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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiongjie Tao Hui Guo Caixu Xu Hubei Normal University Huangshi China Guangzhou ForceControl-Yuanhai IT Co. Ltd Guangdong China Faculty of Arts and Humanities Macao University of Science and Technology Macao China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

With the development of artificial intelligence and the deep integration of digitalization and intelligent manufacturing. Aiming at the “raw material storage yard for raw material preparation in cement manufacturing” which still adopts the traditional manual control method for production, the realization of the digital storage yard not only improves the production efficiency, reduces the production cost of the enterprise, but also improves the working environment of the employees. Therefore, this paper uses IIoT technology and PLC control technology to digitally transform the site, and uses three-dimensional visualization technology to realize the simulation of intelligent equipment in the yard, and at the same time improves the security protection of production data. The realization shows that the remote control scheme of the digital yard based on the IIoT proposed in this paper is of great significance for the enterprise to realize the goal of intelligent, digital and safe production.

关键词： Solid modeling Computational modeling Process control Production Raw materials Data models Manufacturing

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The design of a mechanical force feedback VR data glove

The design of a mechanical force feedback VR data glove

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiliang Lin Bin Hu Jiahao Pang Jingjie Liao School of international education Xiamen University of technology Xiamen China School of Arts Soochow University Suzhou China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China school of big data and software engineering Wuzhou University Wuzhou China

With the development of virtual reality technology, data gloves began to widely popularize various fields. Some of the force feedback data gloves at this stage still have problems such as high cost and complex structure. The mechanical force feedback data glove proposed in this paper uses the principle of mechanical rotation, using a potentiometer as the rotating shaft and a nylon rope to drive the potentiometer to rotate, so that it records the relevant data instead of sensors, and uses a servo to bring force feedback experience. Experiments indicate that the force feedback glove in this paper has both the force feedback effect of the existing force feedback data gloves and is lower in cost than the existing data gloves and has good generalizability.

关键词： Shafts Mechanical sensors Costs Force feedback Virtual reality Data gloves Potentiometers

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Insight into Impact of Low-Content Zro2 Doping on Photocatalytic Performance of Gcn for Tetracycline Degradation

SSRN

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SSRN 2023年

作者： Jiang, Meng Xie, Jinqian Li, Zhanguo Huang, Mingxiu Yao, Yucen Sun, Wei Wang, Bei Key Laboratory of laser Technology and Optoelectronic Functional Materials of Hainan Province Key Laboratory of Functional Materials and Photochemistry of Haikou Collage of Chemistry and Chemical Engineering Hainan Normal University Haikou571158 China College of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China

Semiconductor nanoparticle doping is an effective strategy for improving photocatalytic performance of graphitic carbon nitride. However, the specific mechanism for impact of semiconductor doping on photodegradation process remains unclear. Herein, we fabricated a ZrO2/gCN composite catalyst with a low doping level (1:32) and investigated doping effect on morphology, elemental state and photophysical process, including exciton, charge transfer and recombination behavior, etc. Smaller nanoscale morphology, larger specific surface area and stronger fluorescence emission for composite photocatalyst are confirmed by SEM, BET calculation, and PL spectra. Photoelectrochemical experiments indicate that charge transfer process is significantly improved and then bigger photogenerated current is achieved. Electron paramagnetic resonance results also show that •O2 signal is enhanced obviously with ZrO2 under light condition. Time resolved photoluminescence spectra further explain that ZrO2 doping is favorable for charge transfer with shortened exciton lifetime and unfavorable for charge transport with shortened recombination lifetime. Finally, the photodegradation performance for tetracycline with ZrO2/gCN is enhanced by 70% compared with pure gCN. © 2023, The Authors. All rights reserved.

关键词： Charge transfer

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A Commitment and Ring Signature based Scheme for Amount and Identity Privacy Protection in Blockchain

A Commitment and Ring Signature based Scheme for Amount and ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Shiyong Huang Haocong Li Ruoting Xiong Wei Ren Jie He Yi Ren School of Computer Science China University of Geosciences Wuhan China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China School of Computer Science Beijing Jiaotong University Beijing China School of Computing Science University of East Anglia Norwich UK Beijing Key Laboratory of Urban Spatial Information Engineering Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Blockchain has been envisioned as an anonymous cryptocurrency framework and can be applied in various applications such as e-payment, share economics, and distributed ledger. Although an account is anonymous, privacy in terms of consumption behaviors still imposes leakage risks. For example, an adversary may infer the consumption capability related to an account further by analyzing the history of consumption records. It is thus of critical importance to design a solution to preserve the anonymity of both transaction amount and transaction peer’s identity, which is changeable because the amount must be still authenticated in anonymity. In this paper, we propose a scheme by using Pedersen commitment to anonymize the transaction amount, and together using ring signature to conceal the transaction peer’s identity while maintaining authentication. Especially, we further improve the security of anonymous authentication enabled by ring signature by introducing accountability, which can defend against double-spending attacks by penalization and empower auditability. The extensive performance and security analysis justify the applicability of the proposed scheme.

关键词： Economics Privacy Distributed ledger Authentication Blockchains Information filtering Security

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

关键词：

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Fast Coding Mode Prediction for Intra Prediction in VVC SCC

Fast Coding Mode Prediction for Intra Prediction in VVC SCC

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IEEE International Conference on Image Processing

作者： Dayong Wang Junyi Yu Xin Lu Frederic Dufaux Hongwei Guo Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des signaux et systèmes School of Engineering Honghe University Mengzi Yunnan China University of Electronic Science and Technology of China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by $34.95 \%$ on average while maintaining almost the same coding efficiency.

关键词： Video coding Deep learning Image coding Modulation Prediction algorithms Complexity theory Copper

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Efficient Multi-Query Oriented Continuous Subgraph Matching

Efficient Multi-Query Oriented Continuous Subgraph Matching

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International Conference on Data Engineering

作者： Ziyi Ma Jianye Yang Xu Zhou Guoqing Xiao Jianhua Wang Liang Yang Kenli Li Xuemin Lin School of Artificial Intelligence Hebei University of Technology China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University China Cyberspace Institute of Advanced Technology Guangzhou University China Department of New Networks PengCheng Laboratory China College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China Antai College of Economics and Management Shanghai Jiao Tong University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many efficient CSM algorithms have been recently proposed, they are mainly designed to process a single query. However, in some application scenarios, multi-query oriented continuous subgraph matching (MQCSM) may be of more interest. To our knowledge, the two existing solutions to MQCSM are outdated due to unsatisfactory performance. In this paper, we propose MQ-Match, an efficient approach to MQCSM. First, we design a compact yet effective index structure CCG, which maintains the local matching result of vertices in the data graph using a directed graph. The directed edges in CCG can be utilized as an effective pruning rule for the subsequent incremental matching algorithm when expanding a partial match. Then, we develop a computation sharing incremental matching algorithm. In specific, a set of matching trees is constructed based on the depth-first search trees of the query graphs. By utilizing CCG, we conduct subgraph matching for the matching tree to collect the incremental matches for the query graphs, where the common structures of query graphs are matched only once. Extensive experiments show that MQ-Match can achieve 3.1x-7071.4x speedup over the competitors, and consumes much less memory under the majority of the experiment settings.

关键词： Heuristic algorithms Memory management Directed graphs Data engineering Fraud Computational efficiency Indexes

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Performance studies of jet flavor tagging and measurement of Rb(Rc) using ParticleNet at CEPC

arXiv

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arXiv 2022年

作者： Liao, Libo Wang, Shudong Song, Weimin Zhang, Zhaoling Li, Gang Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University 82 Fumin Third Road Wanxiu District Wuzhou China Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Shijingshan District Beijing China College of Physics Jilin University 2699 Qianjin Street Changchun China University of Chinese Academy of Sciences 19A Yuquan Road Shijingshan District Beijing China

Jet flavor tagging plays a crucial role in the measurement of relative partial decay widths of Z boson, denoted as Rb(Rc), which is considered as a fundamental test of the Standard Model and sensitive probe to new physics. In this study, a Deep Learning algorithm, ParticleNet, is employed to enhance the performance of jet flavor tagging. The combined efficiency and purity of c-tagging is improved by more than 50% compared to the Circular Electron Positron Collider (CEPC) baseline software. In order to measure Rb(Rc) with this new flavor tagging approach, we have adopted the double-tagging method. The precision of Rb(Rc) is improved significantly, in particular to Rc, which has seen a reduction in statistical uncertainty by 40%. © 2022, CC BY.

关键词： Bosons

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AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models

arXiv

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arXiv 2024年

作者： Yao, Yifei He, Wentao Gu, Chenyu Du, Jiaheng Tan, Fuwei Zhu, Zhen Lu, Junguo All authors are with Machine Vision and Autonomous System Laboratory Department of Automation School of Electrical Information and Electronic Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China

Training and deploying reinforcement learning (RL) policies for robots, especially in accomplishing specific tasks, presents substantial challenges. Recent advancements have explored diverse reward function designs, training techniques, simulation-to-reality (sim-to-real) transfers, and performance analysis methodologies, yet these still require significant human intervention. This paper introduces an end-to-end framework for training and deploying RL policies, guided by Large Language Models (LLMs), and evaluates its effectiveness on bipedal robots. The framework consists of three interconnected modules: an LLM-guided reward function design module, an RL training module leveraging prior work, and a sim-to-real homomorphic evaluation module. This design significantly reduces the need for human input by utilizing only essential simulation and deployment platforms, with the option to incorporate human-engineered strategies and historical data. We detail the construction of these modules, their advantages over traditional approaches, and demonstrate the framework’s capability to autonomously develop and refine controlling strategies for bipedal robot locomotion, showcasing its potential to operate independently of human intervention. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Prediction Approach Based on Long Short-Term Memory Networks for Dynamic Multiobjective Optimization

SSRN

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SSRN 2024年

作者： Xu, Biao Rang, Gejie Li, Wenji Fan, Zhun Gong, Dunwei Yang, Shengxiang He, Jie College of Engineering Shantou University Shantou515063 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Energy and Mechanical Engineering Nanchang Institute of Technology Nanchang330013 China Minjiang University China

Dynamic multiobjective optimization problems (DMOPs) have posed considerable hurdles for traditional evolutionary optimization techniques due to the perpetual variations in their Pareto-optimal sets (PSs) and fronts (PFs). Prediction-based methods have shown promise in swiftly tracking these alterations. However, the majority of current methods rely on linear models to anticipate changing PSs, which can be limiting. When PSs across different environments exhibit nonlinear patterns, accurately predicting the PS in a novel problem becomes challenging. To address this issue, this study introduces a dynamic multiobjective evolutionary optimization algorithm leveraging long short-term memory (LSTM) network predictions. In our approach, the PS of each problem is first divided into several clusters, and the center of each cluster can then be obtained. Thereafter, the cluster centers for the PSs of the problems with different environments form a number of time series. Subsequently, the LSTM network models are trained using the time seriesdata as training samples. Furthermore, the cluster centers of the new PS are predicted utilizing the trained models. LSTM networks excel at capturing historical information and modeling nonlinear relationships, ensuring high prediction accuracy. To enhance the algorithm’s performance, a novel population-generation strategy is employed, ensuring a well-converged and diverse initial population. The proposed algorithm is rigorously tested using benchmark functions, and the results demonstrate its effectiveness in handling DMOPs, outperforming state-of-the-art algorithms. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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