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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Das, Polash Kumar Hu, Bin Liu, Chang Cui, Kaixin Ranjan, Prabhat Xiong, Gang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Cloud Computing Center Chinese Academy of Science Dongguan China

ISBN: (纸本)9781728128535

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature. © 2019 IEEE.

关键词： Local binary pattern

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The sub-Gramian method as a tool for the automotive suspension tuning 11

The sub-Gramian method as a tool for the automotive suspensi...

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11th International Conference "Management of Large-Scale System Development", MLSD 2018

作者： Kataev, Dmitry E. Kutyakov, Evgeniy Y. Vershinin, Yuri V.A. Trapeznikov Institute of Control Sciences RAS Moscow Russia Engineering Environment and Computing Coventry University Coventry United Kingdom

The sub-Gramian method allows individual numerical estimation of inner interactions in large-scale systems. However, it may give results that appear difficult to interpret. In this paper we apply the sub-Gramian metho... 详细信息

ISBN: (纸本)9781538649244

关键词： Differential equations

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Efficient dense frontier detection for 2D graph SLAM based on occupancy grid submaps

arXiv

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arXiv 2019年

作者： Oršulić, Juraj Miklić, Damjan Kovačić, Zdenko Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems University of Zagreb Croatia RoMb Technologies d.o.o. Zagreb Croatia

In autonomous robot exploration, the frontier is the border in the world map dividing the explored and unexplored space. The frontier plays an important role when deciding where in the environment the robots should go explore next. We consider a modular control system pipeline for autonomous exploration where a 2D graph SLAM algorithm based on occupancy grid submaps performs map building and localization, and frontier detection is one of key system components. We provide an overview of the state of the art in frontier detection and the relevant SLAM concepts and propose a fast specialized frontier detection method which is efficiently constrained to active submaps, yet robust to graph SLAM loop closures. Copyright © 2019, The Authors. All rights reserved.

关键词： Computer vision

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Observer-based predictive control for networked control systems via event-triggered strategy

Observer-based predictive control for networked control syst...

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2018 Australian and New Zealand control Conference, ANZCC 2018

作者： Yang, Rongni Zheng, Wei Xing School of Control Science and Engineering Shandong University Jinan Shandong250061 China School of Computing Engineering and Mathematics Western Sydney University SydneyNSW2751 Australia

ISBN: (纸本)9781538666173

In this paper, the observer-based predictive control problem for networked control systems (NCSs) subject to the time-delay is investigated based on the event-triggered strategy. Initially, the output-based event-triggered mechanism is introduced in order to reduce the transmission times effectively in the capacity-limited communication network. Then, the system states are reconstructed from the output measurements by utilizing a popular Luenberger observer. Next, the model-based networked predictive control (NPC) scheme is developed to actively compensate for the communication delay. Moreover, based on analysis of the augmented system, sufficient conditions that guarantee the asymptotic stability of the system are obtained. Finally, a simulation example is given to demonstrate the effectiveness in the reduction of network utilization and compensation for communication delay of the proposed controller design method in NCSs. © 2018 IEEE.

关键词： Networked control systems

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3D Face Reconstruction System From A Single Photo Based On Regression Neural Network

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IFAC-PapersOnLine 2020年第5期53卷 71-76页

作者： Yatai Ji Keqiang Li Huaiyu Wu Gang Xiong Zhen Shen Xiuqin Shang Bangwen Xi The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Automation Tsinghua University Beijing 100084 China University of Chinese Academy of Sciences Beijing 100049 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China

3D human face modeling is one of the most popular research directions in the field of computer stereo vision. On the one hand, the complex physiological structure of human face as well as various expressions and attitude changes bring great challenges to three-dimensional modeling, which makes the modeling method of human face have high research value and reference significance. On the other hand, 3D face reconstruction has a broad application prospect, and the application demand attracts more researchers to invest in it. Based on the 3DMM and 3DDFA method, this paper utilizes regression neural network to realize end-to-end face reconstruction. After that, we build a visualization program with the training model to realize face modeling based on a single face image of any pose or expression. It’s a 3D face reconstruction system which has functions such as face alignment, face rotation and so on.

关键词： face reconstruct 3DMM 3DDFA regression network

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LAENet: Light-weight asymmetric encoder-decoder network for semantic segmentation

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Journal of Physics: Conference Series 2021年第1期1966卷

作者： Liangyi Hong Shukai Duan Lidan Wang Yongbin Pan College of Electronic and Information Engineering Southwest University Chongqing China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing China Chongqing Brain Science Collaborative Innovation Center Chongqing China College of Artificial Intelligence Southwest University Chongqing China

Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task of real-time semantic segmentation. We employ an asymmetric convolution and group convolution structure combined with dilated convolution and dense connectivity to reduce computation cost and model size, which can guarantee adequate receptive field and enhance the model learning ability in encoder. On the other hand, feature pyramid networks (FPN) structure combine attention mechanism and ECRE block are utilized in the decoder to strike a balance between the network complexity and segmentation performance. Our approach achieves only have 0.84M parameters, and is able to reach 66 FPS in a single GTX 1080Ti GPU. Experiments on Cityscapes datasets demonstrate that superior performance of LAENet is better than the existing segmentation network, in terms of speed and accuracy trade-off without any post-processing.

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Robot Simulation and Reinforcement Learning Training Platform Based on Distributed Architecture 1

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4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018

作者： Jiang, Yun-Liang Huang, Zeng-Qiang Cao, Jun-Jie Liu, Yong Ma, Xinqiang Huang, Yi School of Computer Science Hangzhou Dianzi University Hangzhou310018 China School of Information Engineering Huzhou University Huzhou313000 China Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China Institute of Intelligent Computing and Visualization Based on Big Data Chongqing University of Arts and Sciences Chongqing402160 China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

In recent years, reinforcement learning, which enables robots to learn previously missing abilities, plays an increasingly important role in robotics, such as learning hard-to-code behaviors or optimizing problems without an accepted closed solution. The main problem of RL in robotics is that it is expensive and takes a long time to learn and operate. Another problem: advanced robot simulators like Gazebo are inefficient and time-consuming. In order to cope with these problems, a hybrid computing platform based on traditional robot simulation architecture and distributed architecture (hereinafter referred to as RDTP) is proposed in this paper, which helps to save cost, shorten time and speed up simulation and training. Additionally, the platform is optimized to a certain extent in terms of ease of use and compatibility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Gate-defined quantum dots in Ge/SiGe quantum wells as a platform for spin qubits 236

Gate-defined quantum dots in Ge/SiGe quantum wells as a plat...

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Symposium on Semiconductors, Dielectrics, and Metals for Nanoelectronics 17 - 236th ECS Meeting

作者： Hardy, W.J. Su, Y.-H. Chuang, Y. Maurer, L.N. Brickson, M. Baczewski, A. Li, J.-Y. Lu, T.-M. Luhman, D.R. Sandia National Laboratories AlbuquerqueNM87123 United States Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Center for Computing Research Sandia National Laboratories AlbuquerqueNM87123 United States Center for Quantum Information and Control University of New Mexico AlbuquerqueNM87131 United States

ISBN: (纸本)9781607688754

In the field of semiconductor quantum dot spin qubits, there is growing interest in leveraging the unique properties of hole-carrier systems and their intrinsically strong spin-orbit coupling to engineer novel qubits. Recent advances in semiconductor heterostructure growth have made available high quality, undoped Ge/SiGe quantum wells, consisting of a pure strained Ge layer flanked by Ge-rich SiGe layers above and below. These quantum wells feature heavy hole carriers and a cubic Rashba-type spin-orbit interaction. Here, we describe progress toward realizing spin qubits in this platform, including development of multi-metal-layer gated device architectures, device tuning protocols, and charge-sensing capabilities. Iterative improvement of a three-layer metal gate architecture has significantly enhanced device performance over that achieved using an earlier single-layer gate design. We discuss ongoing, simulation-informed work to fine-tune the device geometry, as well as efforts toward a single-spin qubit demonstration. ©The Electrochemical Society

关键词： Semiconductor quantum dots

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Multi-objective Optimal Path Planning for Mobile Robots using State Space Cell Mapping

Multi-objective Optimal Path Planning for Mobile Robots usin...

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IEEE Colombian Conference on Automatic control (CCAC)

作者： E. Grisales-Ramírez M.F. Jaramillo-Morales G. Osorio J.B. Gómez-Mendoza Perception and Intelligent Control (PCI) National University of Colombia Faculty of Engineering and Architecture Manizales Colombia Soft and Hard Applied Computing (SHAC) National University of Colombia Faculty of Engineering and Architecture Manizales Colombia

Cell mapping is a promising approach in multiple objective optimization problems. By discretizing the open-loop dynamical system in both the state space and time, it is possible to reduce the mathematical complexity of the problem while introducing constraints in practical mobile robots. Here, we propose to perform the discretization of the controlled system (closed-loop). This can be done introducing an additional constraint in the model that can be interpreted as a zero-order hold (ZOH) on the control. Because of the structure of the optimal problem, it is necessary to release a degree of freedom. This is achieved by allowing nonuniform discretization of time. We study the problem of a particle on a bidimensional configuration space with obstacles, optimizing time, distance and control energy. We also present some numerical results in a case of study with a three wheeled omnidirectional mobile robot in a constrained C-space.

关键词： Mobile robots Trajectory Aerospace electronics Kinematics Wheels

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FSDA-DG: Improving cross-domain generalizability of medical image segmentation with few source domain annotations

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Medical Image Analysis 2025年 105卷

作者： Zanting Ye Ke Wang Wenbing Lv Qianjin Feng Lijun Lu School of Biomedical Engineering Southern Medical University Guangzhou Guangdong 510515 China College of Information Science and Technology Zhejiang Shuren University Hangzhou Zhejiang 310015 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou Zhejiang 310058 China School of Information and Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming Yunnan 650091 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou Guangdong 510515 China Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou Guangdong 510515 China Pazhou Lab Guangzhou Guangdong 510320 China

Deep learning-based medical image segmentation faces significant challenges arising from limited labeled data and domain shifts. While prior approaches have primarily addressed these issues independently, their simultaneous occurrence is common in medical imaging. A method that generalizes to unseen domains using only minimal annotations offers significant practical value due to reduced data annotation and development costs. In pursuit of this goal, we propose FSDA-DG, a novel solution to improve cross-domain generalizability of medical image segmentation with few single-source domain annotations. Specifically, our approach introduces semantics-guided semi-supervised data augmentation. This method divides images into global broad regions and semantics-guided local regions, and applies distinct augmentation strategies to enrich data distribution. Within this framework, both labeled and unlabeled data are transformed into extensive domain knowledge while preserving domain-invariant semantic information. Additionally, FSDA-DG employs a multi-decoder U-Net pipeline semi-supervised learning (SSL) network to improve domain-invariant representation learning through consistent prior assumption across multiple perturbations. By integrating data-level and model-level designs, FSDA-DG achieves superior performance compared to state-of-the-art methods in two challenging single domain generalization (SDG) tasks with limited annotations. The code is publicly available at https://***/yezanting/FSDA-DG .

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