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arXiv 2018年

作者： He, Xudong Zhao, Junqiao Sun, Lu Huang, Yewei Zhang, Xinglian Li, Jun Ye, Chen Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07 m. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS. Copyright © 2018, The Authors. All rights reserved.

关键词： Mapping

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DSC: Deep scan context descriptor for large-scale place recognition

arXiv

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arXiv 2021年

作者： Cui, Jiafeng Huang, Tengfei Cai, Yingfeng Zhao, Junqiao Xiong, Lu Yu, Zhuoping School of Automobile Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to acquire centroids and eigenvalues of the segments. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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The Relationship between Diagnostic Coverage and Proof Test Interval for 1oo2 and 1oo2D Architectures

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IFAC Proceedings Volumes 2012年第24期45卷 181-184页

作者： İlker üstoğlu özgür T. Kaymakçı Josef Börcsök Yıldız Technical University Davutpaşa Campus A209 34220 34220 Esenler İstanbul TURKEY (Tel: +90-535-418-22-64 ) Yıldız Technical University Davutpaşa Campus A210 34220 34220 Esenler İstanbul TURKEY University Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Wilhelmshöher Allee 71 34121 Kassel GERMANY

In order to measure the effectiveness of safety protection systems there are several design parameters. Diagnostic coverage factor is one of the most important parameter which influences all architectures. In this short paper the relationship between PFD avg , DC and T I are presented. 1oo2 and 1oo2D architectures are considered as examples.

关键词： Functional safety 1002 architecture

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Design and implimentation of a real-time platform for studying interaction behaviors of scaled-down autonomous vehicles

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology 2015年 35卷 185-189页

作者： Wang, Xiao-Dong He, Wen Liu, Yu-Chao Guo, Mu Zhang, Tian-Lei Han, Peng Department of Computer Science and Technology Tsinghua University Beijing100081 China Institute of China Electronic System Engineering Corporation Beijing100141 China Chongqing Science and Technology Research Institute Chongqing402699 China

Autonomous vehicle was considered to be one of the toughest challenges in the fields of artificial intelligent, pattern recognition, computer vision, sensor fusion and control theory. In the paper, a kind of scaled-down autonomous vehicles was demonstrated and a more human-like environment perception and control system were proposed, including obstacle detection, combined feature-based and model-based lane mark detection algorithm. To verify the efficiency of the proposed perception and control system, a test platform was built. With the platform, the interaction behaviors between autonomous vehicles were studied. © 2015, Editorial department of Transaction of Beijing Institute of Technology. All right reserved.

关键词： Fuzzy control

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A PERSONALIZED MOOC LEARNING GROUP AND COURSE RECOMMENDATION METHOD BASED ON GRAPH NEURAL NETWORK AND SOCIAL NETWORK ANALYSIS

arXiv

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arXiv 2024年

作者： Luo, Zijin Wang, Xu Wang, Yiquan Zhang, Haotian Li, Zhuangzhuang College of Mathematics Jilin University Jilin Changchun130000 China College of Communication Engineering Jilin University Jilin Changchun130000 China College of Mathematics and System Science Xinjiang University Urumqi Xinjiang830046 China College of Computer Science and Technology Jilin University Jilin Changchun130000 China Department of Electronic Engineering Tsinghua University Beijing100084 China Shenzhen X-Institute Shenzhen518055 China

In order to enhance students’ initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students’ course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Association of PM_(2.5)Reduction with Improved Kidney Function:A Nationwide Quasiexperiment among Chinese Adults

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Health Data science 2022年第1期2022卷 115-123页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM_(2.5))on health outcomes related to metabolic disorders and distant *** exposure to ambient PM_(2.5)leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM_(2.5)reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-indifference *** in long-term exposure to ambient PM_(2.5)were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m^(3)reduction in PM_(2.5),a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95%confidence interval[CI]:0.06,0.78)mL/min/1.73m^(2),-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM_(2.5)on kidney *** results support a significant nephrotoxicity of PM_(2.5)based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： Kidney organs ambient

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Enhanced energy efficiency by implementing MHD flow and heat transfer in Cu-Al₂O₃/H₂O hybrid nanoparticles with variable viscosity

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Nano-Structures and Nano-Objects 2024年 40卷

作者： Majeed, Aaqib Ali, Parvez Kouki, Marouan Siddhu, Muhammad Kashif Department of Mathematics The University of Faisalabad Sargodha Road University Town Faisalabad 38000 Pakistan Department of Mechanical Engineering College of Engineering Qassim University Buraydah 51452 Saudi Arabia Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Computer Science The University of Faisalabad Sargodha Road University Town Faisalabad 38000 Pakistan

Hybrid nanofluids are engaged in phase-change materials and thermal energy storage systems to enhance heat transfer during the charging and discharging processes. Improved understanding of how variable viscosity and thermal radiation affect these fluids contributes to more efficient energy management. This study aims to formulate an efficient mathematical model for the two-dimensional flow of a hybrid nanofluid composed of copper (Cu) and alumina oxide (Al₂O₃) suspended with base fluid H₂O to form a hybrid fluid under the influence of thermal radiation. The present study also integrates the effects of variable viscosity and viscous dissipation. Electromagnetic radiation impact due to temperature also amalgamated. The governing PDEs are reformulated into ODEs via tailored similarity transformations. These reformulated equations are then numerically resolved using Bvp4c solver, leveraging the shooting method within MATLAB for precision and efficiency. The most significant results are predetermined relevant parameters, such as the prosperity parameter, magnetic parameter, radiation parameter, slip velocity parameter,Biot number, convention parameter, Eckert number, heat source parameter, Prandtl number on velocity and temperature distribution are inspected graphically and in the form of table. Outcomes illustrate that fluid velocity flattens by increasing magnetic parameters because there exists a Lorentz force that opposes the fluid motion, whereas enhancement is noted via radiation parameter. Compared to conventional nanofluid, temperature curves of hybrid nanoliquid is higher. Furthermore, recent results indicate strong agreement for a specific instance. © 2024 Elsevier B.V.

关键词： Computational model Convective parameter Hybrid nanofluid Magneto-hydrodynamic Porous surface Variable viscosity Viscous dissipation

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Deep crisp boundaries: From boundaries to higher-level tasks

arXiv

引用

arXiv 2018年

作者： Wang, Yupei Zhao, Xin Li, Yin Huang, Kaiqi Center for Research on Intelligent System and Engineering Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Biostatistics and Medical Informatics Department of Computer Sciences Univeristy of Wisconsin-Madison Center for Research on Intelligent System and Engineering National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology 100190

Edge detection has made significant progress with the help of deep Convolutional Networks (ConvNet). These ConvNet based edge detectors have approached human level performance on standard benchmarks. We provide a systematical study of these detectors' outputs. We show that the detection results did not accurately localize edge pixels, which can be adversarial for tasks that require crisp edge inputs. As a remedy, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve superior performance, surpassing human accuracy when using standard criteria on BSDS500, and largely outperforming state-of-the-art methods when using more strict criteria. More importantly, we demonstrate the benefit of crisp edge maps for several important applications in computer vision, including optical flow estimation, object proposal generation and semantic segmentation. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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A computing model for improving marketable quality and profitability of corporations

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International Journal of High Performance Computing and Networking 2008年第5-6期5卷 343-352页

作者： Barolli, Valbona Fukuda, Heihachiro Barolli, Leonard Takizawa, Makoto Department of Computer and Information Science Sekei University 3-3-1 Kichijoji-Kitamachi Musashino-Shi Tokyo 180-8633 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-ku Fukuoka 811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-ku Fukuoka 811-0295 Japan

In this paper, we provide an evaluation model for marketable quality and profitability of corporations. We apply the real values of some leading manufacturing corporations in Japan to our proposed model to analyse its accuracy. From the analysis, we concluded that the theoretical and real standard values of the marketable quality indicator were both 0.6. We showed that the proposed model gives a good approximation of economic trends of Japanese corporations. We obtained a general distribution of profitability by using the general profitability function and the marketable quality indicator. The general distribution is closer to the real value compared with the standard distribution. Copyright © 2008, Inderscience Publishers.

关键词： Profitability

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