presence of adversarial examples poses a significant threat to deep learning models and their applications. Existing defense methods provide certain resilience against adversarial examples, but often suffer from decreased accuracy and generalization performance, making it challenging to achieve a trade-off between robustness and generalization. To address this, our paper interprets the adversarial example problem from the perspective of sample distribution and proposes a defense method based on distribution shift, leveraging the distribution transfer capability of a diffusion model for adversarial defense. The core idea is to exploit the discrepancy between normal and adversarial sample distributions to achieve adversarial defense using a pretrained diffusion model. Specifically, an adversarial sample undergoes a forward diffusion process, moving away from the source distribution, followed by a reverse process guided by the protected model (victim model) output to map it back to the normal distribution. Experimental evaluations on CIFAR10 and ImageNet30 datasets are conducted, comparing with adversarial training and input preprocessing methods. For infinite-norm attacks with 8/255 perturbation, accuracy rates of 78.1% and 83.5% are achieved, respectively. For 2-norm attacks with 128/255 perturbation, accuracy rates are 74.3% and 82.5%. Additional experiments considering perturbation amplitude, diffusion iterations, and adaptive attacks also validate the effectiveness of the proposed method. Results demonstrate that even when the attacker has knowledge of the defense, the proposed distribution-based method effectively withstands adversarial examples. It fills the gaps of traditional approaches, restoring high-quality original samples and showcasing superior performance in model robustness and generalization. Copyright © 2023, The Authors. All rights reserved.

关键词： Normal distribution

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Beating the break-even point with autonomous quantum error correction

arXiv

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arXiv 2025年

作者： Li, Yi Mei, Qingyuan Jie, Qing-Xuan Cai, Weizhou Li, Yue Liu, Zhiyuan Chen, Zi-Jie Xie, Zihan Cheng, Xu Zhao, Xingyu Luo, Zhenghao Zhang, Mengxiang Zou, Xu-Bo Zou, Chang-Ling Lin, Yiheng Du, Jiangfeng Laboratory of Spin Magnetic Resonance School of Physical Sciences University of Science and Technology of China Hefei230026 China Anhui Province Key Laboratory of Scientific Instrument Development and Application University of Science and Technology of China Hefei230026 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China Anhui Province Key Laboratory of Quantum Network University of Science and Technology of China Hefei230026 China National Advanced Talent Cultivation Center for Physics University of Science and Technology of China Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Anhui Province Engineering Research Center for Quantum Precision Measurement Hefei230088 China Institute of Quantum Sensing School of Physics Zhejiang University Hangzhou310027 China

Quantum error correction (QEC) [1, 2] is essential for practical quantum computing, as it protects fragile quantum information from errors by encoding it in high-dimensional Hilbert spaces. Conventional QEC protocols [3, 4] typically require repeated syndrome measurements, real-time feedback, and the use of multiple physical qubits for encoding. Such implementations pose significant technical complexities, particularly for trapped-ion systems, with high demands on precision and scalability. Here, we realize autonomous QEC [5–9] with a logical qubit encoded in multiple internal spin states of a single trapped ion [10–12], surpassing the break-even point for qubit lifetime. Our approach leverages engineered spin-motion couplings to transfer error-induced entropy into motional modes, which are subsequently dissipated through sympathetic cooling with an ancilla ion, fully eliminating the need for measurement and feedback. By repetitively applying this autonomous QEC protocol under injected low-frequency noise, we extend the logical qubit lifetime to approximately 11.6 ms, substantially outperforming lifetime for both the physical qubit (0.9 ms) and the uncorrected logical qubit (0.8 ms), thereby beating the break-even point with autonomous protection of quantum information without measurement [13–16] or post-selection [17, 18]. This work presents an efficient approach to fault-tolerant quantum computing that harnesses the intrinsic multi-level structure of trapped ions, providing a distinctive path toward scalable architectures and robust quantum memories with reduced overhead. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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