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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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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...

引用

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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Cloud-based Returns Management Solutions in Logistics using K-Nearest Neighbors Approach 2

Cloud-based Returns Management Solutions in Logistics using ...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Flora, T. G. Arul Jeslin, J. Gnana Roychoudri, Subramani Vijayalakshmi, Kuppan Ponnaian, Geetha Velmurugan, S. KCG Coll Technol Dept Elect & Commun Engn Chennai Tamil Nadu India RMK Coll Engn & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Usha Rama Coll Engn & Technol Dept Comp Sci & Engn Vijayawada Andhra Pradesh India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Univ Saveetha Sch Engn Saveetha Inst Med & Tech Sci Dept Comp Sci & Engn Chennai Tamil Nadu India TJS Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Efficiently handling returns is one of the specific issues for the e-commerce logistics providers. Using the K-Nearest Neighbors (KNN) method, this research proposes a cloud-based returns management system that may improve the efficiency of managing return procedures. Traditional methods of returns management often include human intervention in the form of sorting and decision-making, which may result in unnecessary delays and higher expenses. This technology automates decision-making based on similarity criteria between returned products and current inventory, streamlining the returns process. The proposed method effectively assigns returned items to the most appropriate disposition, whether it restocks, refurbishing, or disposal, by using the scalability and computing capacity of cloud infrastructure. Cloud computing also allows for real-time data processing, which speeds up decision-making and makes operations more agile in general. The system may improve e-commerce logistics returns management via a case study and simulation analysis, demonstrating its efficacy and efficiency. It helps move the needle on how e-commerce operations use cloud computing and machine learning, which in turn enhances competitiveness and consumer happiness in the ever-changing online marketplace.

关键词： Inventory management Real-time data processing Operational agility Customer satisfaction Restocking Refurbishment

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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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Blockchain Design Elicitation for Healthcare: A VFT Based Approach

Blockchain Design Elicitation for Healthcare: A VFT Based Ap...

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Shekhar Shukla Rashmi G Shukla Anuja Agarwal Information Systems Area Indian Institute of Management Indore Indore India Technology Management Area MPSTME NMIMS University Mumbai India KJ Somaiya Institute of Management Mumbai India

The healthcare system in India, like in many other countries, is complex and influenced by various factors such as population size, the presence of structured and unstructured healthcare artifacts, social, governance and commercial disparities and structures. Existing research has highlighted several facets that are crucial for stimulating and improving the Indian healthcare system. These include primarily the triple t aspect of traceability, transparency, and trust along with aspects of identity management, incentive management, and standardization of processes and Blockchain technology, as a recent innovation, offers potential solutions for many of these facets as inherent features to the adopting systems. Furthermore, its implementation requires careful consideration and alignment with the specific needs, challenges, and regulatory frameworks of the Indian context. We use the Value-Focused Thinking framework in addressing the research context of adopting Blockchain Technology from a stakeholder viewpoint to elicit design-based recommendations.

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Empowering Anomaly Detection for Industrial Internet of Things with intelligent Edge computing and Decentralized Machine Learning

Empowering Anomaly Detection for Industrial Internet of Thin...

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2024 international conference on advanced Technologies for Communications, ATC 2024

作者： Pham-Nguyen-Hai, Anh Van-Thien, Luan Nguyen-Khanh, Thuat Le-Trung, Quan University of Information Technology Faculty of Computer Networks and Communications Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350353983

The Industrial Internet of Things (IIoT) aims to connect machinery, equipment, human resources, and all other relevant components in an industrial environment to achieve op-erational and management efficiency. Alongside the potential benefits of IIoT, this field also has many exploitable vulnerabilities. IIoT devices are often limited in resources, making implementing security solutions directly on these devices difficult. Instead, N et-work Intrusion Detection systems (NIDS) are typically deployed at edge nodes in IIoT systems. In this study, we build a NIDS on cloud infrastructure to simulate edge devices within the Swarm Learning architecture. In this system, we train two models, CNN and RNN, on the Edge-IIoTset dataset. The results show that the CNN and RNN models in the Swarm Learning architecture achieve good performance compared to Centralized Learning models and Federated Learning approaches. Specifically, our system achieves accuracy close to centralized machine learning models and outperforms Federated Learning approaches in binary and multi-label attack detection. Additionally, we evaluate the aggregators in Swarm Learning. The mean aggregator is stable, while the coord and geo aggregators achieve high accuracy in classes with few data points. © 2024 ieee.

关键词： Edge computing Edge-IIoTset dataset IIoT NIDS Swarm Learning

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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

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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Research on Demand Response data Compression Based on Neural Network under 5G Environment 5

Research on Demand Response Data Compression Based on Neural...

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5th ieee international conference on Power, intelligent computing and systems, ICPICS 2023

作者： Pan, Yuqing Qi, Bing Li, Bin Zhu, Enguo Liu, Yan Lu, Chunguang Wang, Chaoliang School of Electrical and Electronic Engineering North China Electric Power University Beijing China China Electric Power Research Institute Beijing China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China

ISBN: (纸本)9798350333442

In the context of 5G, data volume is exploding, and data transmission and processing are facing challenges. In this paper, a data compression algorithm based on neural network is proposed to solve the problem of large scale and high dimension of demand response data in the process of transmission and storage. By mapping demand response data to low dimensional space for representation, the algorithm effectively reduces the amount of data and improves the efficiency of data transmission and storage. The algorithm uses Long Short Term Memory network for data compression and restoration. The experimental results show that the data compression algorithm based on neural network has good compression effect and accuracy. At the same time, the algorithm has good versatility and adaptability, and can be applied to the compression and restoration of data sets of different types and sizes, providing an effective solution for efficient demand response data processing. © 2023 ieee.

关键词： Restoration

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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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