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SSRN 2024年

作者： Liu, Yiming Yu, Yang Roberto, Baldacci Tang, Jiafu Sun, Wei State Key Laboratory of Synthetic Automation for Process Industries Institute of Intelligent Data and Systems Engineering Northeastern University Shenyang110819 China Dalian University of Technology School of Economics and Management Liaoning Dalian116024 China Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation P.O. Box 34110 Doha Qatar College of Management Science and Engineering Dongbei University of Finance and Economics Shahekou Dalian116026 China Business School Liaoning University Shenyang110316 China

This paper considers a green vehicle routing problem termed the time-dependent green vehicle routing problem with time windows (TDGVRPTW). The TDGVRPTW is an extension of the green vehicle routing problem with time windows in green logistics. It considers time-dependent vehicle speed and aims to minimize carbon emissions. Since the travel times and carbon emissions between locations depend on the departure time from the starting location, optimizing carbon emissions requires determining the optimal departure times from both the depot and customers. This paper presents a branch-price-and-cut (BPC) algorithm to solve the TDGVRPTW. The problem is formulated based on a set-partitioning model. To solve the pricing problem associated with the set-partitioning model, we first define backward non-dominated time-dependent arcs and optimal timed routes. We also introduce three types of routes, forward, backward, and patchwork optimal timed routes, and analyze their characteristics. Then, we introduce a method to adjust the times in backward and patchwork optimal timed routes from the latest to the earliest. We prove that this method guarantees correctness and effectively reduces complexity. We propose a bidirectional labeling algorithm to generate routes. The pricing problem employs state-of-the-art techniques, including limited memory subset row cuts (lm-SRCs), ng-route relaxation, and bucket graphs to enhance the algorithm’s efficiency. The BPC algorithm is tested on a set of instances derived from benchmark instances in the literature. The results demonstrate the effectiveness of the proposed algorithm in solving the TDGVRPTW. © 2024, The Authors. All rights reserved.

关键词： Travel time

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System Identification and Prediction of Dynamic System and Microwave Thermal Process Using a Recurrent Fuzzy Quantum Neural Network

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IOP Conference Series: Materials science and Engineering 2018年第1期466卷

作者： Chaoyin Li Qingyu Xiong Kai Wang Yang Yu Tong Liu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Automation Chongqing University Chongqing 400044 China School of Big Data & Software Chongqing University Chongqing 401331 China

Microwave heating is a time-varying, non-linear process. Mechanism modeling of the microwave thermal process is extremely difficult because of the complex microwave heating environment. This paper presents a recurrent fuzzy quantum neural network with full feedbacks (RFQNN) for prediction and identification of dynamic systems and the actual microwave heating process. In the RFQNN, a quantum neural network is introduced to the consequent part of the fuzzy rules to improve the mapping ability and the identification precision. All of the rules are generated and learned online through a simultaneous structure and parameter learning. During the structure learning, an online clustering algorithm combined with Mahalanobis distance elimination algorithm perform effectively in generating or removing fuzzy rules. And then a gradient descent algorithm is introduced to update the parameters during the parameter learning process. And finally, we test the RFQNN by dynamic plants and the microwave thermal process. The results show that it performs well in dynamic system processing compared with other recurrent fuzzy neural networks.

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Intrablock interleaving for Batched network coding with blockwise adaptive recoding

arXiv

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arXiv 2021年

作者： Yin, Hoover H.F. Ng, Ka Hei Zhong, Allen Z. Yeung, Raymond W. Yang, Shenghao Chan, Ian Y.Y. The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong The Department of Information Engineering The Chinese University of Hong Kong Hong Kong Limited The Department of Physics The Chinese University of Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Economics The Chinese University of Hong Kong Hong Kong

Batched network coding (BNC) is a low-complexity solution to network transmission in multi-hop packet networks with packet loss. BNC encodes the source data into batches of packets. As a network coding scheme, the intermediate nodes perform recoding on the received packets belonging to the same batch instead of just forwarding them. A recoding scheme that may generate more recoded packets for batches of a higher rank is also called adaptive recoding. Meanwhile, in order to combat burst packet loss, the transmission of a block of batches can be interleaved. Stream interleaving studied in literature achieves the maximum separation among any two consecutive packets of a batch, but permutes packets across blocks and hence cannot bound the buffer size and the latency. To resolve the issue of stream interleaver, we design an intrablock interleaver for adaptive recoding that can preserve the advantages of using a block interleaver when the number of recoded packets is the same for all batches. We use potential energy in classical mechanics to measure the performance of an interleaver, and propose an algorithm to optimize the interleaver with this performance measure. Our problem formulation and algorithm for intrablock interleaving are also of independent interest. © 2021, CC BY-NC-ND.

关键词： Network coding

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Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access

arXiv

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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Deep learning for depression recognition with audiovisual cues: A review

arXiv

引用

arXiv 2021年

作者： He, Lang Niu, Mingyue Tiwari, Prayag Marttinen, Pekka Su, Rui Jiang, Jiewei Guo, Chenguang Wang, Hongyu Ding, Songtao Wang, Zhongmin Pan, Xiaoying Dang, Wei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Beijing100190 China Beijing100049 China Department of Computer Science Aalto University Espoo Finland School of Foreign Languages Northwest University Shaanxi Xi'an China School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi'an China School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi'an China Shaanxi Mental Health Center Shaanxi Xi'an710061 China

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies. © 2021, CC BY.

关键词： Diagnosis

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Research on key Technologies of Student Electronic Depository System Based on Blockchain

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Journal of Physics: Conference Series 2019年第3期1168卷

作者： Sheping Zhai Jing Li Cheng Qiu Yuanyuan Yang Weixing Zuo School of Computer Science & Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an 710121 China

Aiming at the problem that a large number of students' electronic deposit data is easy to forge and destroy, a student electronic deposit application architecture based on blockchain is proposed. There are some features on blockchain, such as decentralized, the non-tamperable of data, the consensus mechanism and data encryption features, and the electronic depository network. All these features provide a secure and credible storage environment, ensures the traceable of the student's electronic deposit information, the trust of the entire network, and the security of the data. At the same time, an improved PBFT consensus algorithm is proposed, which eliminates the certificate elimination phase to reduce network resource consumption, and constructs a decentralized electronic depository platform in combination with the blockchain to realize the authenticity, integrity and reliability of the electronic certificate data. The further development and application of blockchain technology is prospected, hoping to provide inspiration and reference for future related research.

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

arXiv

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

arXiv

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arXiv 2021年

作者： Sun, Xian Wang, Peijin Yan, Zhiyuan Wang, Cheng Diao, Wenhui Chen, Jin Li, Jihao Feng, Yingchao Xu, Tao Weinmann, Martin Hinz, Stefan Fu, Kun Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University Xiamen361005 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China Beijing Remote Sensing Information Institute Beijing100011 China Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany

With the rapid development of deep learning, many deep learning based approaches have made great achievements in object detection task. It is generally known that deep learning is a data-driven method. data directly impact the performance of object detectors to some extent. Although existing datasets have included common objects in remote sensing images, they still have some limitations in terms of scale, categories, and images. Therefore, there is a strong requirement for establishing a large-scale benchmark on object detection in high-resolution remote sensing images. In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M. We collected remote sensing images with a resolution of 0.3m to 0.8m from different platforms, which are spread across many countries and regions. All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes. Compared with existing detection datasets dedicated to object detection, the FAIR1M dataset has 4 particular characteristics: (1) it is much larger than other existing object detection datasets both in terms of the quantity of instances and the quantity of images, (2) it provides more rich fine-grained category information for objects in remote sensing images, (3) it contains geographic information such as latitude, longitude and resolution, (4) it provides better image quality owing to a careful data cleaning procedure. To establish a baseline for fine-grained object recognition, we propose a novel evaluation method and benchmark fine-grained object detection tasks and a visual classification task using several State-Of-The-Art (SOTA) deep learning based models on our FAIR1M dataset. Experimental results strongly indicate that the FAIR1M dataset is closer to practical application and it is considerably more challeng

关键词： Object recognition

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DRDST: Low-latency DAG Consensus through Robust Dynamic Sharding and Tree-broadcasting for IoV

arXiv

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arXiv 2024年

作者： Chen, Runhua Luo, Haoxiang Sun, Gang Yu, Hongfang Niyato, Dusit Dustdar, Schahram Glasgow College University of Electronic Science and Technology of China Chengdu611731 China James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Distributed Systems Group TU Wien Vienna1040 Austria ICREA Universitat Pompeu Fabra Barcelona08002 Spain

The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Addressing blockchain's scalability challenges posed by the growing number of nodes and transactions calls for innovative solutions, among which sharding stands out as a pivotal approach to significantly enhance blockchain throughput. However, existing schemes still face challenges related to a) the impact of vehicle mobility on blockchain consensus, especially for cross-shard transaction;and b) the strict requirements of low latency consensus in a highly dynamic network. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we first develop a standard for evaluating the network stability of nodes, combined with the nodes' trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle cross-shard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in latency, throughput, consensus success rate, and node traffic load. © 2024, CC0.

关键词： Trees (mathematics)

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Large-Scale Tensorized Multi-View Kernel Subspace Clustering

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ACM Transactions on intelligent Systems and Technology 1000年

作者： Guang-Yu Zhang Dong Huang Chang-Dong Wang College of Mathematics and Informatics South China Agricultural University China School of Computer Science and Engineering Sun Yat-sen University Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guangdong Key Laboratory of Big Data Analysis and Processing Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph structure in the original linear space, restricting their feasibility for nonlinear scenarios. Second, they usually overlook the potential benefits of jointly capturing the inter-view and intra-view information for enhancing the anchor representation learning. Third, these approaches mostly perform anchor-based subspace learning by a specific matrix norm, neglecting the latent high-order correlation across different views. To overcome these limitations, this paper presents an efficient and effective approach termed Large-scale Tensorized Multi-view Kernel Subspace Clustering (LTKMSC). Different from the existing AMSC approaches, our LTKMSC approach exploits both inter-view and intra-view awareness for anchor-based representation building. Concretely, the low-rank tensor learning is leveraged to capture the high-order correlation (i.e., the inter-view complementary information) among distinct views, upon which the \(l_{1,2}\) norm is imposed to explore the intra-view anchor graph structure in each view. Moreover, the kernel learning technique is leveraged to explore the nonlinear anchor-sample relationships embedded in multiple views. With the unified objective function formulated, an efficient optimization algorithm that enjoys low computational complexity is further designed. Extensive experiments on a variety of multi-view datasets have confirmed the efficiency and effectiveness of our approach when compared with the other competitive approaches.

关键词： data clustering Multi-view clustering Large-scale clustering Tensorized kernel subspace clustering Inter-view and intra-view awareness

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