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A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1918-1932页

作者： Zhuoxuan Li Iakov Korovin Xinli Shi Sergey Gorbachev Nadezhda Gorbacheva Wei Huang Jinde Cao School of Mathematics Southeast UniversityNanjing 210096China Scientific Research Institute of Multiprocessor Computer Systems Southern Federal UniversityTaganrog 347928Russia School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Russian Academy of Engineering Moscow 125009Russia Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China Nanjing Modern Multimodal Transportation Laboratory Nanjing 211100China Yonsei Frontier Laboratory Yonsei UniversitySeoulKorea(South)

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

关键词： Extreme learning machine algorithm with residual correction(RELM) metaheuristic optimization oil-gas transportation RIOHTrack,rutting depth

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Association Rule Guided Web API Complementary Function Recommendation for Mashup Creation: An Explainable Perspective 17th

Association Rule Guided Web API Complementary Function Recom...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： He, Pengfei Qi, Wenchao Liu, Xiaowei Liu, Linlin You, Dianlong Shen, Limin Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao China National Science Libraries Chinese Academy of Sciences Beijing China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (纸本)9789819923557

As more and more service providers encapsulate and publish their data and services on the internet in the form of Web APIs, the number of Web APIs is ever-increasing. For this reason, Web API recommendation is gaining momentum and has achieved high performance in accuracy. However, few studies paid attention to function recommendation. As an indispensable entity in the mashup-API ecosystem, function is not only the in-facto basis of API taxonomy, but also determines the compatibility and the internal construction pattern of mashup composition. Considering that users adopt multiple function invocations during a development cycle and the number of functions is also increasing, we propose the complementary function recommendation(CFR), a function-to-function problem. To solve the CFR, we regard each mashup as a transaction set for frequent pattern mining and propose an association rule-based complementary function recommendation(ARCFR) system, which provides function recommendation and corresponding probability explanation. Our experiments show that ARCFR can recommend complementary function effectively, and we give two applicable scenarios to demonstrate the practical value of our method in more aspects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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system of systems Architecture and Autonomous Robot Swarms: A Long Awaited Junction

System of Systems Architecture and Autonomous Robot Swarms: ...

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Annual IEEE systems Conference

作者： Thomas Rigaut Omar Hammami U2IS - Computer Science and System Engineering Laboratory ENSTA Paris Polytechnique Institute of Paris Palaiseau France

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Research on the design of autonomous robot swarms has explored many directions over the past ten years (multi-robot systems, coordination protocols, biomimicry, centralized or distributed architecture) but has struggled to combine these different axes in a holistic design approach. The junction between autonomous robot swarm engineering and systems of systems engineering must be pushed further. The first step is to evaluate the contribution of systems of systems architecture to the design of autonomous robot swarms. We propose two initial tests of the relevance of this approach: do the methods and languages of systems of systems architecture allow us to describe the dynamics of these autonomous robot swarms? Do the concepts of system of systems architecture allow us to establish a taxonomy of autonomous robot swarms? This approach should also allow us to test the current limits of the standards of system of systems architecture: taxonomies too focused on the governance of systems of systems, and coexistence without unification of numerous static architecture methods (Model-Based systems engineering, Architecture Frameworks, Hypergraphs, etc.) and dynamic architecture methods (Dynamical systems, system Dynamics, State Machine Diagrams, Event-B modeling, Agent-Based Modeling, BPMN, Activity Diagrams, etc.)

关键词： Protocols system dynamics Taxonomy Multi-robot systems Modeling Junctions Dynamical systems Standards system of systems Autonomous robots

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Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing

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Microsystems & Nanoengineering 2022年第3期8卷 153-161页

作者： Muhammad Umair Khan Jungmin Kim Mahesh Y.Chougale Chaudhry Muhammad Furqan Qazi Muhammad Saqib Rayyan Ali Shaukat Nobuhiko P.Kobayashi Baker Mohammad Jinho Bae Hoi-Sing Kwok Department of Ocean System Engineering Jeju National University102 JejudaehakroJeju 63243Republic of Korea Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788UAE System on Chip Center Khalifa UniversityAbu Dhabi 127788UAE Department of Electronics and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina State Key Laboratory on Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Baskin School of Engineering Univerity of California Santa Cruz1156 High StreetSanta CruzCA 95064USA

By exploiting ion transport phenomena in a soft and flexible discrete channel,liquid material conductance can be controlled by using an electrical input signal,which results in analog neuromorphic *** paper proposes an ionic liquid(IL)multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL_(4) and H_(2)O into the two ends of a discrete polydimethylsiloxane(PDMs)*** spike rate-dependent plasticity(SRDP)and spike-timing-dependent plasticity(STDP)behavior are highly stable by modulating the input ***,the discrete channel device presents highly durable performance under mechanical bending and *** the obtained parameters from the proposed ionic liquid-based synaptic device,convolutional neural network simulation runs to an image recognition task,reaching an accuracy of 84%.The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.

关键词： computing gateway state

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PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECASTING 12

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China WeBank Institute of Financial Technology Shenzhen University China

Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency. Code is available at https://***/Hank0626/PDF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Engagement Level Detection Using Facial Extraction and Multi-Stacked Convolutional Neural Network in e-Learning Settings

Engagement Level Detection Using Facial Extraction and Multi...

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Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS)

作者： Fitra Abdurrachman Bachtiar Al'Ravie Mutiar Mahesa Eric Wallace Cooper Computer Science Faculty Intelligent System Laboratory Universitas Brawijaya Malang Indonesia College of Information Science and Engineering Affective Engineering and Arts Laboratory Ritsumeikan University Ibaraki Japan

ISBN: (数字)9798350379914

ISBN: (纸本)9798350379921

Engagement in learning is crucial in maintaining student eagerness as fuel for student learning to achieve learning success. In e-Learning settings, engagement also plays an im-portant role as a factor influencing student learning success as students are regulating themselves during learning one-on-one using computer devices. Failing to detect student engagement may result in unsuccessful learning. In extreme cases, students may disengage and learning purpose cannot be achieved. This research aims to detect user engagement in e-Learning settings by using a deep learning approach with a feature extraction method. The dataset used in this study is the DAiSEE dataset. The dataset is in the form of videos and is extracted into frames and specific facial features. The facial area was extracted using the Mediapipe Library. The features include 1434 3D landmark features, 52 action unit features, one depth estimation feature, and three head pose estimation features. This research also conducts image selection, which selecting frames with an interval of 15 in the train, validation, and test folders. The multi-stacked Convolutional Neural Network model was used to classify four levels of engagements. The results were compared to the existing approach in engagement detection. The untuned proposed model achieved an accuracy of 48.95%, while the tuned proposed model achieved an accuracy of 52.94%. The proposed model is able to compete with state-of-the-art methods using a less complex model. Even so, the model still cannot learn labels perfectly due to a very imbalanced dataset and a possibility of model overfitting. This research is expected to be useful for educational institutions and the e-Learning field in the future.

关键词： Seminars Electronic learning Accuracy Three-dimensional displays Pose estimation Feature extraction Power electronics Convolutional neural networks Videos Overfitting

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Physically Based Bidirectional Path Tacing Algorithm on the Tianhe-2 Supercomputer 25

Physically Based Bidirectional Path Tacing Algorithm on the ...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Yijia Zhang, Congpin Xu, Zhengwei Li, Ligang Chinese Academy of Sciences National Space Science Center Key Laboratory of Electronics and Information Technology for Space System Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer and Information Engineering Henan Normal University Henan Xinxiang China

ISBN: (纸本)9798350330014

Physically based ray tracing is a technique that involves creating a photo-realistic image from a 3D scene description by utilizing the laws of physics to simulate the behavior of light and matter. However, due to the random length of light samples and irregular memory access patterns, it can suffer from low scalability and severe load imbalance. As a result, the development of a framework for multi-node parallelism algorithm in physically based ray tracing is a significant challenge. To tackle these issues, this paper introduces a novel algorithm named distributed parallel physically based bidirectional path tacing algorithm (DP-BDPT). Through analyzing the physical essence of bidirectional path tracing algorithm and the light transport equation, we have devised a framework that carefully divides tasks between inter-compute nodes and intra-compute nodes. To support high scalability, we have developed a physically based architecture. Additionally, we have proposed a hybrid scheduling method to achieve load balancing. To further enhance performance, we have analyzed several key factors that affect rendering performance and implemented adaptive optimization techniques. The experiments conducted on the Tianhe-2 Supercomputer involving typical and challenging 3D scenes, namely the Cornell-box, Veach-bidir, and Teapot-full experiments, have illustrated that DP-BDPT demonstrate near-linear speedups as the number of CPU cores scales from 1200 to 9600, thereby demonstrating their efficiency when operating on a ten-thousand-core system. © 2023 IEEE.

关键词： Ray tracing

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation system

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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