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IEEE International Conference on systems, Man and Cybernetics

作者： Lingyu Tan Zhengyi Sun Min Jiang Shibei Xue Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai Peoples Republic of China School of Electronics and Information Engineering Soochow University Suzhou Peoples Republic of China

ISBN: (纸本)9781665442084

In this paper, we focus on detection of quantum colored noise and present a novel detection method which employs a quantum harmonic oscillator as a noise probe. With respect to an unknown spectrum of the quantum colored noise, quantum spectral decomposition theorem is applied to obtain a linear system model for the internal modes of the quantum colored noise, where the spectrum is parameterized. Then we establish a linear augmented model for the whole system which consists of a probe of a quantum oscillator and the quantum linear system model of the quantum colored noise. Due to the equivalence between the augmented model and the actual system, we estimate unknown parameters of the linear system model for the noise by solving an optimization problem. The effectiveness of our method is verified by an example of identification of quantum Lorentzian noise.

关键词： Linear systems Quantum mechanics Stochastic processes Process control White noise Harmonic analysis Colored noise

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An Integrated Neural Unmanned system for Multi-Domain Perception and Decision-Making

An Integrated Neural Unmanned System for Multi-Domain Percep...

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IEEE International Conference on Robotics and Biomimetics

作者： Qiming Liu Neng Xu Jinpeng Zhang Hesheng Wang Department of Automation Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Second Academy of China Aerospace Science and Industry Corporation (CASIC) Beijing China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665481106

Most recent intelligent systems study the perception or policy modules independently but ignore the inter-connection between them, which results in low information efficiency and high network redundancy, in the end, leads to the incomplete utilization of network potential and poor system-level end-to-end intelligence. This paper proposes an integrated end-to-end network that adapts to multi-domain inputs and multi-function outputs. “Integration” is defined in this paper in mainly three aspects: network structure level, optimization level, and functional level. The integrated network architecture is capable of fusing multiple raw sensory inputs and outputting multiple perceptions and decisions simultaneously. We first implement the integrated system in a virtual simulation environ-ment and investigate the domain transformation methods to real world. Comprehensive experiment results in both virtual and real environments show that our proposed system can behave normally under exploration, target-driven navigation and target shooting tasks.

关键词： Training Point cloud compression Target tracking Navigation Robot kinematics Redundancy Robot sensing systems

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Spatial-nonlocality-induced non-Markovian electromagnetically induced transparency in a single giant atom

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Physical Review A 2022年第4期106卷 043710-043710页

作者： Y. T. Zhu S. Xue R. B. Wu W. L. Li Z. H. Peng M. Jiang 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 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 People's Republic of China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing 100084 People's Republic of China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 People's Republic of China School of Electronics and Information Engineering Soochow University Suzhou 215006 People's Republic of China

The nonlocal waveguide coupling of giant atoms can bring up novel physics and applications. In this paper, we find unconventional electromagnetically induced transparency in giant atoms that has not been reported so far. We present a consistent theory for describing the unconventional EIT which includes a real-space scattering method and a time-delayed master equation. Our analysis shows this phenomenon is a full quantum effect led by a quantized interaction between the atom and the waveguide. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom. This phenomenon results from the inherent nonlocality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time.

关键词： Quantum optics with artificial atoms Scattering theory Quantum master equation

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Double Backpropagation for Training Autoencoders against Adversarial Attack

arXiv

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arXiv 2020年

作者： Sun, Chengjin Chen, Sizhe Huang, Xiaolin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important because they are widely used as a compression scheme for data storage and transmission, however, the current autoencoders are easily attacked, i.e., one can slightly modify an input but has totally different codes. The vulnerability is rooted the sensitivity of the autoencoders and to enhance the robustness, we propose to adopt double backpropagation (DBP) to secure autoencoder such as VAE and DRAW. We restrict the gradient from the reconstruction image to the original one so that the autoencoder is not sensitive to trivial perturbation produced by the adversarial attack. After smoothing the gradient by DBP, we further smooth the label by Gaussian Mixture Model (GMM), aiming for accurate and robust classification. We demonstrate in MNIST, CelebA, SVHN that our method leads to a robust autoencoder resistant to attack and a robust classifier able for image transition and immune to adversarial attack if combined with GMM. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Universal adversarial attack on attention and the resulting dataset damagenet

arXiv

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arXiv 2020年

作者： Chen, Sizhe He, Zhengbao Sun, Chengjin Huang, Xiaolin Department of Automation Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

Adversarial attacks on deep neural networks (DNNs) have been found for several years. However, the existing adversarial attacks have high success rates only when the information of the attacked DNN is well-known or could be estimated by structure similarity or massive queries. In this paper, we propose an Attack on Attention (AoA), a semantic feature commonly shared by DNNs. The transferability of AoA is quite high. With no more than 10 queries of the decision only, AoA can achieve almost 100% success rate when attacking on many popular DNNs. Even without query, AoA could keep a surprisingly high attack performance. We apply AoA to generate 96020 adversarial samples from ImageNet to defeat many neural networks, and thus name the dataset as DAmageNet. 20 well-trained DNNs are tested on DAmageNet. Without adversarial training, most of the tested DNNs have an error rate over 90%. DAmageNet is the first universal adversarial dataset and it could serve as a benchmark for robustness testing and adversarial training. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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Vision Based Polygon Detection system for Industrial Robots under Complex Illumination

Vision Based Polygon Detection System for Industrial Robots ...

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IEEE International Conference on Robotics and Biomimetics

作者： Hao Gao Weidong Chen Ruimin Wu Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing R&D Center Baoshan Iron & Steel Co. Ltd.

ISBN: (纸本)9781665405362

In this paper, we design a polygon detection and localization system for torpedo car capping in steelmaking scenarios. The existing target detection algorithms are, to a large extent, susceptible to the interference from illumination in complex industrial scenarios, which highly motivates our work. First, we enhance the image using histogram key regions based histogram specification. The method can adaptively analyze the illumination conditions and perform histogram specification using different mapping functions set up in advance. Then, a line segment detector is utilized to extract image features and complete the polygon fitting. Finally, we present experiments conducted on image datasets and grabbing tests, which shows both the accuracy and robustness of our algorithm.

关键词： Location awareness Histograms Service robots Lighting Object detection Feature extraction Robustness

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Trailer Tag Hitch: An Automatic Reverse Hanging system Using Fiducial Markers 7th

Trailer Tag Hitch: An Automatic Reverse Hanging System Using...

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7th International Conference on Cognitive systems and information processing, ICCSIP 2022

作者： Lu, Dongxi Yuan, Wei Lian, Chaochun Yao, Yongchun Cai, Yan Yang, Ming Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China SAIC-GM-Wuling Automobile Co. Ltd. Guangxi Liuzhou545007 China

ISBN: (纸本)9789819906161

Unmanned tractor-trailer vehicles are widely used in factory transportation scenarios. However, the trailer hitching process is still manually operated. The automatic trailer hitching is the precondition of fully unmanned logistics. Existing auto hitching methods directly detect the trailer or coupler features, making them hard to generalize and deploy to various types of trailers and couplers. To address this problem, this paper proposes a trailer hitch system using fiducial markers. The system is divided into two modules: hitch coupler pose estimation and visual servoing. An algorithm based on AprilTag detection is used for hitch coupler pose estimation. The pose messages guide the tractor to reverse to the trailer. An algorithm based on decoupled lateral-longitudinal control is used for visual servoing. The proposed system is experimented on 4 different tractor-trailer vehicles with 254 tests under various conditions. Large scale test condition variations include initial position and orientation, light illumination, indoor/outdoor scenario, and fiducial marker damage. Overall success rate of 95% shows that the proposed system is robust to environment variation and different kinds of trailers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Light trailers

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DGSSL:A novel Deep Graph Semi-Supervised Learning framework for financial credit evaluation

DGSSL:A novel Deep Graph Semi-Supervised Learning framework ...

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第32届中国过程控制会议(CPCC2021)

作者： Yun Qiu Jing Wu Shaoyuan Li Xiaoyu Xiang Qian Yu Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Taizhou Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd

Label scarcity is a prevailing problem in financial data which restricts model performance in financial credit evaluation *** this paper,a Deep Graph Semi-Supervised Learning(DGSSL) framework is proposed to evaluate credit of financial individuals accurately and efficiently under label scarcity *** DGSSL,financial features are treated as graph structral data to explore the potential relationship information which is ignored by most of traditional *** this way,deep graph embedding and biased random walk are employed to reconstruct and extract features to exploit latent structral and distribution information of the ***,Graph Convolutional Network(GCN) is adopted to train Semi-Supervised Learning(SSL) model,experiments on credit card dataset prove that our proposed DGSSL framework performs better than several classic and advanced methods,especially in the case of extremely scarce *** addition,DGSSL owns faster training process than traditional GCN.

关键词： Financial Credit Evaluation Semi-Supervised Learning Deep Graph Embedding Graph Convolutional Network

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An Integrated Model Combining CNN-GRU with ARIMA for pressure prediction of water supply network

An Integrated Model Combining CNN-GRU with ARIMA for pressur...

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第40届中国控制会议

作者： Bo Wang Miaoshun Bai Jingcheng Wang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Municipal Engineering Design Institute (Group) Co. Ltd

In the problem of time series prediction, the accuracy has always been an enduring research hotspot. In this paper,we proposed an integrated model named CNN-GRU-ARIMA to improve the accuracy of pressure prediction for water supply network. The proposed model is mainly comprised of the two parts, namely ARIMA and CNN-GRU. The ARIMA is usually used for the univariate prediction, and the CNN-GRU represents the multivariate prediction model. Also, several metrics including MAE, RRSE, RMSE are exploited to facilitate quantitative analysis of the accuracy differences between different models mentioned. The experiments show that our proposed scheme adopted in integrated model can achieve the better performance than some state-of-the-art approaches.

关键词： Time series prediction CNN GRU ARIMA

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