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Point Cloud Completion by Larger Receptive Fields for Intelligent Vehicles

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Point Cloud Completion by Larger Receptive Fields for Intell...

International Conference on Intelligent Transportation

作者： Yunqing Zhang Xiaotong Zhang Kunhua Liu Yisheng Lv Wei He Long Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China School of Mechanical and Automotive Engineering Qingdao University of Technology Qingdao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Waytous Inc. Beijing China

Point cloud completion is a critical task for intelligent vehicles in the Intelligent Transportation Systems (ITS). Due to the disordered nature of point clouds, current point-based point completion methods utilize max pooling operations and Convolutional Neural Networks (CNNs) with a convolution kernel size of 1 to extract point features. However, a network with a kernel size of 1 has a small receptive field and can only extract one point feature at a time; as a result, such a network struggles to precisely extract highly abstracted features. To address this issue, we propose a point cloud completion network with larger receptive fields, termed PLRF-Net, which is composed of two parts: a reconstruction network and a completion network. The reconstruction network focuses on repairing the structures of partial points and includes an offset-attention embedding module, a rearrangement module, several convolution layers with varying kernel sizes, a pooling and repeating layer, and transposed convolution layers with different kernel sizes. Meanwhile, the completion network strives to improve the precision of the completed point cloud and consists of several convolution layers, along with a downsampling and upsampling module. Experimental results indicate that our approach achieves superior quantitative and qualitative outcomes on both the Multiview Partial Point Cloud (MVP) and KITTI datasets.

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Sampled-data funnel control and its use for safe continual learning

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arXiv 2023年

作者： Lanza, Lukas Dennstädt, Dario Worthmann, Karl Schmitz, Philipp Şen, Gökçen Devlet Trenn, Stephan Schaller, Manuel Technische Universität Ilmenau Institute of Mathematics Optimization-based Control Group Ilmenau Germany Universität Paderborn Institut für Mathematik Paderborn Germany University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen Netherlands Istanbul Technical University Department of Control and Automation Engineering Istanbul Turkey Brandenburgische Technische Universität Cottbus-Senftenberg Fachgebiet Regelungssysteme und Netzleittechnik Cottbus Germany

We propose a novel sampled-data output-feedback controller for nonlinear systems of arbitrary relative degree that ensures reference tracking within prescribed error bounds. We provide explicit bounds on the maximum input signal and the required uniform sampling time. A key strength of this approach is its capability to serve as a safety filter for various learning-based controller designs, enabling the use of learning techniques in safety-critical applications. We illustrate its versatility by integrating it with two different controllers: a reinforcement learning controller and a non-parametric predictive controller based on Willems et al.’s fundamental lemma. Numerical simulations illustrate effectiveness of the combined controller design. © 2023, CC BY-NC-ND.

关键词： Predictive control systems

Design and Implementation of a Two-Limbed 3T1R Haptic Device

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Design and Implementation of a Two-Limbed 3T1R Haptic Device

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Long Kang Yang Yang Byung-Ju Yi 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 Nanjing China Xi'an Modern Control Technology Research Institute Xi'an China School of Automation Nanjing University of Information Science and Technology Nanjing China Department of the Electronic Systems Engineering Hanyang University Ansan South Korea

This paper presents a haptic device with a simple architecture of only two limbs that can provide translational motion in three degrees of freedom (DOF) and one-DOF rotational motion. Actuation redundancy eliminates all forward-kinematic singularities and improves the motion-force transmission property. Thanks to the special structure of the kinematic chains, all actuators are close to the base and full gravity compensation is achieved passively by using springs. Force producibility analysis shows that this haptic device is able to produce long-term continuous force feedback of 15–30 N in each direction. By developing a prototype of the haptic device and a virtual three-dimensional simulator, a preliminary performance evaluation of the haptic device was conducted. In addition, a torque distribution algorithm considering a relaxed form of actuator-torque saturation was experimentally evaluated, and a comparison with other algorithms reveals that this algorithm offers several advantages.

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Designing a Secure Smart Remote Patient Monitoring and Warning System using Big Data and Internet of Things Ecosystems

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Designing a Secure Smart Remote Patient Monitoring and Warni...

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： S.P. Ramesh Ali Hadi Abdulwahid Afsana Anjum N. Venkatesh Rajesh Singh M. Kalyan Chakravarthi Department of Computer Science and Engineering AMET University Kanathur Tamil Nadu Department of Control &Automation Southern Technical University Engineering Technical College Basra Iraq Department of Information Technology & Security Jazan University Jazan KSA School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21 st century such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research.

关键词： Industries Patient monitoring Ecosystems Sociology Medical services Big Data Alarm systems

Prediction of Drug-Disease Associations Based on Long Short-Term Memory Network and Gaussian Interaction Profile Kernel 15th

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Prediction of Drug-Disease Associations Based on Long Short-...

15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Jiang, Han-Jing Wang, Yan-Bin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computer Science and Technology Zhejiang University Hangzhou315000 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Drug-disease association depicts the accessibility landscape of the association at the molecular level, thereby revealing the intermolecular reaction in the process of drug reposition. However, the scarcity of drug-disease association data often complicates the drug reposition. In this work, we present a method for predicting drug-disease association, which is called drug-disease association prediction via Gaussian interaction profile kernel and long short-term memory network (G-LSTM). G-LSTM combines a recurrent network framework with a Gaussian interaction profile kernel to learn underlying features that accurately characterize drug-disease-association data. We have verified G-LSTM models on the two benchmark datasets Fdataset and Cdataset, respectively. The experimental results show that the predictive performance was significantly improved, with AUC of 0.9527 and 0.9705, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

Grasp-Anything: Large-scale Grasp Dataset from Foundation Models

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arXiv 2023年

作者： Vuong, An Dinh Vu, Minh Nhat Le, Hieu Huang, Baoru Huynh, Binh T.T. Vo, Thieu Kugi, Andreas Nguyen, Anh FPT Software AI Center Viet Nam Automation & Control Institute TU Wien Vienna Austria AIT Austrian Institute of Technology Vienna Austria Imperial College London United Kingdom Hanoi University of Science and Technology Viet Nam Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science University of Liverpool United Kingdom

Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains. In this paper, we leverage foundation models to tackle grasp detection, a persistent challenge in robotics with broad industrial applications. Despite numerous grasp datasets, their object diversity remains limited compared to real-world figures. Fortunately, foundation models possess an extensive repository of real-world knowledge, including objects we encounter in our daily lives. As a consequence, a promising solution to the limited representation in previous grasp datasets is to harness the universal knowledge embedded in these foundation models. We present Grasp-Anything, a new large-scale grasp dataset synthesized from foundation models to implement this solution. Grasp-Anything excels in diversity and magnitude, boasting 1M samples with text descriptions and more than 3M objects, surpassing prior datasets. Empirically, we show that Grasp-Anything successfully facilitates zero-shot grasp detection on vision-based tasks and real-world robotic experiments. Our dataset and code are available at https://***. © 2023, CC BY-NC-SA.

关键词： Robotics

Distributed Constrained Online Nonconvex Optimization with Compressed Communication

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

A survey on membership inference attacks and defenses in machine learning

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Journal of Information and Intelligence 2024年第5期2卷 404-454页

作者： Jun Niu Peng Liu Xiaoyan Zhu Kuo Shen Yuecong Wang Haotian Chi Yulong Shen Xiaohong Jiang Jianfeng Ma Yuqing Zhang School of Computer Science and Technology Xidian UniversityXi'an 710071China Information Sciences and Technology Pennsylvania State UniversityPA 16802United States School of Telecommunications Engineering Xidian UniversityXian 710071China School of Hangzhou Institute of Technology Xidian UniversityHangzhou 311231China School of Automation and Software Engineering Shanxi UniversityTaiyuan 030006China School of Systems Information Science Future University of HakodateHakodate 041-8655Japan School of Computer&Control Engineering University of Chinese Academy of SciencesBeijing 101408China School of Cyber Engineering Xidian UniversityXian 710071China

Membership inference(MI)attacks mainly aim to infer whether a data record was used to train a target model or *** to the serious privacy risks,MI attacks have been attracting a tremendous amount of attention in the research *** existing work conducted-to our best knowledge the first dedicated survey study in this specific area:The survey provides a comprehensive review of the literature during the period of 2017~2021(e.g.,over 100 papers).However,due to the tremendous amount of progress(i.e.,176 papers)made in this area since 2021,the survey conducted by the one existing work has unfortunately already become very limited in the following two aspects:(1)Although the entire literature from 2017~2021 covers 18 ways to categorize(all the proposed)MI attacks,the literature during the period of 2017~2021,which was reviewed in the one existing work,only covered 5 ways to categorize MI *** 13 ways missing,the survey conducted by the one existing work only covers 27%of the landscape(in terms of how to categorize MI attacks)if a retrospective view is taken.(2)Since the literature during the period of 2017~2021 only covers 27%of the landscape(in terms of how to categorize),the number of new insights(i.e.,why an MI attack could succeed)behind all the proposed MI attacks has been significantly increasing since year *** a result,although none of the previous work has made the insights as a main focus of their studies,we found that the various insights leveraged in the literature can be broken down into 10 *** making the insights as a main focus,a survey study could fail to help researchers gain adequate intellectual depth in this area of *** this work,we conduct a systematic study to address these *** particular,in order to address the first limitation,we make the 13 newly emerged ways to categorize MI attacks as a main focus on the *** order to address the second limitation,we provide-to our best knowledge-the first r

关键词： Machine learning Privacy and security Membership inference attacks Defensive techniques

Evaluating contextualized language models for Hungarian

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arXiv 2021年

作者： Ács, Judit Lévai, Dániel Nemeskey, Dávid Márk Kornai, András Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Hungary Alfréd Rényi Institute of Mathematics Hungary

We present an extended comparison of contextualized language models for Hungarian. We compare huBERT, a Hungarian model against 4 multilingual models including the multilingual BERT model. We evaluate these models through three tasks, morphological probing, POS tagging and NER. We find that huBERT works better than the other models, often by a large margin, particularly near the global optimum (typically at the middle layers). We also find that huBERT tends to generate fewer subwords for one word and that using the last subword for token-level tasks is generally a better choice than using the first one. Copyright © 2021, The Authors. All rights reserved.

关键词： Computational linguistics