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共 10631 条记录，以下是41-50 订阅

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Enhancing Performances of Deep Neural networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable sensors 10

Enhancing Performances of Deep Neural Networks with Ensemble...

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10th International conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing. © 2025 IEEE.

关键词： Deep neural networks

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Research on Gear Fault Diagnosis Method Based on CNN-Transformer Modeling

Research on Gear Fault Diagnosis Method Based on CNN-Transfo...

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International conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Han, Xiangbo Cai, Lulu Wang, Huahua Ma, Zhenliang Gao, Mengqi Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing100144 China Jiangsu Industrial Innovation Center of Intelligent Equipment Co. Ltd. Liyang213300 China

ISBN: (纸本)9789819639687

Aiming at the problem of complex gear operating environment, difficult fault feature extraction and low accuracy, a deep learning fault diagnosis method based on the combination of convolutional neural network (CNN) and Transformer, which is based on three sensors, three channels, and multivariate data processing fusion, is proposed. The method firstly processes the raw data notational by Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT), merges the features after feature extraction of these two data processing methods respectively, and then utilizes the CNN-Transformer neural network model for fault diagnosis of gears. The two dimensions of data processing and deep learning model fusion solve the problem of complementary local and global information processing in fault diagnosis, and realize the accurate classification of gear faults. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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Analysis and design of pervasive computing intelligent surveillance system integrating optical sensors and behavioral analysis for industrial internet 2

Analysis and design of pervasive computing intelligent surve...

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2nd International conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yao, Di Luo, Chenyao Cheng, Weiwei Song, Huaju Beijing100083 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Environmental Science NanJing XiaoZhuang University Nanjing210017 China

ISBN: (纸本)9781510689053

Nowadays, the surveillance system performing as an indispensable part for modern industrial Internet has attracted much attention. With the continuous progress of microprocessor technology, sensor technology, particularly optical sensors, and network technology, it has entered the era of pervasive computing, which leads to the breakthrough of the surveillance system and the emergence of intelligent environment safety monitoring systems. However, the intelligence of surveillance systems is still a significant challenge. In this paper, we propose a surveillance system for detecting and recognizing people based on their behaviors from images or video sequences using computer vision techniques and optical sensor data, which is implemented by designing the system architecture through a hierarchical structure, applying key techniques such as background subtraction, modeling, and optical sensing analysis, and experimentally processing and testing a variety of behavioral samples. The alarm information generated by the proposed surveillance system aided by pervasive computing system can be delivered to remote terminals. Experimental results reveal that the environmental monitoring and alarm information can be processed in real time with devices, such as cell phones, telephones, and PCs. Therefore, the proposed approach can improve the performance of the surveillance systems. © 2025 SPIE.

关键词： Alarm systems

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th International conference on ENTERprise information Systems, CENTERIS 2024 - 12th International conference on Project MANagement, ProjMAN 2024 - 14th International conference on Health and Social Care information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-Than-ideal performance and restricted applicability. This research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-The-Art methods. This demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 The Author(s).

关键词： Convolutional neural networks

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Semantic sensor Network Ontology for Forest Fire Management Decision Support

Semantic Sensor Network Ontology for Forest Fire Management ...

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2025 International conference on intelligent Control, Computing and Communications, IC3 2025

作者： Kirubakaran, S. Hussein, Ramy R. Ramakrishna, Thanneeru Venkata Duraipandi Reddy, Gurram Vijendar Sandeep, C.S. Cmr College of Engineering & Technology Department of Cse Telangana Hyderabad India College of technical engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Information Technology Pulloor Tamil Nadu Kariapatti626115 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Jawaharlal College of Engineering and Technology Department of Ece Kerala Palakkad India

ISBN: (纸本)9798331527495

Forests are essential resources for each country, since they are essential to maintaining environmental equilibrium. The negative effects of deforestation, especially those caused by forest fires, highlight the need of efficient management and monitoring. When determining the risk of a fire, giving warnings, and assigning resources for emergency management, fire weather indices are essential measures. Utilising sensor networks-Semantic sensor networks (SSN) in particular-improves the ability to gather data on a range of meteorological factors, including temperature, relative humidity, and wind speed. processing data streams produced by these sensors poses difficulties for estimating fire weather indicators. This methodology guarantees the system's flexibility in responding to changing circumstances, facilitating reasoning and preserving the open world's generic semantics as established by all participating ontologies. The suggested fire management system contributes to effective forest fire prevention and control by functioning in accordance with the current circumstances © 2025 IEEE.

关键词： Risk management

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Performance Optimization of intelligent Perception System in Maritime Traffic Sensing and Management

Performance Optimization of Intelligent Perception System in...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Daoke Li Navigation College of Fujian Chuanzheng Communication College Fuzhou China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The sea surface is often accompanied by a complex and fluctuating information environment. How to achieve the efficient and accurate water traffic management and perception is an important research branch of computer-aided maritime informatization. Therefore, this study analyzes the application and performance optimization of intelligent perception systems in water traffic management. First, this study expands the application of sensors in the ship hull by optimizing the perception of objects inside the ship. Then, a perception system with wide coverage and strong data processing capabilities is designed for the deployment of external sensor networks and the perception range of wireless sensor networks. At the same time, in response to the challenges of collecting, transmitting and analyzing complex dynamic data of ships, this paper proposes efficient data cleaning, fast data transmission technology and active routing protocol. In the experimental part, this study also verified the effectiveness of the designed system in ship dynamic trajectory prediction (GPS sensor information analysis) and conflict detection through MATLAB simulation.

关键词： Wireless sensor networks Data analysis Accuracy sensor systems Routing protocols Data communication Marine vehicles intelligent sensors Optimization Global Positioning System

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Agri-Guard: IoT-Based Network for Agricultural Health Monitoring with Fault Detection 10

Agri-Guard: IoT-Based Network for Agricultural Health Monito...

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10th International conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Singh, Kushagra Momin, Kafil Abbas Nishal, M. Sultania, Chinmay Rao, Madhav Dept. of Electronics and Communication Engineering International Institute of Information Technology Bangalore Karnataka India

ISBN: (纸本)9789897587504

Agricultural sector is increasingly adopting advanced technologies to enhance crop productivity and sustainability. Precision agriculture leverages IoT devices, sensors, and data analytics to monitor and manage various environmental parameters, addressing challenges such as global food demand, climate change, and resource optimization. Previous research has demonstrated the efficacy of wireless sensor networks (WSNs) and remote sensing technologies in improving irrigation efficiency and early disease detection. However, these systems often assume that all components continue to operate, thereby offering an incomplete view. This study presents an advanced agricultural monitoring system referred to as Agri-Guard that integrates a wide array of sensors to measure temperature, humidity, soil moisture, and gases like CO2, methane and ammonia. By utilizing an ESP8266 microcontroller and IoT connectivity, the system ensures seamless data transmission and real-time processing. Additionally, a centralized hub, equipped with a Raspberry Pi 5 and a thermal camera, enhances the detection of crop anomalies, and an inoperative sensor hub. The sensor hub in the form of a cone is optimally designed to detect environmental parameters besides being rainproof. The proposed Agri-Gaurd setup clearly demonstrated the lack of manure and water from the sensors’ data, whereas thermal imaging showcased the classification of 92.7% between a dead and alive plant. The anomaly between an operating and non-operating Agri-cone was found to be in complete agreement (100%). The proposed system represents a significant improvement over existing solutions, empowering farmers with precise data and faulty hub detection, leading to quick recovery and more sustainable farming practices. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Agricultural robots

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Systematic Study on Improving the All-Weather Monitoring Accuracy of Horse Walking Rooms Using Signal processing Technology

Systematic Study on Improving the All-Weather Monitoring Acc...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Sun, Zhuo Yan, Xin Wuhan Business University Wuhan430056 China

ISBN: (纸本)9798331504205

This study proposes an all-weather monitoring system for horse walking rooms based on signal processing technology. The fast Fourier transform (FFT) algorithm is used to optimize the monitoring accuracy and achieve efficient and accurate real-time monitoring. The system consists of a sensor array, a signal processing module, and a data analysis module, which can monitor key parameters such as temperature, humidity, and motion status. First, the system collects the original signals of multiple sensors, suppresses noise through a filtering algorithm, and extracts effective information;then, the FFT algorithm is used to perform frequency domain analysis on the signal, which improves the efficiency and accuracy of extracting key signal features. The simulation test results show that the monitoring error of the optimized system is reduced to 0.85% under typical conditions, which is significantly better than the 1.8% of the traditional method. In high-noise and complex environments, the system signal interference reduction rate is 45%, and the robustness is improved by about 30%. The experiment verifies the monitoring accuracy and stability of the system under all-weather conditions, providing strong support for the intelligent management of horse walking rooms. © 2025 IEEE.

关键词： Fast Fourier transforms

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Advance Infratructure Maintenance Using Parallel Data processing with Visual Analytics and IoT

Advance Infratructure Maintenance Using Parallel Data Proces...

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2025 International conference on Computing for Sustainability and intelligent Future, COMP-SIF 2025

作者： Muneshwara, M.S. Swetha, M.S. Prasad, Meghana Preethi, K.R. Pruthvi Shetty, S. Reddy, R. Nithish Kumar Bms Institute of Technology & Management Department of Computer Science and Engineering Bengaluru India Bms Institute of Technology & Management Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

A very important strategy for strengthening the security, durability, and efficiency of civil infrastructure is predictive maintenance. This research explores how to assess critical assets such as highways, bridges, and dams by integrating sensor networks, machine learning algorithms, and structural health monitoring techniques. Apache Kafka's realtime data streaming allows for efficient handling of large data volumes, and a Flutter-Dart interface provides easy access to predictive insights. Using predictive analytics enables stakeholders to predict their need for maintenance, improves their schedules, and lowers the probability of costly breakdowns. This study underscores the potential for predictive maintenance in making infrastructure management resilient against downtime and maximizing returns. © 2025 IEEE.

关键词： Predictive maintenance

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Adaptive Spider Monkey Optimization Algorithm Based Clustering and Routing for Cognitive Radio sensor networks

Adaptive Spider Monkey Optimization Algorithm Based Clusteri...

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International conference on 6G Communications Networking and Signal processing, SGCNSP 2023

作者： Nijaguna, G.S. Babu, J. Ananda Ramya, R. Saravanan, V. Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India Department of ISE Malnad College of Engineering Hassan India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Thandalam India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai Thandalam India

ISBN: (纸本)9789819778751

Cognitive radio sensor networks (CRSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve network lifetime. In many CRSN applications, node mobility poses challenges due to changing network topology. In this research, the Adaptive Spider Monkey Optimization (ASMO) algorithm is proposed for obtaining the energy efficient Cluster Head (CH) selection and routing in CRSN. The consumption of energy in CRSN is achieved by the optimal routing method by utilizing the optimization algorithm. The optimization-based clustering and routing approach is developed for designing the optimal transmission path by CH to destination. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network lifetime (NL), and end-to-end delay (EED) are employed to evaluate suggested methodology. Comparative analysis shows that the ASMO methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms

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