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Short-term train arrival delay prediction:a data-driven approach

Railway sciences 2024年第4期3卷 514-529页

作者： Qingyun Fu Shuxin Ding Tao Zhang Rongsheng Wang Ping Hu Cunlai Pu Postgraduate Department China Academy of Railway SciencesBeijingChina Signal and Communication Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Traffic Management Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail TransitBeijingChina Scientific and Technological Information Research Institute China Academy of Railway Sciences Corporation LimitedBeijingChina Yibin Track Signal and Communication DepotChina Railway Chengdu Group Co.LtdChengduChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays ***,delay predictions often involve imprecise shifts based on known delay ***-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment *** current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen ***/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival *** combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information *** on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding ***-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late *** show precise and rapid *** with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late *** enhanced adaptability to real-world train operations,training with full data is ***/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay *** innovatively compares and analyzes the model’s performance using both full data and delay data ***,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstra

关键词： Train delay prediction Intelligent dispatching command Deep learning Convolutional neural network Long short-term memory Attention mechanism

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Integration of Consumer Electronics in Precision Agriculture Using Low-Carbon Wheat Lodging Drone Images

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IEEE Transactions on Consumer Electronics 2025年

作者： Dasari, Rakeshnag Alsaadi, Mahmood Nimma, Divya Ramesh, Janjhyam Venkata Naga Tiwari, Mohit Jangir, Pradeep Santhi, M.V.B.T. Turayevich, Jumaniyazov Inomjon Soni, Mukesh Department of Cse Acharya Nagarjuna university Nagarjuna Nagar Guntur522510 India University of Al Maarif College of Sciences Department of Computer Sciences Al Anbar31001 Iraq Data Analyst in Ummc University of southern Mississippi United States Graphic Era Hill University Department of Cse Dehradun248002 India Graphic Era Deemed To Be University Department of Cse Uttarakhand Dehradun248002 India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi110063 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai602 105 India Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Tashkent State University of Economics Department of Finance and financial technologies Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India Dr. D. Y. Patil School of Science and Technology Tathawade Pune India

Agricultural consumer electronics, such as drones, sensors, and robotics, play a pivotal role in addressing challenges like wheat lodging, which can significantly impact crop yield and quality. This study leverages consumer-grade UAVs to classify wheat lodging types-root lodging and stem lodging-using high-resolution RGB images captured at three altitudes (15, 45, and 91 meters). By employing automatic segmentation techniques, datasets were generated for each altitude, and a refined EfficientNetV2-C model was proposed for classification. The model incorporates a Coordinate Attention (CA) mechanism to enhance feature extraction and Class-Balanced Focal Loss (CB-Focal Loss) to address data imbalance, achieving an average accuracy of 93.58%. This research highlights the integration of advanced AI-based classification with low-carbon agricultural drones, underscoring their relevance to consumer electronics. Compared to four conventional machine learning and two deep learning models, EfficientNetV2-C demonstrated superior performance at all altitudes while maintaining minimal carbon emissions. The study also examines the influence of UAV flight altitude on classification efficacy, revealing that while machine learning models were unaffected, deep learning models showed reduced performance at higher altitudes due to feature loss. These findings emphasize the potential of UAVs as accessible, scalable, and sustainable tools for real-time agricultural monitoring in precision farming. © 2025 IEEE.

关键词： Drones

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Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives

Integrated Bi-Manual Motion Generation and Control shaped fo...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jonathan Vorndamme João Carvalho Riddhiman Laha Dorothea Koert Luis Figueredo Jan Peters Sami Haddadin Department of Computer Engineering Technische Universität München (TUM) TUM School of Computation Information and Technology (CIT) Munich Institute of Robotics and Machine Intelligence (MIRMI) Germany Intelligent Autonomous Systems group Technische Universitat Darmstadt (TUD) Germany Center for Cognitive Science TUD Germany

ISBN: (纸本)9798350309805

This work introduces a novel cooperative control framework that allows for real-time reactiveness and adaptation whilst satisfying implicit constraints stemming from proba-bilistic/stochastic trajectories. Stemming from task-oriented sampling and/or task-oriented demonstrations, e.g., learning based on motion primitives, such trajectories carry additional information often neglected during real-time control deployment. In particular, methods such as probabilistic movement primitives offer the advantage to capture the inherent stochasticity in human demonstrations - which in turn reflects human's understanding about task-variability and adaption possibilities. This information, however, is often poorly exploited and, mostly, used during offline trajectory planning stage. Our work instead introduces a novel real-time motion-generation strategy that explicitly exploits such information to improve trajectories according to changes in the environmental condition and robot task-space topology. The proposed solution is particularly well-suited for bi-manual and coordinated systems where the increased kinematic complexity, tightly-coupled constraints and reduced workspace have detrimental effects on the manipula-bility, joint-limits, and are even capable of causing unstable behavior and task-failure. Our methodology addresses these challenges, and improves performance and task-execution by taking the confidence range region explicitly into account whilst maneuvering towards better configurations. Furthermore, it can directly cope with different closed-chain kinematics and task-space topologies, resulting for instance from different grasps. Experimental evaluations on a bi-manual Franka panda robot show that the method can run in the inner control loop of the robot and enables successful execution of highly constrained tasks.

关键词： Trajectory planning Robot kinematics Humanoid robots Kinematics Probabilistic logic Real-time systems Trajectory

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Broadband focusing of acoustic plasmons in graphene with an applied current

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Physical Review B 2021年第16期104卷 L161409-L161409页

作者： Michael Sammon Dionisios Margetis E. J. Mele Tony Low Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Institute for Physical Science and Technology and Department of Mathematics and Center for Scientific Computation and Mathematical Modeling University of Maryland College Park Maryland 20742 USA Dept. of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA

Nonreciprocal plasmons in current-driven, isotropic, and homogenous graphene with proximal metallic gates are theoretically explored. Nearby metallic gates screen the Coulomb interactions, leading to linearly dispersive acoustic plasmons residing close to its particle-hole continuum counterpart. We show that the applied bias leads to spectral broadband focused plasmons whose resonance linewidth is dependent on the angular direction relative to the current flow due to Landau damping. We predict that forward focused nonreciprocal plasmons are possible with accessible experimental parameters and setup.

关键词： Plasmons Surface plasmon polariton Graphene

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Low-overhead pieceable fault-tolerant construction of logical controlled-phase circuit for degenerate quantum code*

Low-overhead pieceable fault-tolerant construction of logica...

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2020 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2020

作者： Lin, Chen Yang, Guowu Wu, Jinzhao Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning530006 China

ISBN: (纸本)9781728170046

We propose a non-transversal but pieceable fault-tolerant conversion circuit that is used to convert encoded information between five-qubit code and seven-qubit CSS code. Since a syndrome extraction circuit requiring fewer ancillary qubit resources would facilitate the realization of large-scale quantum computations, we further adapt a flag-assisted fault-tolerant syndrome measurement scheme to reduce the cost of ancillary preparation. Numerical simulations are also performed to further analyze the performance of our conversion method. © 2020 IEEE.

关键词： Timing circuits

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ESAE: Evolutionary Strategy-Based Architecture Evolution 14th

ESAE: Evolutionary Strategy-Based Architecture Evolution

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based Architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Machine Learning-Enabled Optimization of a Graphene Coated Terahertz Metasurface for Isoquercitrin Biosensing

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Journal of The Electrochemical Society 2025年第6期172卷 067518-067518页

作者： Selvan C Jothi Prabha Appadurai Habib Kraiem Rathish C R School of Computer Science and Engineering REVA University Bengaluru Karnataka India Department of Computer Science and Engineering (AI & ML) Kakatiya Institute of Technology & Science Warangal 506015 Telangana India Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Computer Science and Engineering New Horizon College of Engineering Bengaluru India

This study presents a biosensor architecture for high-sensitivity detection of isoquercitrin, a biomedically relevant flavonoid compound. Full-wave electromagnetic simulations conducted using COMSOL Multiphysics reveal exceptional sensing performance, achieving a maximum sensitivity of 500 GHz RIU⁻¹ within the frequency range of 0.701–0.716 THz. The sensor demonstrates consistent absorption enhancement ranging from 42.65% to 44.96% corresponding to refractive index variations from 1.333 to 1.385 RIU, exhibiting a linear frequency-refractive index relationship with high correlation (R² = 98.309%). Parametric optimization studies reveal substantial performance improvements through graphene chemical potential modulation (absorption range: 17.75%–54.11%) and incident angle optimization (absorption up to 127.07%). A one-dimensional convolutional neural network (1D-CNN) machine learning model was successfully implemented to predict sensor behavior with exceptional accuracy (R² = 99%–100%), facilitating real-time biosensing applications. Comparative analysis with existing sensor technologies confirms the competitive sensitivity and practical viability of the proposed design for label-free biomedical diagnostics in the terahertz frequency regime.

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A novel ensemble method for enhancing Internet of Things device security against botnet attacks

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Decision Analytics Journal 2023年 8卷

作者： Arshad, Amina Jabeen, Maira Ubaid, Saqib Raza, Ali Abualigah, Laith Aldiabat, Khaled Jia, Heming Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Department of Management Information Systems Ajloun National University Jordan School of Information Engineering Sanming University Sanming 365004 China

The growing number of connected Internet of Things (IoT) devices has led to the daily growth of network botnet attacks. The networks of compromised devices controlled by a single entity can be used for malicious purposes such as denial of service distributed IoT botnet attacks and theft of personal information. The weak security measures of many IoT devices make them easy targets for compromise and inclusion in botnets. In this research, we propose a system for detecting botnet attacks. We develop an ensemble learning system to detect botnets in network traffic with high-performance scores. The system will analyze the traffic and identify any suspicious behavior that may indicate the presence of a botnet. For this purpose, we use the benchmark CTU-13 dataset to build the applied machine learning and deep learning techniques for comparison. We propose a novel ensemble technique, K-neighbors, Decision tree, and Random forest (KDR), to achieve high performance for botnet attack detection. Study results show that the proposed KDR gives 99.7% accuracy in 12.99 s. Hyperparameter optimization and k-fold cross-validation are employed to substantiate the performance. Our research study contributes to the body of knowledge on the detection of botnet attacks and provides a practical solution for securing IoT devices against botnet attacks. © 2023 The Author(s)

关键词： Botnet attacks Cyber security Deep learning Ensemble learning IoT devices Network security

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Broadband switchable infrared absorbers using phase-change materials

arXiv

引用

arXiv 2023年

作者： Emrose, Md Tanvir Payne, Emily L. Chenglong, Y.O.U. Veronis, Georgios School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803 United States Center for Computation and Technology Louisiana State University Baton RougeLA70803 United States Department of Physics Brigham Young University - Idaho RexburgID83460 United States Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States

We introduce multilayer structures with the phase-change material Ge2Sb2Te5 (GST) for use as broadband switchable absorbers in the infrared wavelength range. We optimize both the material composition and the layer thicknesses of the multilayer structures, in order to maximize the difference between the absorption for GST in its crystalline phase and the absorption for GST in its amorphous phase in the wavelength range of interest. We show that in the optimized structures near perfect absorption can be switched to very low absorption in a broad wavelength range by switching GST from its crystalline to its amorphous phase. Our optimized lithography-free structures have better performance than harder-to-fabricate three-dimensional structures. Our results could pave the way to a new class of broadband switchable absorbers and thermal sources in the infrared wavelength range. © 2023, CC BY.

关键词： Phase change materials

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Towards a Quantum Algorithm for the Incompressible Nonlinear Navier-Stokes Equations

Towards a Quantum Algorithm for the Incompressible Nonlinear...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Muralikrishnan Gopalakrishnan Meena Yu Zhang Weiwen Jiang Youzuo Lin Stefanie Günther Xinfeng Gao National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA Theoretical Division Los Alamos National Laboratory Los Alamos NM USA Department of Electrical and Computer Engineering George Mason University Fairfax VA USA School of Data Science and Society University of North Carolina at Chapel Hill Chapel Hill NC USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville VA USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In this work, we present novel concepts for quantum algorithms to solve transient, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the incompressible Navier-Stokes equations as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts solving nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. We propose a new framework based on matrix product states (MPSs) and matrix product operators (MPOs), in addition to the Krylov subspace methods. For example, the solution variables of the Navier-Stokes equations are represented by MPSs, and the linear and nonlinear terms are processed by MPOs. The time evolution of the operators is attained by a fast-forwarding algorithm using Krylov subspace methods. Furthermore, we discuss various techniques for efficient encoding of MPSs, measurement reduction for MPOs, and use of tensor operations to treat multi-variate, multi-physics characteristics of Navier-Stokes.

关键词： computers Quantum algorithm Tensors Fluids Partial differential equations Encoding Transient analysis Nonlinear systems Physics Navier-Stokes equations

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