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Chinese Intelligent Systems Conference, CISC 2017

作者： Shang, Baolin Tan, Jiaju Guo, Xuemei Wang, Guoli Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education Guangzhou China Tianjin China

ISBN: (纸本)9789811064982

A common difficulty in device-free localization (DFL) using received signal strength (RSS) is that the uninformative and redundant RSS data in wireless sensor network (WSN) usually degrades the localization performance. This paper develops a dual radio tomographic imaging (RTI) to keep data efficiency in sparse signal recovery as well as eliminating the redundant measurements. In addition to Tikhonov regularization, we also utilize a robust loss function in sparse Bayesian learning for RTI to handle with the outlier data. Moreover, by taking advantage of proposed spatial continuous filter in position estimation, our dual RTI achieves lower localization errors in DFL system. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Quantum speedup of twin support vector machines

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Science China(Information Sciences) 2020年第8期63卷 272-274页

作者： Zekun YE Lvzhou LI Haozhen SITU Yuyi WANG School of Data and Computer Science Sun Yat-Sen University Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-Sen University) College of Mathematics and Informatics South China Agricultural University

Dear editor,machine learning enables agents or computers to learn from data and improves the performance of specific tasks without requiring explicit programming. As the amount of available data continues to grow, existing machine learning algorithms cannot satisfy the efficiency requirements of various practical applications, and several methods have been

关键词： quantum computing quantum machine learning quantum algorithms twin support vector machines exponential speedup

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A Reliable Road Segmentation and Edge Extraction for Sparse 3D Lidar Data

A Reliable Road Segmentation and Edge Extraction for Sparse ...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Gu, Jianfeng Wang, Yuehui Chen, Long Zhao, Zhihao Zhe, Xuanyuan Huang, Kai Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University China

ISBN: (纸本)9781538644522

Precise segmentation of road areas using cheap Lidar is a tough and critical task due to data sparsity problem. With sparse point clouds, reliable perception of environment is difficult due to the lack of available information and loss of object features. This paper presents a new approach to use sparse 3D Lidar data for road segmentation by fusing multiple frames of point cloud. With registration of multiple frames into a same coordinate system, reliable data can be provided for later ground segmentation and edge extraction. The accuracies of extensive experiments on three kinds of roads demonstrate that the proposed approach obtains high precision and reliability. © 2018 IEEE.

关键词： Optical radar

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DeepFake Videos Detection Based on Texture Features

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Computers, Materials & Continua 2021年第7期68卷 1375-1388页

作者： Bozhi Xu Jiarui Liu Jifan Liang Wei Lu Yue Zhang School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhou510006China Department of Computer Science University of Massachusetts LowellLowell01854MAUSA

In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake ***,a deep learning based forgery technology,can tamper with the face easily and generate fake videos that are difficult to be distinguished by human *** spread of face manipulation videos is very easy to bring fake ***,it is important to develop effective detection methods to verify the authenticity of the *** to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process,the texture details of the fake face are ***,in this paper,a new method is proposed to detect DeepFake ***,the texture features are constructed,which are based on the gradient domain,standard deviation,gray level co-occurrence matrix and wavelet transform of the face ***,the features are processed by the feature selection method to form a discriminant feature vector,which is finally employed to SVM for classification at the frame *** experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance,proving the effectiveness of the proposed method for DeepFake videos detection.

关键词： DeepFake video tampering tampering detection texture feature

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CROSS-SCENE PERSON TRAJECTORY ANOMALY DETECTION BASED ON RE-IDENTIFICATION

CROSS-SCENE PERSON TRAJECTORY ANOMALY DETECTION BASED ON RE-...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yuanxun Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

In this work, we consider the cross-scene person trajectory anomaly detection problem, which detects the anomalous trajectories across multiple nonoverlapping scenes. This problem is highly significant for public security, but it is still underexplored. Since the trajectory is not continuous across nonoverlapping camera views, we take use of person re-identification (re-ID) to associate the same pedestrian in different scenes while mitigating its inaccuracy by a directional probabilistic graph. To better distinguishing normal samples from anomalies, We formulate a maximized margin graph autoencoder (MMGAE) model, and the reconstruction error of the MMGAE is regarded as an anomaly indicator for the sample. To verify the effectiveness of our approach, we collected and labeled a new dataset. we also explore the impact of the re-ID performance on the anomaly detection problem and the effect of an inaccurately constructed graph on the MMGAE. © 2021 IEEE

关键词： Trajectories

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Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection 8

Real-to-Sim Grasp: Rethinking the Gap between Simulation and...

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8th Conference on Robot Learning, CoRL 2024

作者： Cai, Jia-Feng Chen, Zibo Wu, Xiao-Ming Jiang, Jian-Jian Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way1. However, this way explicitly or implicitly forces the simulated data to adapt to the noisy real data when training grasp detectors, where the positional drift and structural distortion within the camera noise will harm the grasp learning. In this work, we propose a Real-to-Sim framework for 6-DoF Grasp detection, named R2SGrasp, with the key insight of bridging this gap in a real-to-sim way2, which directly bypasses the camera noise in grasp detector training through an inference-time real-to-sim adaption. To achieve this real-to-sim adaptation, our R2SGrasp designs the Real-to-Sim Data Repairer (R2SRepairer) to mitigate the camera noise of real depth maps in data-level, and the Real-to-Sim Feature Enhancer (R2SEnhancer) to enhance real features with precise simulated geometric primitives in feature-level. To endow our framework with the generalization ability, we construct a large-scale simulated dataset cost-efficiently to train our grasp detector, which includes 64,000 RGB-D images with 14.4 million grasp annotations. Sufficient experiments show that R2SGrasp is powerful and our real-to-sim perspective is effective. The real-world experiments further show great generalization ability of R2SGrasp. Project page is available on https://***/R2SGrasp. © 2024 Proceedings of machine Learning Research.

关键词： Image annotation

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A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1778-1791页

作者： Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni IEEE the School of Computer Science and Engineering&Key Laboratory of Machine Intelligence and Advanced Computing Ministry of EducationSun Yat-sen UniversityGuangzhou 510006China the School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China the State Key Laboratory of Robotics and Systems Harbin Institute of Technology(HIT)Harbin 150080China

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks *** a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated ***,robust model-free control of robotic arms in the presence of noise interference remains a problem worth *** this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant ***,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic *** finite-time convergence and robustness of the proposed control scheme are proven by theoretical ***,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

关键词： Dual zeroing neural networks(ZNN) finite-time convergence model-free robot control robustness analysis

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Semi-supervised dictionary learning with label propagation for image classification

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Computational Visual Media 2017年第1期3卷 83-94页

作者： Lin Chen Meng Yang College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina Key Laboratory of Machine Intelligence and Advanced Computing(Sun Yat-sen University) Ministry of EducationChina

Sparse coding and supervised dictionary learning have rapidly developed in recent years,and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification,which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semisupervised dictionary learning method using label propagation(SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors *** experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

关键词： semi-supervised learning dictionary learning label propagation image classification

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DBDNet: Learning bi-directional dynamics for early action prediction 28

DBDNet: Learning bi-directional dynamics for early action pr...

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28th International Joint Conference on Artificial intelligence, IJCAI 2019

作者： Pang, Guoliang Wang, Xionghui Hu, Jian-Fang Zhang, Qing Zheng, Wei-Shi Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9780999241141

Predicting future actions from observed partial videos is very challenging as the missing future is uncertain and sometimes has multiple possibilities. To obtain a reliable future estimation, a novel encoder-decoder architecture is proposed for integrating the tasks of synthesizing future motions from observed videos and reconstructing observed motions from synthesized future motions in an unified framework, which can capture the bi-directional dynamics depicted in partial videos along the temporal (past-to-future) direction and reverse chronological (future-back-to-past) direction. We then employ a bi-directional long short-term memory (Bi-LSTM) architecture to exploit the learned bi-directional dynamics for predicting early actions. Our experiments on two benchmark action datasets show that learning bi-directional dynamics benefits the early action prediction and our system clearly outperforms the state-of-the-art methods. © 2019 International Joint Conferences on Artificial intelligence. All rights reserved.

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Discrete-Time Control and Simulation of Ship Course Tracking Using ZD Method and ZFD Formula 4NgSFD 3

Discrete-Time Control and Simulation of Ship Course Tracking...

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2017 IEEE 第3届信息技术与机电一体化工程国际学术会议(ITOEC 2017)

作者： Yunong Zhang Huinan Xiao Jinjin Wang Jian Li Pei Chen School of Information Science and Technology Sun Yat-sen University(SYSU) Key Laboratory of Autonomous Systems and Networked Control Ministry of Education SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

ISBN: (纸本)9781509053643;9781509053636

This paper focuses on solving the ship course tracking problem by using Zhang dynamics(ZD) method and a new Zhang finite difference(ZFD) *** ZD method,which is a powerful class of dynamics to

关键词： Marine vehicles Trajectory Electronic mail Process control Convergence Mathematical model

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