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作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

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Analytical modeling of nonlinear fiber propagation for four dimensional symmetric constellations

arXiv

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arXiv 2020年

作者： Rabbani, Hami Ayaz, Mostafa Beygi, Lotfollah Liga, Gabriele Alvarado, Alex Agrell, Erik Karlsson, Magnus EE Dept. K. N. Toosi University of Technology Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Dept. of Electrical Engineering Chalmers University of Technology Sweden Dept. of Microtechnology and Nanoscience Photonics Laboratory Chalmers University of Technology Sweden

Coherent optical transmission systems naturally lead to a four dimensional (4D) signal space, i.e., two polarizations each with two quadratures. In this paper we derive an anlaytical model to quantify the impact of Kerr nonlinearity on such 4D spaces, taking the interpolarization dependency into account. This is in contrast to previous models such as the GN and EGN models, which are valid for polarization multiplexed (PM) formats, where the two polarizations are seen as independent channels on which data is multiplexed. The proposed model agrees with the EGN model in the special case of independent two-dimensional modulation in each polarization. The model accounts for the predominant nonlinear terms in a WDM system, namely self-phase modulation and and cross-phase modulation. Numerical results show that the EGN model may inaccurately estimate the nonlinear interference of 4D formats. This nonlinear interference discrepancy between the results of the proposed model and the EGN model could be up to 2.8 dB for a system with 80 WDM channels. The derived model is validated by splitstep Fourier simulations, and it is shown to follow simulations very closely. Copyright © 2020, The Authors. All rights reserved.

关键词： Polarization

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Inverse-system method for identification of damping-rate functions in non-Markovian quantum systems

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Physical Review A 2020年第4期102卷 042227-042227页

作者： Shibei Xue Lingyu Tan Rebing Wu Min Jiang Ian R. Petersen Department of Automation Shanghai Jiao Tong University Shanghai 200240 People's Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 People's Republic of China Department of Automation Tsinghua University Beijing 100084 People's Republic of China Center for Quantum Information Science and Technology TNList Beijing 100084 People's Republic of China School of Electronic and Information Engineering Soochow University Suzhou 215006 People's Republic of China Research School of Electrical Energy and Materials Engineering The Australian National University Canberra 0200 Australia

Identification of complicated quantum environments lies in the core of quantum engineering, which systematically constructs an environment model with the aim of accurate control of quantum systems. In this paper, we present an inverse-system method to identify damping rate functions, which describe non-Markovian environments in time-convolution-less master equations. To access information on the environment, we couple a finite-level quantum system to the environment and measure time traces of local observables of the system. By using sufficient measurement results, an algorithm is designed, which can simultaneously estimate multiple damping rate functions for different dissipative channels. Further, we show that identifiability for the damping rate functions corresponds to the invertibility of the system and a necessary condition for identifiability is also given. The effectiveness of our method is shown in examples of an atom and three-spin-chain non-Markovian systems.

关键词： Open quantum systems Quantum control

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Leaf Recognition Based on Capsule Network 15th

Leaf Recognition Based on Capsule Network

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Zheng, Yang Yuan, Chang-An Shang, Li Huang, Zhi-Kai Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China

ISBN: (纸本)9783030267629

Plant is an indispensable part of human life, so it is very important to identify and protect plants. Convolutional neural network is a commonly used neural network for image recognition. However, convolutional neural network has huge defects. In order to avoid the defects of convolutional neural network, this paper adopts a new type of neural network: capsule neural network to complete plant leaf recognition. The experiment shows that the capsule neural network has a more effective recognition rate than the common neural network in plant leaf recognition. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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Motif Discovery via Convolutional Networks with K-mer Embedding 15th

Motif Discovery via Convolutional Networks with K-mer Embedd...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Wang, Dailun Zhang, Qinhu Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

With the rapid development of deep learning, some discriminative motif discovery methods based on deep neural network are gradually becoming the mainstream, which also bringing huge improvement of prediction accuracy. In this paper, we propose a convolutional neural network based architecture (eCNN), combining embedding layer with GloVe. Firstly, eCNN divides each single sequence of ChIP-seq datasets into multiple subsequences called k-mers by a sliding window, and then encoding k-mers into a relatively low dimension vectors by GloVe, and finally scores each vector using multiple convolutional networks. The experiment shows that our architecture can get good results on the task of motif discovery. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings

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Hierarchical Attention Network for Predicting DNA-Protein Binding Sites 15th

Hierarchical Attention Network for Predicting DNA-Protein Bi...

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Yu, Wenbo Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analyses of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN) are introduced to motif discovery task and have achieved state-of–art performance. However, these methods still have limitations such as neglecting the context information in large-scale sequencing data. Thus, inspired by the similarity between DNA sequence and human language, in this paper we propose a hierarchical attention network for predicting DNA-protein binding sites which is based on a natural language processing method for document classification. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea. © 2019, Springer Nature Switzerland AG.

关键词： Transcription factors

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Benchmarking of data predictive control in a real-life apartment during heating season

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Journal of Physics: Conference Series 2021年第1期2042卷

作者： Benjamin Huber Felix Bünning Antoon Decoussemaeker Philipp Heer Ahmed Aboudonia John Lygeros Urban Energy Systems Laboratory Swiss Federal Laboratories for Materials Science and Technology Empa Dübendorf Switzerland Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Switzerland

Model Predictive control is an energy efficient climate control strategy in buildings. However, the effort associated with physics-based modelling seems to prevent widespread application in residential buildings. Applying machine-learning algorithms on historical data promises efficient generation of predictive models for control. In a recent experimental study, Data Predictive control based on random forests and linear models outperformed a baseline controller during cooling season. In this paper, the approach is benchmarked against hysteresis control and conventional Model Predictive control based on an RC-network model during heating season. Data Predictive control shows promising results in terms of energy consumption and thermal comfort.

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An inverse-system method for identification of damping rate functions in non-Markovian quantum systems

arXiv

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arXiv 2020年

作者： Xue, Shibei Tan, Lingyu Wu, Rebing Jiang, Min Petersen, Ian R. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Center for Quantum Information Science and Technology TNList Beijing100084 China School of Electronic and Information Engineering Soochow University Suzhou215006 China Research School of Electrical Energy and Materials Engineering Australian National University Canberra0200 Australia

Identification of complicated quantum environments lies in the core of quantum engineering, which systematically constructs an environment model with the aim of accurate control of quantum systems. In this paper, we present an inverse-system method to identify damping rate functions which describe non-Markovian environments in time-convolution-less master equations. To access information on the environment, we couple a finite-level quantum system to the environment and measure time traces of local observables of the system. By using sufficient measurement results, an algorithm is designed, which can simultaneously estimate multiple damping rate functions for different dissipative channels. Further, we show that identifiability for the damping rate functions corresponds to the invertibility of the system and a necessary condition for identifiability is also given. The effectiveness of our method is shown in examples of an atom and three-spin-chain non-Markovian systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

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Optimization of Grouping Evacuation Strategy in High-rise Building Fires Based on Graph Theory and Computational Experiments

引用

IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1104-1112页

作者： Yuling Hu Xiwei Liu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044China State Key Laboratory fo Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing 100190China Qingdao Academy of Intelligent Industries Qingdao 266109China

It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.

关键词： Computational experiments grouping evacuation graph theory high-rise building fire management strategies

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