检索结果-内蒙古大学图书馆

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Tim Dieter Eberhardt Tim Brühl Robin Schwager Tin Stribor Sohn Wilhelm Stork Dept. of Highly Automated Driving Systems Dr. Ing. h.c. F. Porsche AG Weissach Germany Institute for Information Processing Technologies Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350387278

ISBN: (纸本)9798350387285

Advanced Driver Assistance systems (ADAS) rely extensively on sensors to perceive and interpret their environment, enabling safe and efficient navigation, object detection or other functionalities. However, sensor blockage due to environmental contaminants such as dirt, rain, or snow poses a significant challenge, impacting sensor performance and consequently ADAS reliability. This paper presents a comprehensive taxonomy and survey on sensor blockage in ADAS, focusing on cameras, radars, and lidar sensors. The survey begins with an taxonomy overview of sensor principles used in ADAS and their vulnerabilities to blockage. Cameras, based on geometric optics, are susceptible to opaque contaminants like mud and transparent ones like raindrops. Advanced studies in computer vision and deep learning techniques are discussed for detecting and mitigating these contaminants. Radar systems, operating on electromagnetic waves, face blockage challenges from snow and water, affecting their range and accuracy. Detection algorithms and impact analyses highlight methods to mitigate these effects, crucial for maintaining radar reliability in adverse weather conditions. Lidar sensors, using laser pulses for precise distance measurement, are vulnerable to dust and moisture accumulation. Detection strategies such as total internal reflection and deep neural networks are reviewed, emphasizing advancements in contamination identification and cleaning mechanisms. The paper concludes with insights into the overall impact of sensor blockage on ADAS, stressing the need for robust detection algorithms and standardized testing protocols.

关键词： Surveys Laser radar Taxonomy Radar Benchmark testing Cameras Sensor systems Sensors Detection algorithms Contamination

来源：评论

学校读者我要写书评

暂无评论

What the Cluster: An Unsupervised Framework to Segment Automotive Doppler Radar Pointclouds

What the Cluster: An Unsupervised Framework to Segment Autom...

引用

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

ISBN: (数字)9798350387278

ISBN: (纸本)9798350387285

The accurate identification of clusters from a radar point cloud is crucial for the safe operation of current Advanced Driver Assistance systems (ADAS). Robust clustering of point clouds enables the correct identification and localization of other safety-critical traffic participants. Particularly, the case of undetected traffic participants presents an unsafe and dangerous situation. This paper introduces the Layered Doppler-Position Clustering (LDPC) framework for this task. In the first layer, static and dynamic detections are separated considering the translational and rotational movement of the vehicle. In the second layer, pre-clusters are developed based on one-dimensional hierarchical clustering using the Doppler velocity. In the final layer, each pre-cluster undergoes further clustering using DBSCAN and the position data. By separating the process into layers, LDPC effectively combines Doppler velocity and position data for clustering. Combining both variables with different units, such as meters and meters per second, in a distance function, violates the semantic integrity of the data. Furthermore, the experimental results shown in this paper prove the applicability of the proposed LDPC framework. In various driving situations, the accuracy of the clustering results can be improved compared to existing methods. Additionally, we consider the safety-critical limits concerning specific radar sensor errors. Across all validation data, we achieved higher accuracy compared to existing systems in our experimental results.

关键词： Point cloud compression Meters Accuracy Semantics Radar tracking Parity check codes Doppler radar Doppler effect Advanced driver assistance systems Vehicle dynamics

来源：评论

学校读者我要写书评

暂无评论

A hybrid model based on stacking and multi-correction mechanisms for urban water demand prediction ⁎

引用

IFAC-PapersOnLine 2020年第2期53卷 16685-16690页

作者： Yang Lan Jingcheng Wang Miaoshun Bai Ibrahim Brahmia Haotian Xu Piao Hu Yuhao Long Yeming Zhang Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of education of China Shanghai 200240 China. Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi710021 China. Shanghai Municipal engineering Design Institute (Group) Co. Ltd.

Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water demand prediction models to guarantee high accuracy for a long period. Different holiday types and even tiny changes in temperatures can affect urban water demand seriously. This paper proposes a Stacking-based hybrid model which integrates multi-correction mechanisms to address these problems. A better stacking model is proposed to minimize the generalization error. The stability and reliability of predictions are improved through the design of multi-correction mechanisms such as high temperature weather compensation feature, holiday-type correction model and water quantity fluctuation correction model. Comparing different models before and after stacking also before and after correcting, the prediction accuracy of the proposed hybrid model is much higher and the predictions are more stable and reliable.

关键词： Water supply system Prediction methods Hybrid models Stacking methods Multi-correction mechanisms Neural-network models Time-series analysis

来源：评论

学校读者我要写书评

暂无评论

A New Method for Deriving Weights in Group Fuzzy Analytic Hierarchy Process and Evaluation Measures

引用

IFAC-PapersOnLine 2020年第2期53卷 7941-7946页

作者： Lin Che Yeming Zhang Jingcheng Wang Miaoshun Bai Dept. of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Municipal Engineering Design Institute (Group) Co. Ltd. China Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an Shaanxi 710021 China

The paper discusses fuzzy comparison matrices, consistency check, weight prioritization methods and weight evaluation methods in fuzzy group analytic hierarchy process. There are various methods of weight prioritization, however, they are not critically evaluated. In the paper, two measures are introduced for the evaluation of the group weights. Then, a new method is proposed to improve the process of deriving weights and use it in an application compared to another common three methods. Our results show that the new method is a good method for deriving weights of indexes.

关键词： Group Fuzzy AHP Ari-Geo Method Assessment Measure Fuzzy Consistency Check Triangular Fuzzy Number

来源：评论

学校读者我要写书评

暂无评论

control SYSTEM DESIGN CONSIDERING A TRADEOFF BETWEEN EVALUATED UNCERTAINTY RANGES AND control PERFORMANCE

引用

Asian Journal of control 2008年第1期1卷

作者： Y. Wakasa Y. Yamamoto Dept. of Applied Analysis and Complex Dynamical Systems Graduate School of Informatics Kyoto University Kyoto Japan. Yuji Wakasa was born in Okayama Japan in 1968. He received the B.S. and M.S. degrees in engineering from Kyoto university Japan in 1992 and 1994 respectively. From 1994 to 1998 he was a Research Associate in the Department of Information Technology Okayama University. Since April 1998 he has been a Research Associate in the Graduate School of Informatics Kyoto University. His current research interests include robust control and control system design via mathematical programming. Yutaka Yamamoto received his B.S. and M.S. degrees in engineering from Kyoto University Kyoto Japan in 1972 and 1974 respectively and the M.S. and Ph.D. degree in mathematics from the University of Florida in 1976 and 1978 respectively. From 1978 to 1987 he was with Department of Applied Mathematics and Physics Kyoto University and from 1987 to 1997 with Department of Applied System Science. Since 1998 he is a professor at the current position. His current research interests include realization and robust control of distributed parameter systems learning control sampled-data systems and digital signal processing. Dr. Yamamoto is a receipient of the Sawaragi memorial paper award (1985) the Outstanding Paper Award of SICE (1987) Best Author Award of SICE (1990) the George Axelby Outstanding Paper Award of IEEE CSS in 1996 Takeda Paper Prize of SICE in 1997. He is a Fellow of IEEE. He was an associate editor of Automatica. He is currently an associate editor of IEEE Transactions on Automatic Control Systems and Control Letters and Mathematics of Control Signals and Systems. He is a member of the IEEE the Society of Instrument and Control Engineers (SICE) and the Institute of Systems Control and Information Engineers.

This paper presents a design method of control systems such that a designer can flexibly take account of tradeoffs between evaluated uncertainty ranges and the level of control performance. The problem is reduced to a BMI problem and approximately solved by LMIs. The convergence of the proposed approximation is proved in a modified sense. A numerical example shows the effectiveness of the proposed method in comparison with the standard robust control.

关键词： control system design LMI BMI tradeoff curves

来源：评论

学校读者我要写书评

暂无评论

LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

引用

Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

来源：评论

学校读者我要写书评

暂无评论

COMMERCIAL APPLICATIONS OF ADVANCED MARINE VEHICLES FOR EXPRESS SHIPPING

引用

NAVAL ENGINEERS JOURNAL 1983年第3期95卷 283-300页

作者： LUEDEKE, G FARNHAM, RB JR. George Luedeke Jr.: received his BS degree in Mechanical Engineering from Massachusetts Institute of Technology and his MS degree in Product Design from Illinois Institute of Technology. Early in his career Mr. Luedeke joined General Motors Corporation as a designer responsible for development of people mover and rail rapid transit systems. From 1964 to 1974 he was with Hughes Aircraft Company. At Hughes he performed analyses and developed designs for a wide variety of program and proposal efforts such as: High Speed Ground Transportation (DOT) Task Force Command Center (NAVY) Panama Canal Marine Traffic Control Center (Panama Canal Co.) Royal Iranian Navy Command Center (Iran) Tactical Information Processing and Interpretation Center (Air Force) and WALLEYE CONDOR and PHOENIX Missile Systems (NAVY). He also had marketing development responsibilities related to the diversification of Hughes resources in civil business areas such as: Automatic train control (WMATA BARTD SCRTD) water/sewage treatment plant automation (Santa Clara County) Aqueduct Control (SWR) Hydrometeorological data collection (BPA WMO) and Salton Sea basin systems analysis (Dept. of the Interior). He was responsible for combat system integration for the Hughes 2000T Surface Effect Ship (SES) proposal. He also conducted detailed studies concerning ship flexure for the Improved Point Defense Target Acquisition System Program and for the definition of operational High Energy Laser weapon installations on a series of conventional monohulls (DLG DD and CVN). Since 1974 Mr. Luedeke has been employed at RMI Inc. (formerly Rohr Marine Inc.). During this time he has held several positions. His responsibilities have included directing a number of studies on advanced SES concepts managing activities defining mission/cost effectiveness of military and commercial SES's including defining the operational benefits and enhanced survivability characteristics of cargo SES's for high speed military sealiftfor NA TO and Southeast Asia

This paper will present the results of a marketing, engineering, and economic analysis of advanced marine vehicles done by IMA Resources, Inc. and RMI, Inc., in support of a Maritime Administration project to study “Multimode Express Shipping”. The study was conducted in 1981 and examined the economic benefits of using advanced marine vehicles as express cargo vessels in domestic and international service. Commodity characteristics, desirable express carrier rates, and potential high payoff service and route alternatives were identified. Advanced marine vehicles were surveyed and sized to meet desirable deadweight and block speed objectives. The costs of operating these craft on a variety of trade routes were calculated using an advanced marine vehicle economic analysis program. Revenues, expenses, break-even, profit and loss, cash flow requirements, tax summary and economic indicators (i.e., cost/ton – mile, etc.) were projected over the expected life of the vehicles as was return on investment. Traffic density and market penetration considerations narrowed the field of choice to smaller sized advanced marine vehicle carriers (i.e., 50 and 250 ton deadweight) and to three international and five domestic routes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：