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Letter to the editor, "Agreement between hand therapists and hand surgeons in the management of adults with closed metacarpal fractures"

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Musculoskeletal science & practice 2024年 71卷 102688页

作者： Ming Li Qian Gao Tianfei Yu Department of Computer Science and Technology College of Computer and Control Engineering Qiqihar University Qiqihar 161006 Heilongjiang China. Department of Biotechnology College of Life Science and Agriculture Forestry Qiqihar University Qiqihar 161006 Heilongjiang China. Electronic address: yutianfei@***.

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Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

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Journal of New Media 2022年第2期4卷 59-71页

作者： Liguo Zhang Zilin Tian Yan Zhang Tong Shuai Shuo Liang Zhuofei Wu College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Xidian University Xi’an710000China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China The 54th Research Institute of CETC Shijiazhuang130100China University of Bologna Bologna00013Italy

In recent years,deep learning algorithms have been popular in recognizing targets in synthetic aperture radar(SAR)***,due to the problem of overfitting,the performance of these models tends to worsen when just a small number of training data are *** order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition,in this paper,we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model(RBnet)for SAR image target *** the RBnet,a Bayesian classifier is used to improve the effect of SAR image target recognition and improve the accuracy when the training data is *** experimental results on MSTAR dataset show that the RBnet can fully exploit effective information in limited samples and recognize the target of the SAR images more *** with other state-of-the-art methods,our method offers significant recognition accuracy improvements under limited training *** that theRBnet is moderately difficult to implement and has the value of popularization and application in engineering application scenarios in the field of small-sample remote sensing target recognition and recognition.

关键词： Bayesian classifier limited data synthetic aperture radar(SAR) target recognition

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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AN EXPLICIT TOTAL LAGRANGIAN FRAGILE POINTS METHOD FOR FINITE DEFORMATION OF HYPERELASTIC MATERIALS

arXiv

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arXiv 2022年

作者： Mountris, Konstantinos A. Li, Mingjing Schilling, Richard Dong, Leiting Atluri, Satya N. Casals, Alicia Wurdemann, Helge A. Mechanical Engineering University College London London United Kingdom School of Aeronautic Science & Engineering Beihang University Beijing China Barts Heart Centre St Bartholomew's Hospital London United Kingdom Mechanical Engineering Texas Tech University LubbockTX United States Automatic Control & Computer Engineering Universitat Politecnica de Catalunya Barcelona Spain

This research explored a novel explicit total Lagrangian Fragile Points Method (FPM) for finite deformation of hyperelastic materials. In contrast to mesh-based methods, where mesh distortion may pose numerical challenges, meshless methods are more suitable for large deformation modelling since they use enriched shape functions for the approximation of displacements. However, this comes at the expense of extra computational overhead and higher-order quadrature is required to obtain accurate results. In this work, the novel meshless method FPM was used to derive an explicit total Lagrangian algorithm for finite deformation. FPM uses simple one-point integration for exact integration of the Galerkin weak form since it employs simple discontinuous polynomials as trial and test functions, leading to accurate results even with single-point quadrature. The proposed method was evaluated by comparing it with FEM in several case studies considering both the extension and compression of a hyperelastic material. It was demonstrated that FPM maintained good accuracy even for large deformations where FEM failed to converge. Copyright © 2022, The Authors. All rights reserved.

关键词： Integration

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Adaptive Variational Mode Time-frequency Analysis of Ship Radiated Noise 7

Adaptive Variational Mode Time-frequency Analysis of Ship Ra...

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7th International Conference on Information science and control engineering, ICISCE 2020

作者： Zhang, Haoyi Junejo, Naveed Ur Rehman Sun, Weitao Chen, Hailan Yan, Jiaquan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou China Ilma University Department of Computer Science Karachi Pakistan Northwestern Polytechnical University School of Marine Science and Technology Xi'an China Jimei University School of Science Xiamen China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

ISBN: (纸本)9781728164069

In this paper, we have proposed a novel time-frequency analysis method implementing adaptive variational mode decomposition (AVMD) and Wigner-Ville Distribution (WVD) to visualize the local features of ship radiated noise. Unlike the traditional methods are invalid under low signal to noise ratio (SNR), the proposed method is suitable for constructing time-frequency (TF) image with the capability of mining the local feature information accurately. Firstly, we employ complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) on the raw signal to determine the numbers of variational modes which is equal to intrinsic mode functions (IMFs) numbers. Secondly, we perform VMD algorithm to extract the local features absolutely by decomposing the raw signal into IMFs, where we set the IMFs gained by CEEMDAN as the input parameter of VMD. Finally, we adopt Wigner-Ville distribution (WVD) to draw TF signatures of all the IMFs, then superpose them to reconstruct the objected signal that can solve the cross-item problem. We have conducted the comparative experiments by the real oceanic data. Under the different SNRs, we have verified the effectiveness and robustness of the proposed method, also obtained better performance in terms of resolution than compared algorithm. © 2020 IEEE.

关键词： Intrinsic mode functions

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Kernel-based Class-specific Broad Learning System for software defect prediction

Kernel-based Class-specific Broad Learning System for softwa...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Wuxing Chen Kaixiang Yang Yifan Shi Qiying Feng Chengxi Zhang Zhiwen Yu School of Computer Science and Technology Guangdong University of Technology Guangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China School of Engineering Institute Huaqiao University Quanzhou China Computer Science and Engineering South China University of Technology Guangzhou China Shenzhen Electronic & Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665402460

With the continuous expansion of the software industry, the problem of software defects is receiving more and more attention. There has been a series of machine learning methods applied to the field of software defect prediction (SDP) as a way to ensure the stability of software. However, SDP suffers from the imbalance problem. To solve this problem, we first propose a class-specific broad learning system (CSBLS), which assigns a specific penalty factor to each class in accordance with the data distribution. Then we design a class-specific kernel-based broad learning system (CSKBLS), which adopts kernel mapping instead of random projection. This additive kernel scheme takes into account both outliers and noise in the data set. Extensive experiments on the real-world NASA datasets show that CSKBLS outperforms the comparison methods on the tasks of software defect prediction.

关键词： Learning systems Industries Additives NASA Machine learning Software Kernel

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Secrecy Rate Maximization of RIS-assisted SWIPT Systems: A Two-Timescale Beamforming Design Approach

arXiv

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arXiv 2022年

作者： Zhao, Ming-Min Xu, Kaidi Cai, Yunlong Niu, Yong Hanzo, Lajos The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2BX United Kingdom The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Department of Electronics and Computer Science University of Southampton United Kingdom

Reconfigurable intelligent surfaces (RISs) achieve high passive beamforming gains for signal enhancement or interference nulling by dynamically adjusting their reflection coefficients. Their employment is particularly appealing for improving both the wireless security and the efficiency of radio frequency (RF)-based wireless power transfer. Motivated by this, we conceive and investigate a RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system designed for information and power transfer from a base station (BS) to an information user (IU) and to multiple energy users (EUs), respectively. Moreover, the EUs are also potential eavesdroppers that may overhear the communication between the BS and IU. We adopt two-timescale transmission for reducing the signal processing complexity as well as channel training overhead, and aim for maximizing the average worst-case secrecy rate achieved by the IU. This is achieved by jointly optimizing the short-term transmit beamforming vectors at the BS (including information and energy beams) as well as the long-term phase shifts at the RIS, under the energy harvesting constraints considered at the EUs and the power constraint at the BS. The stochastic optimization problem formulated is nonconvex with intricately coupled variables, and is non-smooth due to the existence of multiple EUs/eavesdroppers. No standard optimization approach is available for this challenging scenario. To tackle this challenge, we propose a smooth approximation aided stochastic successive convex approximation (SA-SSCA) algorithm. Furthermore, a low-complexity heuristic algorithm is proposed for reducing the computational complexity without unduly eroding the performance. Simulation results show the efficiency of the RIS in securing SWIPT systems. The significant performance gains achieved by our proposed algorithms over the relevant benchmark schemes are also demonstrated. Copyright © 2022, The Authors. All rights reserved.

关键词： Approximation algorithms

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A deep neural network near-universal dielectric meta-atom generator

A deep neural network near-universal dielectric meta-atom ge...

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Freeform Optics, Freeform 2021 - Part of OSA Optical Design and Fabrication 2021

作者： Fowler, Clayton An, Sensong Zheng, Bowen Li, Hang Tang, Hong Haerinia, Mohammad Dong, Yunxi Zhang, Yifei Shalaginov, Mikhail Y. Agarwal, Anuradha Murthy Rivero-Baleine, Clara Kang, Myungkoo Richardson, Kathleen A. Gu, Tian Hu, Juejun Zhang, Hualiang Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Materials Research Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Missiles and Fire Control Lockheed Martin Corporation OrlandoFL32824 United States The College of Optics and Photonics Department of Materials Science and Engineering University of Central Florida OrlandoFL32816 United States

Dielectric meta-atoms enable ultra-thin optical devices but have complex electromagnetic responses. Neural networks mitigate the complexity but have had limited predictive scope. We present a network for near-arbitrar... 详细信息

ISBN: (纸本)9781557528209

关键词： Complex networks

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Deep spatiooral attention model for grain storage temperature forecasting 26

Deep spatiooral attention model for grain storage temperatur...

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26th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2020

作者： Duan, Shanshan Yang, Weidong Wang, Xuyu Mao, Shiwen Zhang, Yuan Henan Key Laboratory of Grain Photoelectric Detection Control Key Laboratory of Grain Information Processing Control College of Information Science Engineering Henan University of Technology Zhengzhou Henan China California State University Department of Computer Science SacramentoCA95819-6021 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781728190747

Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatiooral attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods. © 2020 IEEE.

关键词： Piles

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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