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Underwater robot local dry welding system

china Welding 2019年第4期28卷 22-27页

作者： Wang Zhenmin Xie Fangxiang Feng Yunliang Zhang Qin School of Mechanical and Automotive Engineering South China University of TechnologyGuangzhou 510640China School of Computer Science&Engineering South China University of TechnologyGuangzhou 510640China

To satisfy the demand for good quality underwater welding and maintenance of nuclear power stations,a set of local dry automatic welding systems has been *** systems were based on an underwater robot that consisted of a special high-power underwater welding power supply,diving wire feeder,mini drain cap,welding robot,and special underwater welding *** a digital signal controller microprocessor as its core and combined with a dual inverter topology,the welding power supply was characterized by full-digital construction and multi-waveform flexible output.A compact diving wire feeding device was designed,based on the armature voltage negative feedback and high-frequency chopping pulse width *** device yielded a high-efficiency seal of the driving motor with the help of dynamic and static sealing *** overcome the difficulty of local protection and deslagging in the welding area,a mini drain cap(with a duplexgas structure)based on the principle of the convergent nozzle was *** practical tests and the underwater welding experiments revealed that the underwater robotic local dry welding system is quite *** is,the system could strike the arc stably and reliably in the shallow water environment,and formed beautiful welding seams.

关键词： local dry underwater robotic welding underwater welding power supply mini drain cap underwater wire feeder

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A binary i-ching divination evolutionary algorithm for feature selection

A binary i-ching divination evolutionary algorithm for featu...

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Chen, Bianna Zhang, Tong Jia, Xue Jin, Jianxiu Philip Chen, C.L. Xu, Xiangmin South China University of Technology Sch. of Electronics and Inform. Guangzhou China South China University of Technology Sch. of Computer Science Engineering Guangzhou China

ISBN: (纸本)9781728159287

Feature selection is used to extract the most essential features from the data without degrading the performance of an algorithm, especially a classification algorithm. Various evolutionary algorithms (EAs) combined with classification algorithms are commonly used for feature selection. This paper suggests an innovative feature selection algorithm based on I-Ching Divination Evolutionary Algorithm, called binary IDEA (BIDEA). The main idea is to use a series of hexagrams encoded as binary vectors, which is called the hexagram state to represent the solutions of selected features. After three flexible operations, intrication, turnover and mutual, the transformed hexagram state can be obtained as candidate solutions. Then the optimized hexagram state can be searched to form the new state in the next iteration by evaluating candidate solutions. Experiments checked out with standard datasets reveal that the proposed BIDEA performs better in terms of classification accuracy, precision, recall and feature reduction than the competing feature selection methods. © 2019 IEEE.

关键词： Evolutionary algorithms

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Concurrent optimization of structural topology and infill properties with a CBF-based level set method

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Frontiers of Mechanical engineering 2019年第2期14卷 171-189页

作者： Long JIANG Yang GUO Shikui CHEN Peng WEI Na LEI Xianfeng David GU Department of Mechanical Engineering State University of New York at Stony BrookStony BrookNY 11794USA Department of Computer Science State University of New York at Stony BrookStony BrookNY 11794USA State Key Laboratory of Subtropical Building Science School of Civil Engineering and TransportationSouth China University of TechnologyGuangzhou 510641China DUT-RU International School of Information Science&Engineering Dalian University of TechnologyDalian 116620China

In this paper,a parametric level-set-based topology optimization framework is proposed to concurrently optimize the structural topology at the macroscale and the effective infill properties at the micro/meso *** concurrent optimization is achieved by a computational framework combining a new parametric level set approach with mathematical *** the proposed framework,both the structural boundary evolution and the effective infill property optimization can be driven by mathematical programming,which is more advantageous compared with the conventional partial differential equatiodriven level set ***,the proposed approach will be more efficient in handling nonlinear problems with multiple *** of using radial basis functions(RBF),in this paper,we propose to construct a new type of cardinal basis functions(CBF)for the level set function *** proposed CBF parameterization ensures an explicit impose of the lower and upper bounds of the design *** overcomes the intrinsic disadvantage of the conventional RBF-based parametric level set method,where the lower and upper bounds of the design variables oftentimes have to be set by trial and error;A variational distance regularization method is utilized in this research to regularize the level set function to be a desired distanceregularized *** the distance information embedded in the level set model,the wrapping boundary layer and the interior infill region can be naturally *** isotropic infill achieved via the mesoscale topology optimization is conformally fit into the wrapping boundary layer using the shape-preserving conformal mapping method,which leads to a hierarchical physical structure with optimized overall topology and effective infill *** proposed method is expected to provide a timely solution to the increasing demand for multiscale and multifunctional structure design.

关键词： concurrent topology optimization parametric level set method cardinal basis function shell-infill structure design confonnal mapping

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Hijacking Tracker: A Powerful Adversarial Attack on Visual Tracking

Hijacking Tracker: A Powerful Adversarial Attack on Visual T...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xiyu Yan Xuesong Chen Yong Jiang Shu-Tao Xia Yong Zhao Feng Zheng Dept. of Computer Science and Technology Tsinghua University China Shenzhen Graduate School of Peking University Sch. of Electronic and Computer Engineering China Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Visual object tracking has made important breakthroughs with the assistance of deep learning models. Unfortunately, recent research has clearly proved that deep learning models are vulnerable to malicious adversarial attacks, which mislead the models making wrong decisions by perturbing the input image. The threat to the models alerts us to pay attention to the model security of deep learning- based tracking algorithms. Therefore, we study the adversarial attacks against advanced trackers based on deep learning to better identify the vulnerability of tracking algorithms. In this paper, we propose to add slight adversarial perturbations to the input image by an inconspicuous but powerful attack strategy-hijacking algorithm. Specifically, the hijacking strategy misleads trackers in two aspects: one is shape hijacking that changes the shape of the model output;the other is position hijacking that gradually pushes the output to any position in the image frame. Besides, we further propose an adaptive optimization approach to integrate two hijacking mechanisms efficiently. Eventually, the hijacking algorithm results in fooling the tracker to track the wrong target gradually. The experimental results demonstrate the powerful attack ability of our method-quickly hijacking state-of-the-art trackers and reducing the accuracy of these models by more than 90% on OTB2015.

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Sentiment Analysis Using Autoregressive Language Modeling and Broad Learning System

Sentiment Analysis Using Autoregressive Language Modeling an...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Xin-rong Gong Jian-xiu Jin Tong Zhang Sch. of Electronics and Information South China University of Technology Sch. of Computer Science and Engineering South China University of Technology

ISBN: (纸本)9781728118680

In the era of big data, mining the emotional tendency of opinions through natural language processing technology is meaningful for the timely understanding of human behavior data. Nowadays, Generalized Autoregressive Pre-training Language Modeling (XLNet) can not only capture bidirectional contextual knowledge but also learn the word dependency. However, its sentence-level representation didn't take broad features into account. In this paper, we design a novel architecture, called Broad Autoregressive Language Model (BroXLNet), to automatically process sentiment analysis task. BroXLNet integrates the advantage of generalized autoregressive language modeling and broad learning system, which has the ability of extracting deep contextual features and randomly searching high-level contextual representation in broad spaces. We evaluated our algorithm on binary Stanford Sentiment Treebank dataset. Compared with the state-of-the-art methods, e.g., BiLSTM, ELMo, OpenAI GPT, BERT and XLNet, BroXLNet achieved the best result of 94.0% in sentiment analysis task of binary Stanford Sentiment Treebank. The result demonstrated the excellent classifying ability of BroXLNet in sentiment analysis.

关键词： Sentiment analysis broad learning system autoregressive language modeling emotion classification transfer learning Sentiment analysis modelling languages Transfer (Psychology) big data Bit error rate test

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A Deep Neural Network for Anomaly Detection and Forecasting for Multivariate Time Series in Smart City

A Deep Neural Network for Anomaly Detection and Forecasting ...

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Junjie He Min Dong Sheng Bi Weijie Zhao Xutao Liao School of Computer Science&Engineering South China University of Technology GuangZhou

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

In the progress of constructing a smart city, large amounts of univariate and multivariate times series data is generated by complex real-world systems and internet of things(IoT) with sensors such as wearable devices. Abnormal status in univariate and multivariate time series are necessary to be identified by abnormal detection methods. Time series forecasting in univariate and multivariate which refers to detect the different patterns in the input time series is significant for managers. However, building a system for anomaly detection and forecasting is challenging. On the one hand, the temporal dependency is required to capture in time series data, on the other hand, inter-correlations in different pairs of time series data are so important for the system that the system needs to encode inter-correlations. In this work, we propose an Attention based Convolutional Recurrent Encoder-Decoder (ACRED), which is effective to address anomaly detection and forecasting problems in time series. The studies based on a Secure Water Treatment tested (SWaT) dataset suggest that ACRED can outperform popular deep recurrent neural network methods.

关键词： Iron IP networks Conferences Automation Control systems Intelligent systems Neural networks

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RoverOS: Linking ROS with WebSocket for moblie robot

RoverOS: Linking ROS with WebSocket for moblie robot

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IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Hansi Hong Zifan Wen Sheng Bi Yue Zhang Wenxing Yang School of Computer Science&Engineering South China University of Technology Guangzhou

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

With the rise of the Internet of Things(IoT), human-robot interaction between robots and humans has become one of the hotspots in the field of robotics. Thus, we present RoverOS, an integration WebSocket-based layer which provides bidirectional and stable connection among ROS system and Web client. RoverOS is a simple and uniform application implemented in Java, with which one allows users to control robot moving, get video data and navigate Turtlebot robot in a pre-existing map by the Web client. Basically it acts as an agent between clients and ROS system, forwarding messages via ros topics. RoverOS performance was tested on Turtlebot2 and Google browser. And RoverOS was shown to be efficient and stable.

关键词： Servers Navigation Robot kinematics Java Monitoring Real-time systems

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Learning to Search Efficient DenseNet with Layer-wise Pruning

Learning to Search Efficient DenseNet with Layer-wise Prunin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xuanyang Zhang Hao Liu Zhanxing Zhu Zenglin Xu SMILE Lab Sch. Computer Science & Engineering University of Electronic Science & Technology of China Chengdu Sichuan China Electronic Engineering and Computer Science University of California Berkeley California USA School of Mathematical Science Peking University Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong China Center for Artificial Intelligence Peng Cheng Lab Shenzhen Guangdong China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Deep neural networks have achieved out-standing performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to the dense connections of the internal structure, which leads to high computational costs in inference with such dense networks. To address this issue, we design a reinforcement learning framework to search for efficient DenseNet architectures with layer-wise pruning (LWP) for different tasks, while retaining the original advantages of DenseNet, such as feature reuse, short paths, etc. In this framework, an agent evaluates the importance of each connection between any two block layers, and prunes the redundant connections. In addition, a novel reward-shaping trick is introduced to make DenseNet reach a better trade-off between accuracy and float point operations (FLOPs). Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives.

关键词： Training Neural networks computer architecture Task analysis Image coding Learning (artificial intelligence) Load modeling

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A Binary I-Ching Divination Evolutionary Algorithm for Feature Selection

A Binary I-Ching Divination Evolutionary Algorithm for Featu...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bianna Chen Tong Zhang Xue Jia Jianxiu Jin C. L. Philip Chen Xiangmin Xu Sch. of Electronics and Inform. South China University of Technology Guangzhou China Sch. of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

关键词： Feature extraction Evolutionary computation Optimization Genetic algorithms Security Pattern analysis Cybernetics

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Integrating Dense LiDAR-Camera Road Detection Maps by a Multi-Modal CRF Model

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IEEE Transactions on Vehicular technology 2019年第12期68卷 11635-11645页

作者： Gu, Shuo Zhang, Yigong Tang, Jinhui Yang, Jian Alvarez, Jose M. Kong, Hui PCA Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Sch. of Comp. Sci. and Engineering Nanjing Univ. of Sci. and Technol. Nanjing 210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection method via a LiDAR-camera fusion strategy to exploit both the range and color information. The whole system consists of three parts. In the LiDAR based part, we transform the discrete 3D LiDAR point clouds to continuous 2D LiDAR range images and propose a distance-aware height-difference based scanning approach to get the road estimations quickly. In the camera based part, we apply a light-weight transfer learning based road segmentation network. In the LiDAR-camera fusion part, we transform the detection results from LiDAR and camera to dense and binary ones to solve the data imbalance problem and fuse them in a multi-modal conditional random field (MM-CRF) framework. Experiments show that the proposed MM-CRF fusion method can operate in real-time and achieve competitive performance compared with the state-of-the-art road detection algorithms on the KITTI-Road benchmark. © 1967-2012 IEEE.

关键词： KITTI LiDAR-camera fusion road detection

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