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Chinese Control Conference (CCC)

作者： Hui Lv Changjun Zhou Qiang Zhang Ministry of Education Key Laboratory of Advanced Design and Intelligent Computing (Dalian university) Dalian P.R.China

ISBN: (纸本)9781538629185

This paper is concerned with the sliding mode control of decaying-dimerizing system with three molecular species and four reaction channels. An integral sliding surface is designed, and the resulting dynamics is a nonlinear biochemical reaction system. By introducing some specified matrices, a sufficient condition is proposed in terms of strict linear matrix inequality (LMI), which guarantees the stability of the sliding mode dynamics. A sliding mode control law is also synthesized for reaching motion. Finally, simulation results illustrate the effectiveness of our methods.

关键词： Sliding mode control Stochastic processes Linear matrix inequalities Stability analysis Trajectory Biological system modeling

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On robust stabilization of uncertain singular Markovian jump systems with time-delay

On robust stabilization of uncertain singular Markovian jump...

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Chinese Control Conference (CCC)

作者： Hui Lv Changjun Zhou Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing (Dalian university) Ministry of Education Dalian P. R. China

ISBN: (纸本)9781538629185

This paper is concerned with the robust normalization and stochastic stabilizing problem for singular Markovian jump systems with norm-bounded parameter uncertainties and time delay. A novel proportional-derivative controller with memory is designed. Sufficient conditions are derived to ensure that the closed-loop system is not only robust normal, but also stochastically stable. In addition, the explicit expression of the desired control gains is also given by solving strict linear matrix inequalities. Finally, simulation results illustrate the effectiveness of our methods.

关键词： Robustness Symmetric matrices Delay effects Uncertainty Linear matrix inequalities Markov processes Control systems

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Texture classification in extreme scale variations using GANet

arXiv

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arXiv 2018年

作者： Liu, Li Chen, Jie Zhao, Guoying Fieguth, Paul Chen, Xilin Pietikäinen, Matti Center for Machine Vision and Signal Analysis University of Oulu Oulu90014 Finland Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing100190 China

Research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination, rotation, viewpoint and small scale changes. In contrast, in real-world applications a change in scale can have a dramatic impact on texture appearance, to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are amongst the hardest to handle. In this work we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a Genetic Algorithm to change the units in the hidden layers during network training, in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a FV-CNN (Fisher Vector pooling of a Convolutional Neural Network filter bank) feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing superior performance compared to the state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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Reversible Data Hiding Based on DNA computing

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Computational Intelligence and Neuroscience 2017年第20170101期2017卷 7276084-7276084页

作者： Wang, Bin Xie, Yingjie Zhou, Shihua Zhou, Changjun Zheng, Xuedong Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China Applied Technology College Dalian Ocean University Dalian116300 China

Biocomputing, especially DNA, computing has got great development. It is widely used in information security. In this paper, a novel algorithm of reversible data hiding based on DNA computing is proposed. Inspired by the algorithm of histogram modification, which is a classical algorithm for reversible data hiding, we combine it with DNA computing to realize this algorithm based on biological technology. Compared with previous results, our experimental results have significantly improved the ER (Embedding Rate). Furthermore, some PSNR (peak signal-to-noise ratios) of test images are also improved. Experimental results show that it is suitable for protecting the copyright of cover image in DNA-based information security. © 2017 Bin Wang et al.

关键词： Signal to noise ratio

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Four digits BCD adder with DNA strand displacement

Four digits BCD adder with DNA strand displacement

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International Conference on Systems and Informatics (ICSAI)

作者： Lanlan Shi Changjun Zhou Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

As a way of biological computing technology, DNA strand displacement has become more and more popular in recent years. With the principle of the DNA strand displacement, a four digits BCD adder is presented in this paper, in which single-stranded DNA express input signals and output signals, and double-stranded DNA represent gates. The progress of DNA strand displacement reaction can be easily achieved and operated. The proposed adder can autonomously correct its computing results, when the computing results overflows. Then, the reaction progress is simulated in the Visual DSD software. The experiment results of the four digits BCD adder show that the design of the four digits BCD adder is feasible and successful. The proposed BCD adder provides a new pattern to the development of DNA strand displacement computing.

关键词： DNA Adders Logic gates Visualization Logic circuits Software Integrated circuit modeling

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DNA self-assembly model to solve compound logic operators problem 11th

DNA self-assembly model to solve compound logic operators pr...

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11th International Conference on Bio-inspired computing – Theories and Applications, BIC-TA 2016

作者： Zhou, Shihua Wang, Bin Zheng, Xuedong Zhou, Changjun Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China

ISBN: (纸本)9789811036101

Self-assembly is the process that the component form an ordered form or structure. Because of the biochemical characteristics of DNA molecules, they become a research emphasis in the field of selfassembly. DNA-based self-assembly technology has been widely used in the fields of nanometer machining, molecular circuit, polymer materials, and so on. DNA self-assembly is an effective mechanism that nanometer structure is built bottom-up. In order to overcome the problem that any kind of self-assembled model can only solve the single algorithm, in this paper, a new DNA self-assembly algorithmic model is designed to solve compound logic operators problem. Five types of DNA tiles are designed according to the characteristic of compound operation problem, namely Initial Tile, Process Tile, Operation Tile, End Tile and Boundary Tile. At last, the process of self-assembly are demonstrated by an instance. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Self assembly

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Human motion data editing based on a convolutional automatic encoder and manifold learning

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Entertainment computing 2019年 30卷

作者： Dongsheng Zhou Xinzhu Feng Xin Yang Qiang Zhang Xiaopeng Wei Xiaoyong Fang Deyun Yang Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian 116622 China College of Computer Science and Technology Dalian University of Technology Dalian 116024 China Research Institute of Human Factors and Safety Engineering Hunan Institute of Technology Hengyang Hunan Province 421002 China School of Information Science and Technology Taishan University Tai’an Shandong Province 271000 China

Technology of human motion capture has been widely used in digital entertainment field. Editing the existing large amount of human motion capture data, correcting and eliminating motion distortion caused by noise and other defects have important value and significance for data reuse. In this paper, data processing is carried out based on convolutional automatic encoder and manifold learning. The popular structure of human motion data was learned by a one-dimensional time domain convolution automatic encoder, in which the hidden unit of the automatic encoder represents motion data. Three constraints were used to overcome the problem that the hidden unit has too much motion editing range. The data to be processed in this paper has no limit on the number of motions. The proposed method can process large data sets in parallel and automatically perform manifold learning without manual labelling and segmentation. In the final, comparative experiments based on a variety of damaged motion data have been carried out. The results showed that the proposed method can effectively reduce the error of the original motion data, and has achieved good results in both objective evaluation and subjective evaluation.

关键词： Human motion data Convolution automatic encoder Manifold learning Motion editing

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Recommendation system based on trusted relation transmission

Recommendation system based on trusted relation transmission

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Yixiong Bian Huakang Li Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

With the rapid development of the internet, applications of recommendation systems for online shops and entertainment platforms become more and more popular. In order to improve the effectiveness of recommendation, external information has been incorporated into various algorithms, such as location and social relationship. However, most algorithms only focus on the introduction of external information without depth analysis of the intrinsic mechanism in the external information. This paper proposed a transfer model of social trusted relationship, and optimized the reliability of the transfer model using pruning algorithm based on original trust recommendation. A credible social relationship macro-transfer model based on iterations of new credible relationships is defined by the similarity of social relationships. With a certain interest topic as a source of information, a micro-transfer model achieves the theme of interest and credibility of the expansion using social information dissemination algorithm. To demonstrate the effectiveness of the macro and micro credible transfer models, we used the Mantra search tree pruning algorithm and the optimization algorithm of similar category replacing similar products. The experimental results show that the proposed method based on the macroscopic and microscopic transfer models of the trusted relationship enhances the success rate and stability of the recommended system.

关键词： Collaboration Reliability Social network services Heuristic algorithms Optimization System performance Convergence

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Overdue Prediction of Bank Loans Based on Deep Neural Network

Overdue Prediction of Bank Loans Based on Deep Neural Networ...

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2017 International Symposium on Computer Science and Artificial Intelligence（ISCSAI 2017）

作者： Li Xin Sun Guozi Li Huakang Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Collaborative Innovation Center for Economics crime investigation and prevention technology State Key Laboratory of Mathematical Engineering and Advanced Computing

With the development of information technology, the application of big data in financial aspects becomes more and more deepening. However, in the aspect of bank loans, the accuracy of traditional user loan risk prediction models, such as KNN, Bayesian, are not benefit from the data *** paper proposes to use DNN algorithm to forecast the risk of user loan based on the difficulties of current overdue prediction and the excellent learning ability of DNN. This article uses user basic information, bank records, user browsing behavior, credit card billing records, and loan time information to evaluate whether users are delinquent. Firstly, this paper record bank records according to the transaction type, respectively, to generate income and spending data. Secondly, to sum the user browsing behavior also, and to record the average of credit card bill. In addition, in order to reduce the effect of eigenvalue size on the result, all characteristics are standardized. Finally, users who lack user information are discarded and the above fields are spliced. The spliced fields are the basic input for DNN. From the experimental results, DNN algorithm inc rease over 6% prediction than kNN, Bayes algorithm.

关键词： Overdue Prediction Bank Loans DNN

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