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arXiv 2019年

作者： Liu, Yong-Lei Wang, An-Min Wang, Guo-Dong Sun, Yi Zhang, Peng-Fei Department of Modern Physics University of Science and Technology of China Hefei230026 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China

Machine learning has achieved success in many areas because of its powerful Fitting ability, so we hope it can help us to solve some signiFicant physical quantitative problems, such as quantum correlation. In this research we will use neural networks to predict the value of quantum discord. Quantum discord is a measure of quantum correlation which is deFined as the difference between quantum mutual information and classical correlation for a bipartite system. Since the deFinition contains an optimization term, it makes analytically solving hard. For some special cases and small systems, such as two-qubit systems and some X-states, the explicit solutions have been calculated. However, for general cases, we still know very little. Therefore, we study the feasibility of estimating quantum discord by machine learning method on two-qubit systems. In order to get an interpretable and high performance model, we modify the ordinary neural network by introducing some prior knowledge which come from the analysis about quantum discord. Our results show that prior knowledge actually improve the performance of neural network. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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COSINE:a software development model integrating collective intelligence, service and ecosystem

COSINE:a software development model integrating collective i...

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IEEE World Congress on Services (SERVICES)

作者： Hongbing Wang Tianjing Hong Jian Cao Haijun Zhang Changhai Nie Bo Cheng Yangfan He Li Kuang Dunwei Gong Wuhui Chen Yuliang Shi Deyi Huang School of Computer Science and Engineering and Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Shanghai Jiaotong University Shanghai China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China Nanjing University Nanjing China Wuhan University Wuhan China Research Institute of Network Technology Beijing University of Posts and Telecommunications Beijing China Software College Central South University Changsha China College of information and control engineering China University of mining and technology Xuzhou China College of data science and computer science Zhongshan University Guangzhou China Shandong University Jinan China Landry Company Shenzhen China

ISBN: (数字)9781728182032

ISBN: (纸本)9781728182049

With the development of the internet technology, a large amount of softwares have emerged to meet users' increasing needs. At the mean time, software systems have been faced with a problem that they must adapt to the dynamic network environment. It is obvious that a variety of software development models have been proposed in the past few decades. However, the majority of these methods are gradually unadaptable to new circumstances. In this paper, we proposed a new software development model integrating collective intelligence, service and ecosystem. On the one hand, we have introduced the model in detail. On the other hand, We took a practical example to demonstrate the effectiveness of the proposed model.

关键词： Adaptation models Biological system modeling Ecosystems Collective intelligence Software systems

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Business models for the circular economy: Opportunities and challenges

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Business Strategy and the Environment 2019年第2期28卷 430-432页

作者： Fraccascia, Luca Giannoccaro, Ilaria Agarwal, Abhishek Hansen, Erik G. Department of Computer Control and Management Engineering “Antonio Ruberti” Sapienza University of Rome Rome Italy Department of Industrial Engineering and Business Information Systems University of Twente Enschede Netherlands Department of Mechanics Mathematics and Management Polytechnic University of Bari Bari Italy Department of Management Aberdeen Business School Robert Gordon University Aberdeen United Kingdom Institute for Integrated Quality Design (IQD) Johannes Kepler University Linz Linz Austria

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Distributed online convex optimization with time-varying coupled inequality constraints

arXiv

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arXiv 2019年

作者： Yi, Xinlei Li, Xiuxian Xie, Lihua Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex functions. A distributed online primal-dual dynamic mirror descent algorithm is proposed to solve this problem, where the local cost, regularization, and constraint functions are held privately and revealed only after each time slot. Without assuming Slater's condition, we first derive regret and constraint violation bounds for the algorithm and show how they depend on the stepsize sequences, the accumulated dynamic variation of the comparator sequence, the number of agents, and the network connectivity. As a result, under some natural decreasing stepsize sequences, we prove that the algorithm achieves sublinear dynamic regret and constraint violation if the accumulated dynamic variation of the optimal sequence also grows sublinearly. We also prove that the algorithm achieves sublinear static regret and constraint violation under mild conditions. Assuming Slater's condition, we show that the algorithm achieves smaller bounds on the constraint violation. In addition, smaller bounds on the static regret are achieved when the objective function is strongly convex. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2019, The Authors. All rights reserved.

关键词： Mirrors

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A comprehensive study and comparison of core technologies for MPEG 3D point cloud compression

arXiv

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arXiv 2019年

作者： Liu, Hao Yuan, Hui Liu, Qi Hou, Junhui Liu, Ju School of Information Science and Engineering Shandong University Qingdao266200 China School of Control Science and Engineering Shandong University Jinan250000 China Department of Computer Science City University of Hong Kong Hong Kong999077 Hong Kong

Point cloud based 3D visual re resentation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Color

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Design of iterative learning control schemes for spatially interconnected systems

Design of iterative learning control schemes for spatially i...

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IEEE Annual Conference on Decision and control

作者： Robert Maniarski Kamil Klimkowicz Wojciech Paszke Eric Rogers Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This paper develops novel procedures for design of iterative learning control schemes applied to spatially interconnected systems. In particular, differential linear repetitive process stability theory is used to design stabilizing and learning controllers for spatially interconnected systems composed of a finite number of linear continuous-time subsystems, where each one directly interacts with neighbouring subsystems. control law design is based on linear matrix inequality computations. Specifically, sufficient conditions for the existence of the required decentralized controllers are developed together with a design algorithm for the associated control law matrices. A simulation based case study on periodically interconnected subsystems in one spatial dimension is given to demonstrate the feasibility and effectiveness of the new design.

关键词： Symmetric matrices Stability analysis Asymptotic stability Linear matrix inequalities Interconnected systems Process control Computational modeling

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Autonomous Swimmers Tracking Algorithm Based on Kalman Filter and CamShift

Autonomous Swimmers Tracking Algorithm Based on Kalman Filte...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Muhammad Aftab Hayat Goutian Yang Atif Iqbal Adeel Saleem Muhammad Mateen School of Control & Computer Engineering North China Electric power University Beijing China School of Renewable Energy & Clean Energy North China Electric power University Beijing China Department of Electrical Engineering The University of Lahore Lahore Pakistan

ISBN: (数字)9781728146133

ISBN: (纸本)9781728146140

The visual system is the most important sensory organ of human beings. Most of the external information obtained by human beings comes from vision. With the continuous exploration of the natural world by human beings, people always hope to realize the function of human perception through some digital machine and automatically acquire the information of the outside world. The traditional CamShift algorithm needs to manually locate the target during tracking, and it can be used in complex backgrounds such as color interference and occlusion. In this paper, a comfortable and autonomous swimmer tracking algorithm based on Kalman filter with CamShift is proposed. First, we use the Canny edge detection and inter-frame difference method to segment the entire region of the moving swimmer and then use the extracted target region to initialize the CamShift algorithm. The modified search window supports the automatic tracking of the swimmer. When there is similar color interference in the background, or the swimmer is severely impeded, an improved algorithm combined the Kalman filter with the CamShift algorithm for tracking the target accurately. Experimental results confirmed that the enhanced algorithm significantly improves the outcomes and it can still follow the object effectively and steadily under severe occlusion, noisy environment and color interference.

关键词： Target tracking Image color analysis Image edge detection Interference Kalman filters Heuristic algorithms

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Privacy-preserving medical treatment system through nondeterministic finite automata

arXiv

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arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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ITSS Technical Activities Spotlight Getting to Know the Big Data and AI for Mobility Technical Committee

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2018年第3期10卷 205-205页

作者： Moreira-Matias, Luis School of Control and Computer Engineering North China Electric Power University Baoding China

The recent technological advances on telecommunications create a new reality on mobility sensing. Digital devices are now ubiquitous and able to broadcast rich information about human mobility in real-time. Such fact exponentially increased the availability of large-scale mobility data (i.e. Big Data) which has been popularized in the media as the new currency, fueling the future vision of our smart cities that will transform our lives. The reality is that we just began to recognize significant research challenges across a spectrum of topics. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders on build Artificial Intelligence applications leveraging on such data sources.

关键词： Big Data Artificial intelligence Machine learning Intelligent vehicles Wireless sensor networks Sensors

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Semi-Parametric Kernel-Based Identification of Wiener Systems 57

Semi-Parametric Kernel-Based Identification of Wiener System...

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57th IEEE Conference on Decision and control, CDC 2018

作者： Sven Risuleo, Riccardo Lindsten, Fredrik Hjalmarsson, Hakan Department of Automatic Control School of Electrical Engineering and Computer Science KTH - Royal Institute of Technology Stockholm Sweden Department of Information Technology Division of Systems and Control Uppsala University Uppsala Sweden

ISBN: (纸本)9781538613955

We present a technique for kernel-based identification of Wiener systems. We model the impulse response of the linear block with a Gaussian process. The static nonlinearity is modeled with a combination of basis functions. The coefficients of the static nonlinearity are estimated, together with the hyperparameters of the covariance function of the Gaussian process model, using an iterative algorithm based on the expectation-maximization method combined with elliptical slice sampling to sample from the posterior distribution of the impulse response given the data. The same sampling method is then used to find the posterior-mean estimate of the impulse response. We test the proposed algorithm on a benchmark of randomly-generated Wiener systems. © 2018 IEEE.

关键词： Impulse response

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