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arXiv 2023年

作者： Tan, Shuhang Wang, Zhiling Zhong, Yan Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Laboratory for Intelligent Driving Technology Hefei230031 China Application and Innovation Research Institute of Robotics and Intelligent Manufacturing C.A.S. Hefei230031 China University of Science and Technology of China Hefei230026 China

Recent years have witnessed the proliferation of traffic accidents, which led wide researches on Automated Vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time-based frameworks can not handle complex traffic scenarios and ignore the motion tendency influence of each moving objects on the risk distribution, leading to performance degradation. To address this problem, we novelly propose a comprehensive driving risk management framework named RCP-RF based on potential field theory under Connected and Automated Vehicles (CAV) environment, where the pedestrian risk metric is combined into a unified road-vehicle driving risk management framework. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework, which can improve the performance of the driving risk model. Moreover, it requires only O(N2) of time complexity in the proposed method. Empirical studies validate the superiority of our proposed framework against state-of-the-art methods on real-world dataset NGSIM and real AV platform. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Electric Field Distribution Characteristics of Novel Fiber Sensor with Semiconductor Light Source 2

Electric Field Distribution Characteristics of Novel Fiber S...

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2nd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2022

作者： Jie, Huang Ni, Pengcheng Shuo, Chen Zhao, Xuyang Global Energy Internet Research Institute Changping Beijing China Elec. Pwr. Intelligent Sensing Technology and Application of State Grid Corporation Joint Laboratory Beijing China Anhui Jiyuan Software Co. Ltd Sgitg Hefei China North China Electric Power University School of Electrical and Electronic Engineering Changping Beijing China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

The light source element is an important part of the optical fiber sensor, and the selection of a suitable and reliable light-emitting element is critical factor for accurately measuring the parameters. The structural optimization of the optical fiber sensor semiconductor light source device is based on obtaining a more accurate field distribution. This paper analyzes the electric field distribution characteristics of a novel optical fiber sensor with semiconductor light source device by finite element method (FEM). The rationality of the electromagnetic shielding design of the model is verified by analyzing electric field distribution of the semiconductor light source device under different electromagnetic radiation conditions. © 2022 IEEE.

关键词： Finite element method

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Closed-Loop Simulation Test Method for Autonomous Vehicles Based on Adversarial Scenarios

Closed-Loop Simulation Test Method for Autonomous Vehicles B...

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IEEE International Conference on Mechatronics and Automation

作者： Xiaokun Zheng Huawei Liang Zhiyuan Li Chen Hua Qiong Wu Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China/P.R. China University of Science and Technology of China Hefei China/P.R. China Anhui Engineering Laboratory for Intelligent Driving Technology and Appllication Hefei Anhui China Anhui Jianghuai Automobile Group Co. Ltd Hefei Anhui China

Using simulation technology to verify the safety of autonomous vehicles (AVs) is a critical aspect of the development of autonomous driving technology. The primary objective is to reduce the time and cost of AV testing while enhancing testing efficiency. In this study, we propose a closed-loop simulation test method for AVs based on an adversarial scenario generation process. A comprehensive closed-loop simulation system was established, incorporating Simulink, Prescan, Carsim, and hardware-in-the-loop *** swiftly identify challenging test scenarios, we formulated an optimization model for safety evaluation, which is based on an improved driving safety field model. We employed the genetic algorithm to effectively solve this optimization problem, allowing for the rapid generation of difficult *** results demonstrate that our proposed method can effectively generate challenging scenarios, enabling the swift confirmation of the safety performance boundaries of the system under test. This approach significantly improves the overall testing efficiency and provides valuable insights for the advancement of autonomous driving technologies.

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MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for Medical Image Segmentation

MSCA-UNet: Multi-Scale Channel Adaptive Sensing Module for M...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Liangjin Diao Longfeng Shen Jiacong Chen Fangzhen Ge Mengwen Li Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Huaibei Anhui China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Recent advancements in CNNs for medical image segmentation have focused on the combination of CNN and Transformer architectures to capture both local and global features. However, challenges remain, such as effectively utilizing fused features at the encoder-decoder bottleneck and addressing blurred object edges in datasets. To tackle these issues, we introduce a multi-scale channel adaptive sensing module, enhancing the model's representational and perceptual capabilities by integrating multi-scale and channel-specific information. Additionally, we propose a cross-perception module and multi-scale segmentation head to improve global coordination and multi-scale information gathering. Experiments on the ACDC and Synapse datasets demonstrate our model's superiority.

关键词： Image segmentation Adaptation models Head Image edge detection Computer architecture Transformers Market research Sensors Synapses Biomedical imaging

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NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

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National Science Review 2024年第5期11卷 202-216页

作者： Weijie Zheng Huawei Mu Zhiyi Chen Jiajun Liu Debin Xia Yuxiao Cheng Qi Jing Pak-Ming Lau Jin Tang Guo-Qiang Bi Feng Wu Hao Wang AHU-IAI AI Joint Laboratory Anhui University Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science Center National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of China Division of Life Sciences and Medicine University of Science and Technology of China Interdisciplinary Center for Brain Information Brain Cognition and Brain Disease InstituteShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep learning–based high-efficiency,high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

关键词： whole-brain imagingneuronal activity trace mapping deep learning quantitative analysis

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A Low-Delay Circuit Structure Construction Method for AES Key Expansion Units

A Low-Delay Circuit Structure Construction Method for AES Ke...

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IEEE Conference on Industrial Electronics and applications (ICIEA)

作者： Xiaoqiang Zhang Zhiwei Peng Han Yan Xinxing Zheng Mingyu Xu Anhui Engineering Research Center of Vehicle Display Integrated Systems Anhui Polytechnic University Wuhu China Joint Discipline Key Laboratory of Touch Display Materials and Devices in Anhui Province Wuhu China Wuhu Engineering Research Center of Vehicle Intelligent Product Development and Application Wuhu Institute of Technology Wuhu China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Advanced Encryption Standard (AES) key expansion unit usually implemented by chain structure, which has a long critical path length. That makes key expansion unit become the bottleneck of high-speed AES implementations. In this paper, we propose a construction method to construct low-delay circuit structures for AES key expansion units. The proposed construction method first derives the expressions of AES key expansion. Then, the common subexpressions in the expressions are extracted by a delay-aware common subexpressions elimination algorithm. After the extraction, low-delay AES key expansion unit based on delay-drive binary tree (DDBT) structure are constructed according to the expressions. Based on the proposed construction method, AES encryption key expansion unit is designed in this paper. Our low-delay designs are further verified by the Synopsys™ integrated circuit (IC) synthesis tool Design Compiler (DC) with SMIC $\mathbf{0.18}\boldsymbol{\mu}\boldsymbol{m}$ technology. The synthesized results indicate the delay of our proposed low-delay structure is reduce by 15.23% at cost of 10.08% area increasing, compared with traditional chain structure. Compared with previous works, our designs can achieve the largest throughput.

关键词： Integrated circuits Industrial electronics Costs Binary trees Throughput Encryption Delays

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Transform-Invariant Convolutional Neural Networks for Image Classification and Search

arXiv

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arXiv 2019年

作者： Shen, Xu Tian, Xinmei He, Anfeng Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and nonlinear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https: ACM ISBN 978-1-4503-2138-9. Copyright © 2019, The Authors. All rights reserved.

关键词： Image recognition

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Efficient Object Detection Method Based on Improved YOLOv3 Network for Remote Sensing Images

Efficient Object Detection Method Based on Improved YOLOv3 N...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Jintao Wang Wen Xiao Tianwei Ni Key Laboratory of Unmanned Aerial Vehicle Development & Data Application of Anhui Higher Education Institutes Maanshan China Wanjiang University of Technology Maanshan Wireless Sensor Network and Intelligent Perception Engineering Technology Research Center Maanshan China Wanjiang University of Technology Maanshan China

ISBN: (数字)9781728197418

ISBN: (纸本)9781728197425

In the future, Drones or general Unmanned Aerial Vehicles(UAVs) need to implement online real-time object detection, but their memory and computing power are limited. An improved YOLOv3 is adopted in this paper, making full use of the lightweight network instead of the feature extraction network of YOLOv3 to achieve high-efficiency object detection for remote sensing images. In the case of similar detection accuracy, parameters and FLOPs (floating point operations) of the improved model are 2.5 times and 3.3 times smaller than YOLOv3, respectively. In addition, an IoU K-medians algorithm is proposed, which improves mAP by 7.0% on YOLOv3 and by 2.3% on the improved *** show that the detection speed of the improved YOLOv3 is 101 frame/s at the fastest, and it is still 1.6 times faster than that of YOLOv3 when its mAP is 6% higher than that of YOLOv3. The efficient remote sensing object detection method proposed in this paper lays the foundation for future UAVs.

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Research on Hot Isostatic Pressing Process Of Cfetr Wccb Components

SSRN

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SSRN 2022年

作者： Shen, Xu Liu, Jia-Qin Wu, Yu-Cheng Wu, Jie-Feng Liu, Song-Lin Zhu, Xiao-Yong Jiang, Bei-Yan School of Materials Science and Engineering Hefei University of Technology Hefei230009 China Institute of Plasma Physics Chinese Academy of Sciences Hefei230031 China Engineering Research Center of Advanced Composite Materials Design & Application of Anhui Province Hefei University of Technology Hefei230051 China Anhui Province Key Laboratory of Special Welding Technology Huainan232000 China Institute of Intelligent Manufacture Hefei University of Technology Hefei230051 China

Chinese Fusion engineering Testing Reactor (CFETR) is a superconducting magnet tokamak reactor of equivalent size and complementary functionality to ITER. The water-cooled ceramic breeder (WCCB) cladding designed by the Institute of Plasma Physics, Chinese Academy of Sciences is one of the candidate cladding concepts for CFETR. As an important part, the WCCB directly determines the safe operation of the fusion reactor. In the WCCB manufacturing technology, the hot isostatic pressing process is applied to the manufacture of WCCB due to its unique technical characteristics. This paper introduces the process research and inspection during the development of WCCB components, and these results will be the guidance to the manufacture of WCCB Components. © 2022, The Authors. All rights reserved.

关键词： Superconducting magnets

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Robust fault tolerant manoeuvring and rudder control of marine vehicles with state saturation

Robust fault tolerant manoeuvring and rudder control of mari...

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IEEE International Conference on Industrial technology (ICIT)

作者： Xiaozheng Jin Qiuhong Liu Yu Kang Jiahu Qin School of Electrical Engineering and Automation HeFei University of Technology HeFei AnHui China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467380768

In this paper, the problem of rudder-roll damping control for a class of cruise keeping ships subject to both actuator fault and state saturation is investigated. A manoeuvring and rudder control model of marine vessels is firstly reviewed based on the existing literature. Actuator faults which including partial loss of rudder effectiveness and actuator-bias faults are considered in the model. Then a switching control strategy is developed to compensate for the actuator faults and to guarantee the stability of the rudder-roll damping control system. Moreover, some relationship regarding state saturation bounds, actuator fault limits and control parameters are analyzed in this paper. Finally, simulation results are given to illustrate the proposed procedures and their effectiveness.

关键词： Actuators Vehicles Damping Mathematical model Marine vehicles Vehicle dynamics

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