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第33届中国控制与决策会议

作者： Tang Jian Hu Yuling Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data

In order to decrease the damage in emergencies and improve the efficiency of evacuation in the space of public buildings,an evacuation strategy with guiders is *** guiders are set within equal areas in evacuation *** force model is used to describe *** on the multi-agent simulation platform Anylogic,plenty of simulation experiments are ***-guided evacuation processes and equal area partitioning guiding processes are compared with double and three *** research results show the guider strategy with equal area partitioning can improve efficiency of evacuation *** addition,the relationship between the number of guiders and evacuation areas are also studied in order to optimize the evacuation *** also compared and analyzed the evacuation density map of double exits and triple exits to observe the evacuation and congestion analysis.

关键词： evacuation partitioning guide strategy equal area zone Anylogic

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Blockchain-Based Dangerous Driving Map data Cognitive Model in 5G-V2X for Smart City Security

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Security and Communication Networks 2022年第0期2022卷

作者： Chen, Kai Xu, Cheng Liu, Hongzhe Wang, Pengfei Chen, Ziyi Beijing Key Laboratory of Information Service Engineering College of Robotics Beijing Union University Beijing China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Communication and Information Center Ministry of Emergency Management of the People's Republic of China Beijing China

The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety. © 2022 Kai Chen et al.

关键词： Risk perception

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Research on Multi-AGVs dynamic scheduling based on deep reinforcement learning

Research on Multi-AGVs dynamic scheduling based on deep rein...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Zhuorui Qin Xiaoqian Wu Yimin Zheng Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Shenzhen Bangqi Technology Intelligent Development Co. Ltd Shenzhen China Pazhou Lab Guangzhou China

Driven by the latest advances in industrial artificial intelligence, automatic guided vehicles (AGVs) have been widely used for material handling in flexible workshops. In the pursuit of agile and flexible production transportation planning, the ability to handle unpredictable and dynamic events is becoming increasingly important. In order to achieve this real-time dynamic control, this study proposes a distributed multi-agent architecture to solve the dynamic Multi-AGVs scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agent, capture the relationship between production transportation operation information and scheduling goals, and make real-time scheduling decisions for flexible jobs with constant arrival intervals. Specialized state, action, and reward representations are proposed to handle variable specifications of dynamic scheduling problems. The performance of this method under different scene scales was verified through simulation studies. Numerical results show that compared with existing traditional heuristic scheduling strategies, this method not only has superior performance, but also its advantages persist even if the manufacturing system job scale changes.

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An Improved Q-RRT^(*) Algorithm Based on Virtual Light

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Computer Systems Science & engineering 2021年第10期39卷 107-119页

作者： Chengchen Zhuge Qun Wang Jiayin Liu Lingxiang Yao Department of Computer Information and Cyber Security Jiangsu Police InsituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InsituteNanjing210031China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Jiangsu Police InsituteNanjing210031China University of Technology Sydney Sydney2007Australia

The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random *** RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal ***,it has been proven to take an infinite time to do *** improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this *** virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the *** this way,the tree can find the initial solution ***,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal ***^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling *** efficiency of the algorithm is verified by comparison experiments in different simulation *** results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.

关键词： Path planning RRT^(*) Q-RRT^(*) LQ-RRT^(*) virtual light

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Multiuser Communication Aided by Movable Antenna

Multiuser Communication Aided by Movable Antenna

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Lipeng Zhu Wenyan Ma Boyu Ning Rui Zhang Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

This paper investigates the multiuser communication aided by movable antenna (MA) via antenna position optimization, where the uplink transmission from multiple user terminals (UTs) each equipped with a single MA to a base station (BS) with a fixed-position antenna (FPA) array is considered. Specifically, we formulate an optimization problem for minimizing the total transmit power of UTs, subject to a minimum-achievable-rate requirement for each UT, where the positions of MAs and the transmit powers of UTs, as well as the receive combining matrix at the BS are jointly optimized. To solve this non-convex optimization problem, we first express the combining matrix of the BS and the total transmit power of UTs as a function of the MAs’ position vectors, which are then optimized by using the gradient descent method in an iterative manner. Simulation results demonstrate that compared to conventional FPA systems, the proposed MA-enhanced multiple access systems can significantly decrease the total transmit power of UTs by moving antennas within small regions of several-wavelength size.

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Intelligent Edge Caching for Metaverse Applications Based on Grover Search

Intelligent Edge Caching for Metaverse Applications Based on...

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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Guoquan Wu Jianhang Tang Yang Zhang Bo Wang Wenchao Jiang Zehui Xiong Yixiong Feng State Key Laboratory of Public Big Data Guizhou University China Department of Computer Science and Engineering Southern University of Science and Technology China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

Metaverse is envisaged as an evolving Internet paradigm that allows people to play, work, and socialize in a shared and virtual ecosystem with immersive and seamless experiences. However, multiple users simultaneously request massive resources for avatar physics emulation and graphical rendering. How to provide high-quality and low-latency meta-verse services for massive concurrent users is a crucial problem. In this work, an intelligent Grover search-based edge caching (GEC) algorithm is proposed for metaverse applications, where quantum theory is used to generate caching solutions with low time complexity. Metaverse scenes can be divided into massive environment panoramic frames and dynamic objects. An edge server is deployed to render and cache the common environment panoramic frames, while the dynamic objects are rendered on head-mounted displays (HMDs). Based on the quantum theory, the panoramic frames are split into several tiles and represented by qubits. The optimal caching results are obtained by performing unitary transformations for the tiles’ features. Finally, we provide extensive simulation experiments by a real- world metaverse dataset. The numerical results reveal that the GEC algorithm can reduce 11.09% of the service time and increase 13.34% of the cache hit rate on average by comparing it with two benchmark caching algorithms.

关键词： Metaverse Heuristic algorithms Simulation Qubit Quantum mechanics Rendering (computer graphics) Internet of Things

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Application note: China's agricultural machinery operation big data system

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Computers and Electronics in Agriculture 2023年 205卷

作者： Wu, Caicong Li, Dong Zhang, Xiaoqiang Pan, Jiawen Quan, Lei Yang, Lili Yang, Weizhong Ma, Qin Su, Chunhua Zhai, Weixin College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China Agricultural Mechanization General Station Ministry of Agriculture and Rural Affairs Beijing100122 China

Agricultural machinery operations are mainly performed by cross-regional socialized services in China. It is necessary to take advantage of national data system to implement dynamic monitoring, data sharing, and big data applications for agricultural machinery operations. This application note outlines the system composition, principal technology, primary functions, and application examples of the big data system. This system integrates agricultural machinery operations with big data, which can promote scientific decision-making with data management and facilitate more efficient agricultural production. Promoting the effective, orderly, and scientific growth of agricultural machinery operations and the advancement of agricultural modernization is of theoretical and practical importance. © 2022 Elsevier B.V.

关键词： Big data

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： knowledge representation

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Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video

arXiv

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arXiv 2023年

作者： Xu, Zenan Meng, Xiaojun Wang, Yasheng Su, Qinliang Qiu, Zexuan Jiang, Xin Liu, Qun School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Noah’s Ark Lab Huawei Technologie The Chinese University of Hong Kong Hong Kong Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript. Inspired by the success of the large-scale generative pre-trained language model (GPLM) in generating high-quality textual content (e.g., summary), recent MAS methods have proposed to adapt the GPLM to this task by equipping it with the visual information, which is often obtained through a general-purpose visual feature extractor. However, the generally extracted visual features may overlook some summary-worthy visual information, which impedes model performance. In this work, we propose a novel approach to learning the summary-worthy visual representation that facilitates abstractive summarization. Our method exploits the summary-worthy information from both the cross-modal transcript data and the knowledge that distills from the pseudo summary. Extensive experiments on three public multimodal datasets show that our method outperforms all competing baselines. Furthermore, with the advantages of summary-worthy visual information, our model can have a significant improvement on small datasets or even datasets with limited training data. Copyright © 2023, The Authors. All rights reserved.

关键词： Visual languages

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Hypergraph Attribute Attention Network for Community Recommendation

Hypergraph Attribute Attention Network for Community Recomme...

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IEEE International Conference on data Mining (ICDM)

作者： Kang Li Wu-Dong Xi Xing-Xing Xing Chang-Dong Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China Contribution during internship at NetEase Games

In recent years, the gaming industry has flourished. Therefore, game manufacturers need to strive to improve the gaming experience of users in the game. Social recommendation tasks in game scenes have become increasingly important. In this work, we focus on community recommendation scenario. A distinctive feature of game community recommendation is that each member can only belong to one gang for a certain duration, which we refer to as uniqueness of communities. The problem caused by uniqueness is that for users to be recommended, there are no positive samples available for training. The challenge caused by uniqueness is that there are no positive samples available for training when users are recommended. Therefore, the collaborative filtering information between the user and the community is very sparse. Meanwhile, existing methods fail to fully model communities and users based on their features and profiles. To address these problems, we propose Hypergraph Attribute Attention Network (HATT) framework. In order to fully exploit user profiles and similarity between users, we discretize user features into entity nodes and model the heterogeneous relationships between users and communities by hyperedge. We propose a hypergraph attention-based message passing mechanism to capture the high-order relationships and obtain embedding with more semantics. At last, we design contrastive learning paradigms to enhance the model’s representation ability and apply a multi task training strategy to train the model. Extensive experiments on two real world game datasets are conducted and the results demonstrate the superiority of our method in community recommendation.

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