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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Song, Yulin Yan, Han Sun, Chenxi Huang, Jianwei The Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798350366945

Due to privacy and security concerns surrounding actual power system structure data, many studies have focused on generating simulated power grids and providing corresponding datasets. However, existing methods and datasets often struggle with effective visualization and lack diverse power grid scenarios. To address these challenges, we propose a large language model-assisted workflow for small-scale synthetic power grid generation, verification, and visualization. Moreover, by taking into account the variety of node types and connection relationships inherent in power networks, our approach aims to enhance understanding of structural characteristics through detailed feature representation. We leverage the large language model to enrich the generated power grid structure data, uncovering potential internal structures. Furthermore, we employ advanced visualization techniques to generate power structure images, facilitating analysis by human experts and supporting potential learning tasks. Our results demonstrate that the generated power grid structures align well with the properties of real-world networks, and the visual outputs effectively highlight the intricacies of these structures. The code and examples are available at https://***/SYLLABLE604/PG-generator. © 2024 IEEE.

关键词： data generation Large language model power system visualization

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A Dual-Arm Nasopharyngeal Swab Manipulation Robot for Polymerization Chain Reaction Sampling 16th

A Dual-Arm Nasopharyngeal Swab Manipulation Robot for Polyme...

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16th International Federation of Theory of Machines and Mechanisms World Congress, IFToMM WC 2023

作者： Zhang, Tianwei Zheng, Shipei Liu, Cong Yang, Yiming Sun, Zhenglong Lam, Tin lun Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Moying Robotics Shenzhen China The Chinese University of Hong Kong - Shenzhen Shenzhen China

ISBN: (纸本)9783031457692

In the three years since the COVID-19 disease crossed, nasopharyngeal(NP) swab sampling and reverse-transcription Polymerase Chain Reaction (RT-PCR) test techniques have proven to be very effective and reliable tests for the early detection of infected individuals. However, this highly reliable RT-PCR testing is difficult to operate and is the only one of the conventional sampling methods that must be operated by a dedicated swab and trained medical personnel, unlike several other nasopharyngeal swabs, nasal swabs, which can be easily operated by ordinary people for sampling. This paper presents a robot that operates nasopharyngeal swabs for RT-PCR sampling. In order to mimic the difficult sampling operation process of medical professionals and to accurately and stably reach the sampling position deep in the posterior wall of the nasal cavity, this robotic system uses an advanced 3D vision and force sensing hybrid perception system to accurately locate the nostril position, feed the swab into the nostril and then carefully and carefully advance the swab along the inferior nasal tract to the designated sampling position in the posterior nasal cavity through Force and Torque (F/T) sensors. The swab is withdrawn after the sampling operation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robots

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Incorporating Credit Mechanism for Joint Pricing and Charging Optimization for an Electric Taxi Charging System

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Jiwei Sun, Chenxi Tang, Xiaoying School of Science and Engineering Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Science and Engineering The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

In recent years, research on electric vehicles has gained significant momentum driven by the growing emphasis on low-carbon mobility. Despite this progress, the specific challenges faced by electric taxis (ETs) as a vital component of sustainable urban transport have received limited attention. This study formulates a joint optimization problem to determine optimal pricing for charging stations (CSs) while guiding plug-in pure electric taxi (PPET) drivers toward the most profitable charging options. The proposed model aims to maximize the overall utility of CSs under cooperative relationships and provide the optimal charging strategy for ETs. To guarantee that the behaviors of both CS and ET drivers do not interfere with the smooth operation of the system, we incorporate a credit mechanism. We propose an algorithm to simultaneously optimize charging prices at each CS and charging selections for ET drivers. Additionally, we present a comprehensive framework for the credit system, detailing its reward and penalty effect. To validate the efficacy of our pricing optimization system and credit mechanisms, comparative experiments are conducted. The results demonstrate the system's advantages and confirm the credit mechanisms' crucial role in maintaining system integrity and promoting good behavior. As a further testament to our approach's practicality, we conducted a case study in Singapore, verifying the system's performance in a real-world context. © 1967-2012 IEEE.

关键词： Urban transportation

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MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets 24

MaterialSeg3D: Segmenting Dense Materials from 2D Priors for...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Zeyu Gan, Ruitong Luo, Chuanchen Wang, Yuxi Liu, Jiaheng Zhu, Ziwei Li, Qing Yin, Xucheng Zhang, Man Zhang, Zhaoxiang Peng, Junran Beijing University of Posts and Telecommunications Beijing China The Hong Kong Polytechnic University Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong Hong Kong School of Artificial Intelligence Shandong University Jinan China Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong Hong Kong Beihang University Beijing China University of Science and Technology Beijing Beijing China The Hong Kong Polytechnic University Hong Kong Hong Kong Institute of Automation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HKISI-CAS Beijing China

ISBN: (纸本)9798400706868

Driven by powerful image diffusion models, recent research has achieved the automatic creation of 3D objects from textual or visual guidance. By performing score distillation sampling (SDS) iteratively across different views, these methods succeed in lifting 2D generative prior to the 3D space. However, such a 2D generative image prior bakes the effect of illumination and shadow into the texture. As a result, material maps optimized by SDS inevitably involve spurious correlated components. The absence of precise material definition makes it infeasible to relight the generated assets reasonably in novel scenes, which limits their application in downstream scenarios. In contrast, humans can effortlessly circumvent this ambiguity by deducing the material of the object from its appearance and semantics. Motivated by this insight, we propose MaterialSeg3D, a 3D asset material generation framework to infer underlying material from the 2D semantic prior. Based on such a prior model, we devise a mechanism to parse material in 3D space. We maintain a UV stack, each map of which is unprojected from a specific viewpoint. After traversing all viewpoints, we fuse the stack through a weighted voting scheme and then employ region unification to ensure the coherence of the object parts. To fuel the learning of semantics prior, we collect a material dataset, named Materialized Individual Objects (MIO), which features abundant images, diverse categories, and accurate annotations. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method. © 2024 Owner/Author.

关键词： Image texture

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Dataflow Accelerator Architecture for Autonomous Machine Computing (Invited Paper) 24

Dataflow Accelerator Architecture for Autonomous Machine Com...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Liu, Shaoshan Zhu, Yuhao Yu, Bo Gaudiot, Jean-Luc Gao, Guang R. Shenzhen Institute of Artificial Intelligence and Robotics for Society China University of Rochester United States University of California Irvine United States University of Delaware United States

ISBN: (纸本)9798400710773

Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software. © 2024 Copyright is held by the owner/author(s).

关键词： Software architecture

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Multispectral Registration Algorithm Based on Fusion of Infrared and Color Images 16

Multispectral Registration Algorithm Based on Fusion of Infr...

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Wan, Teng Du, Shaoyi Zhang, Qiang Qi, Ying Li, Ce Northwest Normal University The College of Computer Science and Engineering Lanzhou China Xi'an Jiaotong University The Institute of Artificial Intelligence and Robotics The College of Artificial Intelligence Xi'an China Lanzhou University of Technology The College of Electrical and Information Engineering Lanzhou China

ISBN: (纸本)9798331506896

Multispectral data fusion is a fundamental research in the fields of artificial intelligence and computer vision, which provides technical support and theoretical basis for remote sensing, autonomous driving, and medical imaging. In this paper, we present a robust registration algorithm to align RGB-D scans. In colored point clouds, limited by the principle of point cloud collection and generation, noise, and outliers are common and may cause registration failure. Therefore, the core of our method is to apply color information and kernel function to remove the influence of noise and outliers on the alignment accuracy. In the beginning, for the first time, we innovatively use the Cauchy kernel as the energy function of the point set registration to assign different weights to inliers, noise, and outliers to maximize their differentiation. Combined with the maximization optimization strategy, noise and outliers are removed to achieve robust registration. Secondly, since RGB-D data contain both shape and color information, the trimmed bidirectional color distance is applied to reduce the probability of noise and outliers being selected as the matching point pairs when establishing the correspondences. Moreover, the overall formulation is solved by the iterative optimization method. Experiments demonstrate that our algorithm is more robust and accurate than previous point set registration algorithms, especially when aligning point sets containing noise and outliers. © 2024 IEEE.

关键词： Image registration

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Towards robustness and generalization of point cloud representation:A geometry coding method and a large-scale object-level dataset

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Computational Visual Media 2024年第1期10卷 27-43页

作者： Mingye Xu Zhipeng Zhou Yali Wang Yu Qiao Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518000China University of Chinese Academy of Sciences Beijing065001China Shanghai AI Laboratory Shanghai200001China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen518000China Alibaba DAMO Academy Hangzhou242332China

Robustness and generalization are two challenging problems for learning point cloud *** tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengraph to group points with similar geometric information,even when such points are far from each *** also introduce a large-scale point cloud dataset,*** consists of 184 categories and 51,915 synthetic objects,which brings new challenges for point cloud classification,and provides a new benchmark to assess point cloud cross-domain ***,we perform extensive experiments on point cloud classification,using ModelNet40,ScanObjectNN,and our PCNet184,and segmentation,using ShapeNetPart and *** method achieves comparable performance to state-of-the-art methods on these datasets,for both supervised and unsupervised *** and our dataset are available at https://***/MingyeXu/PCNet184.

关键词： geometry coding self-supervised learning point cloud classification segmentation 3D analysis

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Human-like Adaptive Cruise Control System Based on Fuzzy Reinforcement Learning 6

Human-like Adaptive Cruise Control System Based on Fuzzy Rei...

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6th CAA International Conference on Vehicular Control and intelligence, CVCI 2022

作者： Zhang, Tangyike Shi, Jiamin Xin, Jingmin Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

With the great development of artificial intelligence, it is possible to build an autonomous driving system that has truly driving intelligence. As one of the basic functions of autonomous driving, adaptive cruise control (ACC) needs to further improve safety and ride comfort on the existing basis. Recently, reinforcement learning (RL) shows great potential on building ACC systems. However, low learning efficiency and slow convergence speed limit its practical applications. This paper proposes an ACC system combined with semantic information based on fuzzy RL, which mainly includes the following two contributions: 1) a fuzzy RL algorithm with a new update method that is suitable for vehicle following;2) a multivariate reward function is proposed for the ACC, taking into account both the target reward and the process reward, to ensure safety and driving stability of the vehicle while improving the training efficiency. The experimental results show that our algorithm greatly improves the learning speed of Q- Learning under the vehicle following tasks. Unlike the traditional ACC strategy, the learned control strategy is highly similar to that of human drivers. While accurately tracking the target speed, the ride comfort and safety of driving process are fully considered, which reflects a high degree of driving intelligence. © 2022 IEEE.

关键词： Reinforcement learning

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A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1679-1694页

作者： Sanxiu Jiao Lecai Cai Xinjie Wang Kui Cheng Xiang Gao College of Automation and Information Engineering Sichuan University of Science and EngineeringYibin644000China Sanjiang Research Institute of Artificial Intelligence and Robotics Yibin UniversityYibin644000China College of Mechanical Engineering Sichuan University of Science and EngineeringYibin644000China

As a distributed machine learning method,federated learning(FL)has the advantage of naturally protecting data *** keeps data locally and trains local models through local data to protect the privacy of local *** federated learning method effectively solves the problem of artificial Smart data islands and privacy protection ***,existing research shows that attackersmay still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server *** solve this problem,differential privacy(DP)techniques are widely used for privacy protection in federated ***,adding Gaussian noise perturbations to the data degrades the model learning *** address these issues,this paper proposes a differential privacy federated learning scheme based on adaptive Gaussian noise(DPFL-AGN).To protect the data privacy and security of the federated learning training process,adaptive Gaussian noise is specifically added in the training process to hide the real parameters uploaded by the *** addition,this paper proposes an adaptive noise reduction *** the convergence of the model,the Gaussian noise in the later stage of the federated learning training process is reduced *** paper conducts a series of simulation experiments on realMNIST and CIFAR-10 datasets,and the results show that the DPFL-AGN algorithmperforms better compared to the other algorithms.

关键词： Differential privacy federated learning deep learning data privacy

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Efficient Multicore Computing and High-Precision Arithmetic: A Comprehensive Guide to Multicore and Big Number Programming 13

Efficient Multicore Computing and High-Precision Arithmetic:...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Hasija, Taniya Ramkumar, K.R. Kaur, Amanpreet Mittal, Sudesh Kumar Singh, Bhupendra Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Centre for Artificial Intelligence & Robotics Defence Research and Development Organization Bangalore India

ISBN: (纸本)9798350305463

Today system and application programming is moving toward concurrent and parallel programming with the development of multicore and multiprogramming architectures. In an effort to improve study performance, researchers are looking for more efficient methods to include multiprocessing and multicore programming into their simulation systems. This article provides an overview of multicore programming and illustrates how it can be implemented. The paper also focusing the limitations of primitive data types for diverse applications, especially in the context of computer systems. The article delves into the necessity of big numbers and arithmetic on a significant scale. Focusing on C programming, the article showcases the implementation of big numbers, providing scholars with a comprehensive understanding of the concept and its practical realization. © 2024 IEEE.

关键词： Multicore programming

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