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IEEE International Conference on Robotics and Biomimetics

作者： Qiaozhe Li Yu Qiao Jie Yang Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479973989

Visual tracking, when regarded as a classification problem, usually need sufficient amount of data for online learning. Current approaches usually employ multiple binary labeled samples to train or update the appearance model at each frame. However, most positive samples collected by perturbation of the target location are equally treated as the estimated target when used for updating. The exact target information might be ambiguous. In this paper, we propose a robust visual tracking algorithm based on local kernelized representation. The use of kernels incorporates the target information to better represent each samples. Local kernel scores, which measure the similarity between samples and target templates in specific feature space, are used to form feature vector to represent the entire region. A neural network is then deployed into the particle Alter framework to estimate target location. Experimental results on a variety of challenging sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

关键词： Conferences Robots Biomimetics

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An information Diffusion Model Based on Individual Characteristics

An Information Diffusion Model Based on Individual Character...

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第三十三届中国控制会议

作者： Jun Hua Lin Wang Xiaofan Wang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education

ISBN: (纸本)9781479947249

We proposes a model of information diffusion over social networks, in which individuals can be classified into innovative ones and conformable ones according to their reactions to new information. By simulations over various networks,we find that both the network structure and the composition of population have apparent effect on the extent of information diffusion. We also find that, in different stages of diffusion, innovative individuals and conformable individuals play different roles to promote the spread of information. Furthermore, initial conditions such as which kind of individual is initially activated also has impact on the diffusion.

关键词： Complex networks,information diffusion,Individual characteristics

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Structure reconstruction for linear network systems with dynamical structure functions

Structure reconstruction for linear network systems with dyn...

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第三十三届中国控制会议

作者： Tongcai Wang Lin Wang Xiaofan Wang Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education

ISBN: (纸本)9781479947249

The state-space model for linear network systems which represents the structure information, in general, cannot be recovered from the dynamical input-output data, since typically the realization problem does not have a unique *** structure functions have been introduced to represent the structural information only between the *** though, it has shown that for a given network composed of p measured individuals, in order to completely recover the relationships between the measurements, p experiments with each experiment independently controlling a measured individual must be performed. This paper will investigate the structure reconstruction for a kind of linear networks, in which all states are measurable, but only partial states can be directly controlled by external inputs. Therefore, the network structure cannot be inferred directly by making use of the dynamical structure function. A new algorithm is proposed, in which the relations between the nodes with input are obtained from the dynamical structure function;and then, the influences from the nodes with control input to the nodes without are deduced;and finally, a convex optimization algorithm with a sparsity assumption is used to infer the full structure information.

关键词： Network reconstruction,Linear systems,Dynamical structure function,Transfer function,Convex optimization

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A Noise Removal Approach for Object-based Classification of VHR Imagery via Post-classification

A Noise Removal Approach for Object-based Classification of ...

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International Conference on Audio, Language and Image processing

作者： Laiwen Zheng Lihong Wan Hong Huo Tao Fang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781479939046

The pixel-based classification of remotely sensed images always produces a large amount of "speckled" or "salt and pepper" noises. Both post-classification smoothing and object-based classification techniques have been proposed to tackle this problem. However, most of them are not adequate to deal with the noises in object-based classification of very high resolution (VHR) remote sensing imagery, because a lot of noisy regions will be produced by image segmentation and the existing post-classification approaches generally are tailored towards pixel-based classification. This paper proposes a novel noise removal approach for object-based classification of VHR imagery via post-classification. It includes four phases: firstly, an image is segmented into homogeneous regions;secondly, all regions are classified according to their spectral and texture features;thirdly, noisy regions are distinguished by using shape features. Finally, the noisy regions are removed by using contextual features. Experimental results show the proposed approach is effective and can improve the overall accuracy of classification of VHR remote sensing imagery.

关键词： VHR Remote Sensing Imagery Object-based Classification Noise Removal Post-classification

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A soft-sensing method with online correction based on semi-supervised learning

A soft-sensing method with online correction based on semi-s...

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第25届中国过程控制会议

作者： Qifeng Tang Dewei Li Yugeng Xi Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education

Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high ***,one inherent difficulty is insufficiency of the training samples because the labeled data is *** this,the traditional soft-sensing structure has no online correction *** forecasting result may be incorrect if the working condition is *** this work,a semi-supervised method is proposed to build the soft-sensing model by use of the unlabeled ***,an online correction mechanism is proposed to establish a soft-sensing approach,which estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation *** experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.

关键词： soft-sensing,semi-supervised learning(SSL),online correction,neural network(NN).

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

arXiv

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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

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Energy-aware effectiveness analysis in two-link MIMO networks

Energy-aware effectiveness analysis in two-link MIMO network...

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World Congress on Intelligent control and Automation (WCICA)

作者： Zhenzhe Sun Jing Wu Chengnian Long Dept. of Automation Key Lab. of System Control and Information Processing

This paper investigates energy effectiveness problem for 802.11 n+ protocol in heterogeneous MIMO networks. Specifically, we consider a two-link network, where a 2 × 2 MIMO link transmits concurrently with an 1 × 1 link. We show that the completely cancelling out all the interference may result in an ineffective transmission for the incoming link. It consumes much energy on eliminating interference but achieving low transmission rate. Based on the observation, we propose a compromising nulling approach to mitigate the energy effectiveness problem. Compromising nulling takes an energy metric for transmission and interference design, which can be converted into an optimal pre-coding design problem. Numerical results show that compromising nulling strikes a balance between interfering other transmissions and maintain its own transmission effectiveness.

关键词： Interference Measurement Signal to noise ratio MIMO Blogs Antennas Receivers

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Descriptor systems State-space Conditions to Guarantee Negative Imaginary Properties without Minimality Restrictions

Descriptor Systems State-space Conditions to Guarantee Negat...

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IEEE Annual Conference on Decision and control

作者： Junlin Xiong Alexander Lanzon Ian R. Petersen CAS Key Lab of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei 230026 China Control Systems Centre School of Electrical and Electronic Engineering University of Manchester Manchester M13 9PL U.K. School of Engineering and Information Technology University of New South Wales at the Australian Defence Force Academy Canberra ACT 2600 Australia

ISBN: (纸本)9781479978878

This paper is concerned with negative imaginary lemmas for descriptor systems. Without the minimal state-space realization assumption, sufficient conditions are developed for a negative imaginary lemma and a strictly negative imaginary lemma, respectively. As corollaries, sufficient conditions are derived to ensure the systems are both admissible and negative imaginary. Also, new sufficient negative imaginary lemmas are found for standard linear systems as special cases. The developed negative imaginary lemmas are applicable to descriptor systems with impulse modes. Two examples are used to illustrate the theory.

关键词： Theorem Negative Linear system Descriptors Lemma Sufficient conditions

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Flocking of partially-informed multi-agent systems avoiding obstacles with arbitrary shape

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Autonomous Agents and Multi-Agent systems 2014年第5期29卷 943-972页

作者： Li, Jiaojie Zhang, Wei Su, Housheng Yang, Yupu Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Measurement and Control Technology Shanghai Dian Ji University Shanghai China School of Automation Key Laboratory of Image Information Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan China

In this paper, we study the flocking problem of multi-agent systems with obstacle avoidance, in the situation when only a fraction of the agents have information on the obstacles. Obstacles of arbitrary shape are allowed, no matter if their boundary is smooth or non-smooth, and no matter it they are convex or non-convex. A novel geometry representation rule is proposed to transfer obstacles to a dense obstacle-agents lattice structure. Non-convex regions of the obstacles are detected and supplemented using a geometric rule. The uninformed agents can detect a section of the obstacles boundary using only a range position sensor. We prove that with the proposed protocol, uninformed agents which maintain a joint path with any informed agent can avoid obstacles that move uniformly and assemble around a point along with the informed agents. Eventually all the assembled agents reach consensus on their velocity. In the entire flocking process, no distinct pair of agents collide with each other, nor collide with obstacles. The assembled agents are guaranteed not to be lost in any non-convex region of the obstacles within a distance constraint. Numerical simulations demonstrate the flocking algorithm with obstacle avoidance both in 2D and 3D space. The situation when every agent is informed is considered as a special case.

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Evidence-based treatment of medical guideline

Communications in Computer and Information Science

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Communications in Computer and information Science 2014年 480卷 185-197页

作者： Tian, Pingfang Zhu, Zhonghua Huang, Zhisheng College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Realtime Industrial System Wuhan430065 China Department of Computer Science Vrije University of Amsterdam AmsterdamHV1081 Netherlands

Medical guidelines are recommendations on the appropriate treatment and care of people with specific diseases and conditions. Evidence-based medical guidelines are the document or recommendations which have been annotated with their relevant medical evidences, namely research findings from medical publications. We have observed the fact that there exist significant amount of medical guidelines have not yet annotated with relevant medical evidences, which becomes even more serious in the Chinese medical guidelines. In this paper, we propose an approach of evidence process of medical guidelines, such that we can find relevant evidences for those non-evidence-based medical guidelines. We develop a system called Link2Pubmed, which can retrieve the text which is described with a natural language and get the corresponding medical evidences. We use the word segmentation and part-of-speech tagging tools in natural language processing (NLP) to extract the keywords, and then translate them into corresponding English concepts in SNOMED CT, a well-known medical ontology. This system is an attempt to solve the existing problems in Chinese medical guidelines, which lack the annotations of relevant evidences. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Natural language processing systems

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