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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819994151

The proceedings contain 180 papers. The special focus in this conference is on Frontier computing. The topics include: Optimization of English Machine Translation Model Based on Neural Network;exploration on Text Detection Optimization Algorithm Based on Neural Network Technology;CO2 Emission Prediction of Vehicle Fuel Consumption Based on EMD-LSTM;application of data Fusion Algorithms in the data Processing of intelligent Greenhouses;intelligent Film and Television Communication Optical Technology Based on Network New Media;design of Heterogeneous database Encryption and Decryption System Based on data Mining;research and Implementation of Music Recommendation System Based on Particle Swarm Algorithm;design of High-Speed Multi-channel data Transmission System Based on Single-Chip Microcomputer;application of Distributed database System in Financial Management;Research on the Development of Japanese MOOC System Based on BP Neural Algorithm;research on Default Risk Prediction of Listed Companies’ Green Credit Based on Deep Learning Algorithm;improvement of Cloud Platform Utilization Based on Evaluation and Optimization of computing Resource Runtime Surplus Capacity;design and Implementation of Operation and Maintenance Monitoring Platform Based on data Mining Algorithm;civil Aviation Logistics Transportation Route Optimization Algorithm Based on A+ Algorithm;design of Interactive Japanese Translation System Based on Feature Extraction Algorithm;a Preliminary Study on Low-Carbon City Planning Methods Supported by the Cluster Optimization Algorithm;object Detection Algorithms in Embedded systems and their Applications;Application of Computer VR Technology in Digital Media System Design;discuss the Technical Application of Cloud data Center and Cloud Management Platform;Design of ARM-Based Program Automatic Shelling data acquisition System.

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The Wetland Map Validation for Ukraine 11

The Wetland Map Validation for Ukraine

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Shelestov, Andrii Yailymova, Hanna Yailymov, Bohdan Chyrkov, Artem Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Prosp Peremohy 37 UA-03056 Kiev Ukraine NASU SSAU Space Res Inst Glushkov Ave 404-1 UA-03187 Kiev Ukraine

ISBN: (纸本)9781665426053

This paper considers the topical issue of using data from a drone with a very high spatial resolution to solve the problems of validation of individual classes on land cover maps obtained at the national level with a 10-meter spatial resolution. In particular, this study validated the land cover map for the territory of Ukraine for 2020 and conducted a comparative analysis of orthophoto data with open Sentinel-2 satellite data, as well as validated some wetland locations from the classification map for Ukraine. Comparative analysis showed that orthophotoplane data have a correlation from 0.3 to 0.7 depending on the drone survey parameters with the Sentinel-2 satellite, and large wetlands on data with 10-meter spatial resolution are identified with an overall accuracy of 90%, but for more accurate details of wetlands contours correct will be the use of orthophotos data.

关键词： orthophotoplan drone data classification validation

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data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes 6

Data processing, analysis and Machine Learning of Electrical...

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6th ieee international conference on Image Processing, Applications and systems, IPAS 2025

作者： Germanicus, Rosine Coq De Wolf, Peter Klapetek, Petr Normandie Univ Unicaen Ensicaen CNRS CRISMAT UMR6508 Caen14000 France Bruker Nano Surfaces Santa BarbaraCA93117 United States Czech Metrology Institute Brno638 00 Czech Republic

ISBN: (纸本)9798331506520

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material's components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics. © 2025 ieee.

关键词： Network security

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Transferring Knowledge from Observed to Unknown: A data-Driven Lane Change Trajectory Prediction Strategy Based on the Concept of Few-Shot Learning 27

Transferring Knowledge from Observed to Unknown: A Data-Driv...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Zhao, Nanbin Lu, Yun Wang, Bohui Cheng, Xinyi Luo, Ruikang Song, Yaofeng Zhou, Yao Su, Rong

ISBN: (纸本)9798331505929

Existing data-driven lane change trajectory prediction methods lack the capability to achieve broad consistency in the complex and variable real-world traffic scenarios due to their reliance on training with pre-collected static datasets from fixed scenarios [13]. In reality, the scenarios vary widely, and there is no assurance of sufficient training data for each scenario. The primary challenge is to effectively apply knowledge from observed scenarios to unknown ones while ensuring the method adapts quickly to limited data without compromising prediction accuracy. Based on the aforementioned research gap, this paper develops a novel data-driven lane change prediction method which enhances the transfer of knowledge from observed to unknown scenarios using the few-shot learning (FSL) concept. Our proposed method enables a pre-trained LSTM model to be quickly deployed in new scenarios with only a few samples. Extensive experiments have been conducted using the NGSIM dataset to demonstrate the performance of our method. © 2024 ieee.

关键词： Zero-shot learning

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Research on the Control System and Method of Cutting Arms for Digital Twin Driven Excavation Equipment 9

Research on the Control System and Method of Cutting Arms fo...

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9th international conference on intelligent computing and Signal Processing, ICSP 2024

作者： Hu, Shouxin Yang, Yong Bao, Wenliang Nie, Yukun Taiyuan Institute Co. Ltd China Coal Technology & Engineering Group Shanxi Taiyuan030006 China Shanxi Tiandi Coal Mining Machinery Co. Ltd. Shanxi Taiyuan030006 China China National Engineering Laboratory for Coal Mining Machinery Shanxi Taiyuan030006 China

ISBN: (纸本)9798350376548

A digital twin driven cutting arm control system for excavation equipment is proposed to address issues such as low intelligence level, low excavation efficiency, and mismatch between equipment performance parameters and actual mine conditions. Firstly, the overall scheme of the digital control system for the cutting arm of excavation equipment was proposed using digital intelligent technology;Secondly, based on the feedback of physical information from sensors and servo hydraulic cylinders, a dual closed-loop control structure is designed, taking into account external disturbances and input saturation uncertainties. Using backstepping control theory and adaptive technology, a controller is derived to control the displacement and speed of the cutting arm. Finally, the on-site working face and virtual device twin models are restored using the Unity3D platform. Through intelligent perception of the environment and backend data analysis and processing, functions such as state detection, collision detection, and fault diagnosis are achieved. © 2024 ieee.

关键词： Digital control systems

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Detecting Deepfake Videos using Face Recognition and Neural Networks 1

Detecting Deepfake Videos using Face Recognition and Neural ...

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1st ieee international conference on Cognitive Robotics and intelligent systems, ICC - ROBINS 2024

作者： Murugan, Muthu Aravind Mathu, T. Priya, S Jeba Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350372748

Deepfake videos created using advanced artificial intelligence techniques, pose a significant threat to digital media credibility. This project introduces a holistic strategy for identifying these videos, incorporating face recognition, feature extraction, and an innovative deep-learning model. The methodology involves pre-processing video data, extracting facial features using the face recognition library, and training a neural network model on processed face-only videos. The project filters videos based on frame count, extracts face, and creates a curated dataset for the detection model. Face-only videos are loaded and pre-processed to train a custom neural network model, which combines a pre-trained ResNext CNN with an LSTM layer for temporal feature extraction. The model is trained using Adam optimization with a cross-entropy loss function, and after completion, it has an accuracy of 95% and is capable of differentiate between fake and real videos using a confidence score. © 2024 ieee.

关键词： Face recognition

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Vehicle Pseudonym Change Mechanism Based on Dynamic Game

Vehicle Pseudonym Change Mechanism Based on Dynamic Game

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2024 international conference on Mechatronics and intelligent Control, ICMIC 2024

作者： Zhao, Zheng Wang, Junren Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

intelligent Transportation systems (ITS), particularly vehicular ad-hoc networks, play a crucial role in improving road safety and traffic efficiency. However, transmitting vehicle location and identity data poses significant privacy risks, potentially allowing attackers to exploit this information. In this study, we developed a user-centric privacy model based on information entropy, enabling vehicles to monitor real-time location privacy levels. We also proposed a decision algorithm for pseudonym changes, dynamically identifying a cooperative set of vehicles for pseudonym alteration. Comparative experiments using SUMO and NS-3 simulations show that our scheme enhances location privacy while reducing pseudonym change frequency. This research provides valuable theoretical and technical foundations for ITS. © 2025 SPIE.

关键词： Vehicular ad hoc networks

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Advancing Road Condition Prediction in intelligent Transportation systems: A Deep Learning Approach Using Vehicle Vibration data 27

Advancing Road Condition Prediction in Intelligent Transport...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Arce-Saenz, Luis A. Izquierdo-Reyes, Javier Campero-Garcia, Luis A. Bustamante-Bello, Rogelio School of Engineering and Science Tecnologico de Monterrey Mexico City14380 Mexico

ISBN: (纸本)9798331505929

This study addresses the need for advanced road assessment methodologies in intelligent Transportation systems (ITS). Our investigation explores Deep Learning (DL) methodologies to enhance road condition prediction accuracy by analyzing vehicle vibration data. Through strategic positioning of Inertial Measurement Units (IMUs) within vehicles, our research aims to categorize road conditions into five distinct classes containing various anomalies and elements of the road. By proposing and testing 1-dimensional Convolutional Neural Network (CNN) architectures and diverse data inputs, we refine the classification process to optimize performance. We conduct a comparative analysis between DL models and traditional algorithms, highlighting the advantages of DL methodolo-gies. Notably, our DL models surpass traditional algorithms by increasing overall classification performance by 0.103, as evidenced by F1-scores obtained in our experiments. This study stands as a significant contribution to the field of ITS, offering and facilitating more precise and reliable road condition assessments. Key contributions include the development and assessment of tailored DL models for road condition classification, as well as comparative analysis with traditional algorithms. Through this research, we underscore the potential of DL techniques in significantly improving road condition prediction accuracy, thereby driving the evolution and success of ITS applications. © 2024 ieee.

关键词： Convolutional neural networks

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2023 ieee/ASME international conference on advanced intelligent Mechatronics, AIM 2023

2023 IEEE/ASME International Conference on Advanced Intellig...

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2023 ieee/ASME international conference on advanced intelligent Mechatronics, AIM 2023

ISBN: (纸本)9781665476331

The proceedings contain 187 papers. The topics discussed include: multi-objective co-design for mission-specific development of unmanned aerial systems;vertical take-off and landing fixed-wing designed for autonomous missions;soft continuum robot airbag integrated with passive walker for fall mitigation;a survey on the current trends and applications of design optimization for compliant and soft robotics;study on soft robotic pinniped locomotion;independent tendons increase stiffness of continuum robots without actuator coupling;AFM microcantilever with on-chip electrothermal and piezoelectric transducers: Z-axis control and standalone operation;and data-driven robust acoustic noise filtering for atomic force microscope image.

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13th international conference on Frontier computing, FC 2023

13th International Conference on Frontier Computing, FC 2023

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13th international conference on Frontier computing, FC 2023

ISBN: (纸本)9789819993413

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