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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Chenxi Shao Zhenzhong Shao Juntao Zhang Fanzhou Bai College of Computer Science & Technology University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui China Jianghuai Automobile Co. Ltd. Hefei China College of Computer Science & Technology University of Science and Technology of China Hefei

Based on the QSIM reasoning of Kuipers, comparative constraint is presented to reduce the diagnosis space. The transfer regulation of qualitative constrain is used in the simulation and reasoning of system diagnosis. This arithmetic takes observed faulty state as the beginning, to diagnosis the discrepancy of variable and location of the causation according to the transfer regulation of qualitative constraint, reasons from faulty diagnosed to examine the result and delete the redundancy of diagnosis result. In the example of Condensation refrigeration system, the constraint relation is built according to qualitative difference equation. Aim at the bad efficiency of refrigeration, the mixture of air or Freon superfluously is diagnosed as the result faulty source, is consistent with the factual system.

关键词： Circuit faults Mathematical model Differential equations Algebra Valves Cognition Educational institutions

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Design of MEMS Step Impedance Microstrip Filter in Ku Band

Design of MEMS Step Impedance Microstrip Filter in Ku Band

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Tianyu Bai Guohui Yang Kuang Zhang Yingsong Li Tao Jiang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin China

According to the miniaturization characteristics of step impedance resonators, a step impedance bandpass filter with a center frequency of 16.55 GHz and a bandwidth of 500MHz is designed. The effects of parameter changes such as substrate resistivity and the shape of metal via etching on the performance of the filter are studied. Finally, the filter is fabricated and its performance is tested under MEMS technology.

关键词： Band-pass filters Micromechanical devices Microstrip filters Shape Resonator filters Metals Conductivity

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A Parallel NURBS-based Isogeometric Analysis Framework on Multi-core CPUs

A Parallel NURBS-based Isogeometric Analysis Framework on Mu...

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2012年全国理论计算机科学学术年会(NCTCS2012)

作者： Licai Guo Zhangjin Huang School of Computer Science and Technology University of Science and Technology of China PR China USTC-SICT Joint Laboratory on Network and Key Laboratory of Software in Computing and Communication Anhui Province PR China USTC-SICT Joint Laboratory on Network and Communication PR China School of Computer Science and Technology University of Science and Technology of China PR China

parallel framework for NURBS-based isogeometric analysis on multi-core cpus is presented in this *** framework is composed of two parts:(1) construction of the stiffness matrix and right hand side vector; (2) a parall...

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Smoothness in noise reduction of chaotic time series

Smoothness in noise reduction of chaotic time series

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2011 International Conference on Computers, communications, Control and Automation

作者： Chenxi Shao Jiangong Ji Jingyuan Shao Lipeng Xiao Department of Computer Science and Technology University of Science and Technology of China Department of Modern Physics and Nonlinear Science Center University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Information center Jianghuai Automobile Co.Ltd.

Noise is an omnipresent phenomenon. It obscures the real behavior of dynamical system. Lots of methods are proposed to remove the noise contaminating time series. However, almost all the methods consider the noise reduction in the phase space and often sharp points are kept. Different with these methods, this paper proposes a method directly on the time series itself, considering the gauss noise feature and the smoothness of the real data, uses curve-fitting way to eliminate the sharp points. The numeral results verify the effectiveness of our method.

关键词： smoothness,noise reduction,curve-fitting,Local-Geometric-Projection

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Dynamic protocol conformance test method based on local error diagnostic information

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Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications 2007年第1期30卷 140-144页

作者： Lin, Hua-Hui Zhou, Hao Pan, Quan-Ke Zhao, Bao-Hua Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

To enhance the reliability and the fault coverage of the test results, a local error diagnostic mechanism was introduced into dynamic protocol conformance test, a new dynamic test method was proposed, whose principle and correctness was analyzed on determinate FSM model. The new method took full advantage of the dynamic information generated during test to avoid the incorrect locating of local error. Also a local error diagnostic method was presented on error sub-sequence, which was not restricted to single error assumption and more practical. The experimental results showed that the method can effectively avoid the descending of the test reliability and give better fault coverage.

关键词： Transmission control protocol

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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An Efficient and Slight Convolutional Neural Network for Vehicle Type Classification

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Haibo Zheng Naijie Gu Xiaoci Zhang Anhui Province Key Laboratory of Computing and Communication Software School of Computer and Technology University of Science and Technology of China Hefei China

With the popularity of small storage embedded devices, recent researches on deep convolutional neural networks no longer only focus on improving the classification accuracy but start to attach great importance to the size of convolutional neural networks. This paper proposes a small deep convolutional neural network for vehicle type classification, based on the Fire module and the residual module. The Fire module is applied to maintain a small number of parameters. The residual module is employed to accelerate the training process and improves the classification accuracy. Experimental results show that our model achieves 95.57% accuracy of vehicle type classification on an open source dataset BIT-Vehicle. This outperforms previous researches and some classical convolutional neural networks such as AlexNet, ResNet-32 and SqueezeNet. What is more, the size of our model is only 5.3 MB, which has 40X fewer parameters than AlexNet and has 15X fewer parameters than ResNet-32.

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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Self-adaptive method dealing with the end issue of EMD

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2007年第10期35卷 1944-1948页

作者： Shao, Chen-Xi Wang, Jian Fan, Jin-Feng Yang, Ming Wang, Zi-Cai MOE-Microsoft Key Laboratory of Multimedia Computing and Communication University of Science and Technology of China Hefei 230026 China Department of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Control and Simulation Center Harbin Institute of Technology Harbin 150001 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

The empirical mode decomposition (EMD) developed by Huang etc. of NASA is an advanced method for signal analysis. But there is an involved end issue in the course of getting two envelops of the data using spline interpolation. A self-adaptive method to dealing with the end issue is proposed. Generally, it extends the externa sequence near the ends of the data by the most suited sequence in the inner data. For the chosen inner sequence has the most similarity of tendency with the ends, the data extension is reasonable. After the extension, the spline does not swing at both ends of the data. The result of experiment proved that the method can be used to solve the end issue effectively.

关键词： Signal analysis

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