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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： L. Liu H. Gong Z. Yu P. Fan Z. Lyu T. Xu Y. Han T. Ma Department of Engineering Physics Tsinghua University Beijing China Ministry of Education Key Laboratory of Particle & Radiation Imaging Beijing China Beijing Institute of Spacecraft System Engineering Beijing China State Nuclear Security Technology Center Beijing China

Passive imaging of radiative neutron sources plays a crucial role in various industrial applications. However, neutron imaging is challenging due to the strong penetration ability of neutrons and their complex interactions with materials. Current neutron imaging devices, such as coded aperture and scatter cameras, have limitations such as field of view (FOV) size and imaging *** this study, we propose a novel neutron camera design that aims to achieve high-performance neutron imaging. Our imaging detector is composed of interspaced plastic scintillators and boron-loaded polyethylene strips, which we term a self-collimating camera. The interactions of neutrons with the scintillators or polyethylene strips create directional-dependent neutron detection sensitivity for the other scintillators, allowing one scintillator or polyethylene strip to play the collimation role for other scintillators. This design enables omnidirectional neutron imaging. We conducted proof-of-concept experiments to validate the feasibility of our approach. Our experimental results demonstrate that our system achieves a positioning accuracy of approximately 5° using a Cf-252 source with an activity of 16.8μCi (neutron emission rate: 7.3×10 4 n/s) at a distance of 50 cm during a 900-second scan. Additionally, our system successfully resolved dual sources separated by an angle of 40°. We conclude that the proposed self-collimating neutron imager is portable, has excellent resolution and detection efficiency, and is capable of providing imaging in a 4π field of view (FOV).

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A High-Performance Infrared Imaging system with Adaptive Contrast Enhancement 11

A High-Performance Infrared Imaging System with Adaptive Con...

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Deng, Feng Su, Zhong Wang, Rui Liu, Jun Wang, Yanzhi Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science and Technology University Beijing China State Key Laboratory of Nickel and Cobalt Resources Comprehensive Utilization Jinchang China Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering College of Engineering Northeastern University BostonMA United States

ISBN: (纸本)9781643682402

Most of the existing infrared imaging systems employ the scheme of FPGA/FPGA+DSP with numerous peripheral circuits, which leads to complex hardware architecture, limited system versatility, and low computing performance. It has become an intriguing technical problem worldwide to simplify the system structure while improving the imaging performance. In this paper, we present a novel real-time infrared imaging system based on the Rockchip's RV1108 visual processing SoC (system on chip). Moreover, to address the problem of low contrast and dim details in infrared images with a high dynamic range, an adaptive contrast enhancement method based on bilateral filter is proposed and implemented on the system. First, the infrared image is divided into a base layer and a detail layer through bilateral filter, then the base layer is compressed by an adaptive bi-plateau histogram equalization algorithm, and finally a linear-weighted method is used to integrate the detail layer to obtain the image with enhanced details. The experimental results indicate that compared with traditional algorithms, our method can effectively improve the overall contrast of the image, while effectively retaining the image details without noise magnification. For an image of 320*240 pixels, the real-time processing rate of the system is 68 frames/s. The system has the characteristics of simplified structure, perceptive image details, and high computing performance. © 2022 The authors and IOS Press.

关键词： system-on-chip

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Loaded tooth contact analysis and meshing stiffness calculation for cracked spiral bevel gears

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Chinese Journal of Aeronautics 2025年第3期 597-627页

作者： Zhen LIU Fucai LI Wenjie BAO Xiaolei XU Freeda A.AMIR State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University School of Mechatronic Engineering China University of Mining and Technology Jiangsu JITRI Composite Equipment Research Institute Co. Ltd.

Tooth cracks may occur in spiral bevel gear transmission system of the aerospace equipment. In this study, an accurate and efficient loaded tooth contact analysis(LTCA) model is developed to predict the contact behavior and time-varying meshing stiffness(TVMS) of spiral bevel gear pair with cracked tooth. The tooth is sliced, and the contact points on slices are computed using roll angle surfaces. Considering the geometric complexity of crack surface, a set of procedures is formulated to generate spatial crack and determine crack parameters for contact points. According to the positional relationship between contact point and crack path, each sliced tooth is modeled as a nonuniform cantilever beam with varying reduced effective load-bearing tooth thickness. Then the compliance model of the cracked tooth is established to perform contact analysis, along with TVMS calculations utilizing three different models. By employing spiral bevel gear pairs with distinct types of cracks as examples, the accuracy and efficiency of the developed approach are validated via comparative analyses with finite element analysis(FEA) outcomes. Furthermore, the investigation on effects of cracks shows that tooth cracks can induce alterations in meshing performance of both entire gear pair and individual tooth pairs, including not only cracked tooth pair but also adjacent non-cracked tooth pairs. Hence, the proposed model can serve as a useful tool for analyzing the variations in contact behavior and meshing stiffness of spiral bevel gears due to different cracks.

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Impedance control for a novel composite modular lower-limb hemiplegic exoskeleton

Impedance control for a novel composite modular lower-limb h...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Bao, Ruoyu Li, Pengbo He, Bailin Wang, Can Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering Chinese University of Hong Kong College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shaanxi Xi'an710021 China

ISBN: (纸本)9781665436786

This paper proposes a control scheme based on impedance control for the smoothing control of the operating movements of a lower-limb hemiplegic exoskeleton robot on the basis of a new composite modular lower-limb hemiplegic ex-oskeleton(CMLHE) structure. The CMLHE has good structural performance and can be used in the rehabilitation of patients with hemiplegia caused by stroke and other diseases. At the same time, the smoothing control by means of impedance control ensures that patients with lower limb hemiplegia can use the CMLHE to achieve smooth walking movements without additional injuries caused by the CMLHE during the rehabilitation training. This paper analyses the performance of this CMLHE robot based on walking movements and demonstrates through kinematic and kinetic modelling and simulation that the impedance control scheme of this CMLHE robot is feasible and effective and can be more widely used in the rehabilitation training of patients with lower limb hemiplegia. © 2021 IEEE.

关键词： Exoskeleton (robotics)

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An SGX and Blockchain-Based system for Federated Learning in Classifying COVID-19 Images

An SGX and Blockchain-Based System for Federated Learning in...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Sihang Qin Xianjun Gu Xing Jun Haitao Li Mengqi Huang Weiqi Dai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China State Grid Wuhan Power Supply Company China

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes.

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Time-frequency decomposition-based weighted ensemble learning for motor imagery EEG classification

Time-frequency decomposition-based weighted ensemble learnin...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Zheng, Liangsheng Ma, Yue Li, Mengyao Xiao, Yang Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781665436786

Motor imagery brain-computer interface system based on Electroencephalogram (EEG) is an effective way to help the disabled recover part of their motor abilities. However, decoding the movement intention contained in the EEG signal accurately presents many challenges. In this paper, we propose a time-frequency decomposition-based weighted ensemble learning (TFDWEL) method, which aims to improve the classification performance of motor imagery EEG signals. The TFDWEL method divides the EEG signal into multiple subsets, and uses four time-frequency processing methods to extract the time-frequency sub-bands of each subset. Then the feature extraction model and classifier model of each subset trained by the common spatial pattern (CSP) algorithm and the support vector machine (SVM) algorithm are used to build a set of base learners. The least square error estimation method is used to learn the weight of each base learner, and finally the weighted summation method is used to obtain the final decision. The classification performance of the TFDWEL method is evaluated on the BCI Competition IV Data Set 2b, and the results show that the classification accuracy of 81.58% can be obtained. Superior classification performance indicates that the TFDWEL method can be used in further research to help the rehabilitation of the disabled. © 2021 IEEE.

关键词： Electroencephalography

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Design of optimized architected structures with exact size and connectivity via an enhanced multidomain topology optimization strategy

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Computational Mechanics 2021年第3期67卷 743-762页

作者： Liu, Chang Du, Zongliang Zhang, Weisheng Zhang, Xiaoyu Mei, Yue Guo, Xu State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics International Research Center for Computational Mechanics Dalian University of Technology Dalian 116023 China Ningbo Institute of Dalian University of Technology Ningbo 315016 China Beijing Institute of Spacecraft System Engineering CAST Beijing China

With the rapid developments of modern fabrication techniques, architected structures are increasingly used in many application areas, e.g., lightweight structures, heat exchangers, energy absorption components, aircraft engines, etc. To systematically design optimized architected structures with favorable manufacturability in terms of exact sizes and good connectivity, in the present work, an enhanced multidomain topology optimization method is developed. The design domain is divided into several subdomains and boundary layers between them first. Periodic base cells with exact sizes are distributed in each subdomain, and analyzed and optimized approximately based on the homogenization method with coarse meshes to reduce the numerical efforts. Besides, gradient boundary layers discretized by fine meshes are optimized between subdomains and boundaries to connect adjacent base cells with different patterns and address the boundary effect. Quadtree technology is used to match the meshes of different sizes. Numerical examples verify both the effectiveness and efficiency of the proposed approach for designing manufacturable optimized architected structures. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Computational efficiency Homogenization method Manufacturable optimized architected structures Multidomain topology optimization

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Parallel cognition:hybrid intelligence for human-machine interaction and management

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Frontiers of Information technology & Electronic Engineering 2022年第12期23卷 1765-1779页

作者： Peijun YE Xiao WANG Wenbo ZHENG Qinglai WEI Fei-Yue WANG State Key Laboratory for Management and Control of Complea Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China QingdaoAcademy of Intelligent Industries Qingdao 266109China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Macao Institute of System Engineering Macao University of Science and TechnologyMacao 999078China

As an interdisciplinary research approach,traditional cognitive science adopts mainly the experiment,induction,modeling,and validation *** models are sometimes not applicable in cyber-physical-socialsystems (CPSSs),where the large number of human users involves severe heterogeneity and *** reduce the decision-making conflicts between people and machines in human-centered systems,we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages:descriptive cognition based on artificial cognitive systems (ACSs),predictive cognition with computational deliberation experiments,and prescriptive cognition via parallel behavioral *** make iteration of these stages constantly on-line,a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive *** experiments on two representative scenarios,urban travel behavioral prescription and cognitive visual reasoning,indicate that our parallel cognition learning is effective and feasible for human behavioral prescription,and can thus facilitate human-machine cooperation in both complex engineering and social systems.

关键词： Cognitive learning Artificial intelligence Behavioral prescription

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Research on Highly Anti-Humidity and Selective NO2 Detection Based on GaN/MoO3 N-N Heterojunction

SSRN

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SSRN 2024年

作者： Hong, Yutao Han, Dan Li, Donghui He, Xiuli Zhao, Li Wang, Weidong Li, Hongwei Sang, Shengbo Liang, Hua Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Bioengineering Research Center Medical Innovation Research Division Chinese PLA General Hospital Beijing China Shanxi Academy of Medical Sciences China

Effective and accuratedetection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by the in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that at the operating temperature of 225 °C, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a low theoretical limit of detection (0.78 ppb) for NO2, which is significantly improved compared with the limit of detection of MoO3 (5 ppm). Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor also possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70 % RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3, and the heterojunction effect between them. This work provides a way to improve the efficiency and anti-humidity ability of real-time monitoring of NO2 for the sensors of MoO3-based composite. © 2024, The Authors. All rights reserved.

关键词： Gallium nitride

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Interannual variability of short rains in Tanzania and the influences from ENSO and the Indian Ocean Dipole

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Atmospheric and Oceanic Science Letters 2025年

作者： Rwambo, Issa Fan, Yi Yu, Peilong Chu, Changyu Nyasulu, Matthews State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/School of Atmospheric Science Nanjing University of Information Science and Technology Nanjing China Tanzania Meteorological Authority Dar es Salaam Tanzania College of Meteorology and Oceanography National University of Defense Technology Changsha China Key Laboratory of High Impact Weather(special) China Meteorological Administration Changsha China School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing China

Tanzania is mainly subject to a bimodal rainfall pattern, characterized by two distinct seasons: the long rains, occurring from March to May, and the short rains, which typically take place from October to December (OND). Short rains are usually less intense but still significantly influence local agriculture. Therefore, with station-based observations and reanalysis data, the current paper examines the interannual variability of OND precipitation in Tanzania from 1993 to 2022 and explores the possible impacts from El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) as well as the mechanisms. It is found that the Tanzania OND precipitation is above (below) normal in 1997, 2006, 2011, and 2019 (1993, 1998, 2005, and 2016). The composite difference between wet (dry) years and the climatology indicates that the anomalous lower-level convergence (divergence) and upward (downward) motion are the critical circulation characters for above (below) precipitation. Further analysis indicates ENSO and the IOD are the two main oceanic systems modulating OND precipitation in Tanzania. El Niño and a positive IOD could induce easterly anomalies and weaken the Walker circulation over the Indian Ocean, consequently leading to lower-level convergence in water vapor flux, upward anomalies, and more than normal precipitation in Tanzania. In contrast, La Niña and a negative IOD produce opposite circulation anomalies and less than normal precipitation over Tanzania. Moreover, through partial correlation and Generalized Equilibrium Feedback Analysis, the individual contributions of ENSO and the IOD to circulation are investigated. It is found that although both the IOD and ENSO impact the Walker circulation, the feedback to the IOD is stronger than ENSO. These results provide critical insights into the oceanic drivers and their mechanistic pathways underlying precipitation anomalies in Tanzania. 摘要坦桑尼亚降水模态呈现双峰型, 其显著特征为两个不同的雨季: 长雨季 (3月至5月) 和短雨季 (10月至12月 (OND)). 短雨季降水强度通常较

关键词： ENSO Generalized equilibrium feedback assessment Indian ocean dipole Precipitation Tanzania

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