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Design of intelligent Image Recognition Algorithms Based on Optical sensors and Generative Adversarial networks

Design of Intelligent Image Recognition Algorithms Based on ...

引用

2024 International conference on Power, Electrical Engineering, Electronics and Control, PEEEC 2024

作者： Ye, Cong Suzhou College of Information Technology Department of Big Data and Internet Jiangsu215200 China

ISBN: (纸本)9798350378917

This paper presents the design of an intelligent image recognition algorithm based on optical sensors and Generative Adversarial networks (GAN). In today's digital era, the growth of image data is increasing exponentially, which puts higher requirements on image recognition algorithms. Optical sensors, as an important data acquisition tool, play an important role in the field of image processing. Generative Adversarial networks, on the other hand, is a powerful deep learning model that can learn and generate images with realistic features. In this paper, an intelligent image recognition algorithm is designed by combining optical sensors with GAN, which realizes efficient recognition and classification of images by taking full advantage of data acquisition and feature learning. This study first introduces the basic principles of optical sensor technology and its application in image recognition. With the development of technology, optical sensors have continuously improved the accuracy and speed of image acquisition, and have been widely used in industrial, medical and other fields. Then, this paper elaborates the working principle of Generative Adversarial networks (GAN) and its importance in image *** realizes the ability of learning from data and generating realistic images by means of competitive learning, which brings new breakthroughs in the field of image recognition. An intelligent image recognition algorithm design based on optical sensor and GAN is proposed. The algorithm realizes the effective extraction and learning of image features through the steps of data preprocessing, optical sensor feature extraction and GAN feature learning. In the process of algorithm design, the features and advantages of optical sensors and GAN in image processing are fully considered, which makes the algorithm have high recognition accuracy and robustness. Finally, this paper verifies the performance of the proposed algorithm in image classification and recognition

关键词： Generative adversarial networks

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

Performance Optimization of Intelligent Perception System in...

引用

2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Li, Daoke Navigation College Fujian Chuanzheng Communication College Fuzhou350007 China

ISBN: (纸本)9798331509828

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. © 2025 IEEE.

关键词： intelligent perception Maritime traffic sensing Performance optimization Sensing and management

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Research on Real-Time Monitoring and Early Warning System of Lightning Protection and Grounding System Based on Computer intelligent sensor 2

Research on Real-Time Monitoring and Early Warning System of...

引用

2nd IEEE International conference on Image processing and Computer Applications, ICIPCA 2024

作者： Liu, Jian College of Science Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9798350360240

In the study of real-time monitoring and early warning of the application of intelligent sensors to lightning protection and grounding system, an innovative work is to combine Graph Neural networks (GNN) and Multi-Objective Evolutionary Algorithm based on Decomposition, MOEA/D) combined to build a new hybrid model called Graph Evolutionary networks (GEN) to address the challenges in monitoring complex systems. With its efficient processing of network structure data, GNN can effectively capture and analyze the topological relationship and real-time data flow between lightning protection and grounding system sensors, and achieve accurate evaluation and prediction of system status. In view of the fact that the real-time monitoring and early warning of lightning protection and grounding system involves a number of mutually dependent objectives, such as response speed, monitoring accuracy and energy consumption, the researchers introduced the MOEA/D algorithm to seek the balance point between the objectives by decomposing the multi-objective problem into multiple single-objective sub-problems for synchronous optimization, so as to ensure the overall optimal operation of the system. The GEN model successfully combines the advantages of GNN and MOEA/D: GNN is used to analyze the complexity of sensor networks and obtain critical information in real time;MOEA/D is used to coordinate and optimize multiple performance indicators in system monitoring and early warning. Therefore, the GEN model can not only realize real-time and accurate system condition monitoring, but also make reasonable tradeoff decisions between different performance objectives, which significantly improves the safety and reliability of lightning protection and grounding systems. This interdisciplinary innovation opens up a new technical path for the application of intelligent sensors in the field of complex system monitoring and early warning, which has wide practical value and far-reaching industry develo

关键词： Smart sensors

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Fault detection of highway flow monitoring sensors based on grey correlation degree and neural network 6

Fault detection of highway flow monitoring sensors based on ...

引用

6th International conference on intelligent Control, Measurement and Signal processing, ICMSP 2024

作者： Zheng, Jianfeng Zhang, Yi Chen, Shawen Zhejiang Expressway Information Engineering and Technology Co. Ltd. Hangzhou310000 China

ISBN: (纸本)9798331516550

Highway traffic monitoring sensors require real-time collection of a large amount of data, including traffic flow, vehicle speed, lane occupancy rate, etc. These data are not only large in volume, but also highly complex and dynamic, and are affected by various factors such as weather, road conditions, and vehicle types, which increase the difficulty of fault detection. This paper proposes a research method for fault detection of highway flow monitoring sensors based on grey relational analysis and neural networks. By using grey relational analysis to evaluate the correlation between highway traffic data and its influencing factors, the close relationship between key influencing factors and traffic has been determined. Furthermore, the BP neural network model is introduced to model and predict the output data of the flow monitoring sensor, and identify whether the sensor has malfunctioned. The experimental results show that the detection method combining grey correlation degree and neural network can effectively improve the accuracy and timeliness of fault detection, providing strong guarantee for the stable operation of highway flow monitoring system. © 2024 IEEE.

关键词： Neural networks

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ESMDP: Encrypted ML Framework for Secure Medical Data processing 3rd

ESMDP: Encrypted ML Framework for Secure Medical Data Proces...

引用

3rd International conference on Network Security and Blockchain Technology

作者： Reddy, B. Pradeep Kumar Chatterjee, Ayantika Indian Instutite Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9789819780501;9789819780518

In this age of Internet of Things (IoT), remote health monitoring and out-of-hospital treatment is not a distant dream. Wearable medical sensors and smartphones in the current IoT-driven environment allow for real-time monitoring of important health markers, with data quickly sent to distant specialists for analysis. Moreover, due to the onset of technological advancements like Electronic Health Records (EHR), IoT-enabled devices, Public Open health Data (POD), Linked Opened Data (LOD), remote health monitoring, vast quantities of information created in this domain as big data in health care. intelligent storage and analysis of this big data may lead to generate critical insights for future predictions. The storage challenge of big data can be mitigated by the use of cloud computing. However, processing of Personal Health information (PHI) on remote medical servers or cloud raises serious concern about the security of critical PHI. One immediate solution is for the information proprietors to transmit and store their delicate information in encrypted form. However, traditional encryption schemes do not support processing on that encrypted form of data. To take full advantage of cloud computing and outof-hospital treatment, there should be a solution to process on that encrypted form of data directly. In this paper, we propose a framework which supports secure data transmission from medical sensors to distant cloud server or remote medical organizations, secure processing on that data to provide distant medical treatments and perform secure machine learning-based analytics on such sensor-generated datasets. For this secure processing, we use Fully Homomorphic Encryption(FHE) scheme, which supports direct processing on encrypted data. Our implementation shows the proposed secure framework is capable of disease detection remotely in an encrypted domain confirming PHI data confidentiality. The framework also supports performing encrypted machine learning algorithm on clo

关键词： IoT Security eHealth Fully homomorphic encryption (FHE)

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Spatio-Temporal Synchronized Retention networks for Methane Concentration Prediction 9

Spatio-Temporal Synchronized Retention Networks for Methane ...

引用

9th International conference on intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Zhaozhao Dong, Wangzhi College of Computer Science and Technology Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9798350376548

Methane accidents are one of the most dangerous accidents that threaten the safety and production of coal mines, therefore, how to accurately predict the methane concentration in the future period by utilizing the spatio-temporal data of methane concentration collected by sensors is crucial to reduce the probability of accidents. Usually, there are multiple methane concentration sensors arranged in the working face of underground coal mines, and the data collected by these sensors have strong spatio-temporal correlation. Existing methods either rely on the data from a single sensor for prediction or model the temporal and spatial characteristics of the data from multiple sensors separately by two different modules, which makes it difficult to explore the complex spatio-temporal correlation of the data from multiple sensors. Therefore, this article proposes a new model of encoder-decoder architecture based on retention mechanism. By connecting the same nodes at adjacent time steps, allowing the temporal and spatial information to freely propagate, and then synchronously modeling the temporal and spatial relationships with the parallel computing capability of the retention mechanism, the efficiency and accuracy of the prediction are enhanced. © 2024 IEEE.

关键词： Coal mines

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Progress in Artificial Intelligence Applications Based on the Combination of Self-Driven sensors and Deep Learning 4

Progress in Artificial Intelligence Applications Based on th...

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4th International conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Wan, Weixiang Sun, Wenjian Zeng, Qiang Pan, Linying Xu, Jingyu University of Electronic Science and Technology of China Electronics & Communication Engineering Chengdu China Yantai University Electronic and Information Engineering Shandong Yantai China Zhejiang University Computer Technology Zhejiang Hangzhou China Trine University Information Studies PhoenixAZ United States Northem Arizona University Computer Information Technology FlagstaffAZ United States

ISBN: (纸本)9798350384062

In the era of the Internet of Things, developing a smart sensor system with sustainable power supply, easy deployment, and flexible use has become a challenging problem. Traditional power supplies, prone to frequent replacements or requiring charging during usage, hinder the advancement of wearable devices. The contact-to-separate friction nanogenerator (TEN G), composed of polychotomy ethylene (PTFE) and aluminum (AI) foils, addresses this issue by harvesting human motion energy through body movement arrangement. This energy is then utilized to monitor human motion posture by detecting changes in output electrical signals. In 2012, Academician Wang Zhonglin and his team invented the triboelectric nanogenerator (TENG), which operates as a self-driven sensor primarily powered by Maxwell displacement current. This enables direct conversion of mechanical stimulation into electrical signals during action, making it suitable for self-powered sensor applications. TENG-based sensors boast a simple structure and high instantaneous power density, providing a crucial tool for constructing intelligent sensor systems. Additionally, when combined with machine learning characteristics such as low cost, short development cycles, robust data processing, and predictive capabilities, the processing of the numerous electrical signals generated by TENG has a significant impact. The integration of TENG sensors is poised to drive the rapid evolution of intelligent sensor networks in various fields such as transportation, security, water conservancy, and construction, where urban sound management is essential. The method of seamlessly integrating sensors with Internet access is implemented using the NetBox network development platform, following these steps: 1) Initially, read the default parameter settings from pre-existing storage media, including parameters like IP address, subnet code, gateway address, sensor input and output types, and sensor range. These settings can be modified onl

关键词： Ethylene

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Leveraging Residual Deep Neural networks and Multi-Device sensors for Heterogeneous Activity Recognition 47

Leveraging Residual Deep Neural Networks and Multi-Device Se...

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47th International conference on Telecommunications and Signal processing, TSP 2024

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

ISBN: (纸本)9798350365597

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection. © 2024 IEEE.

关键词： Smartphones

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Research on intelligent warning mechanism of accurate personnel positioning system based on fuzzy neural network

Research on intelligent warning mechanism of accurate person...

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2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Tang, Lijun Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9781510677685

In order to improve the intelligent level of accurate personnel positioning system to deal with underground emergencies, this paper shows an intelligent warning mechanism based on fuzzy neural network. The fuzzy neural network is a multi-input single-output model structure model, which has four layers, input layer, fuzzification layer, fuzzy inference layer and output layer. The intelligent warning mechanism standardizes the data of sensors in the location of the personnel label, and then uses the trained fuzzy neural network to predict. Before the disaster accident, the staff is automatically reminded to evacuate the dangerous area as soon as possible through the sound and light alarm function of the personnel label. The test shows the intelligent warning mechanism can remind the underground staff to evacuate the dangerous area as soon as possible before the disaster accident. Compared with the 70% prediction accuracy of the single sensor warning mechanism, the intelligent warning mechanism using fuzzy neural network can achieve 95% prediction accuracy. © 2024 SPIE.

关键词： Fuzzy neural networks

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Poster Abstract: Representation Learning from Multimodal sensor Data with Maximally Correlated Autoencoders 21

Poster Abstract: Representation Learning from Multimodal Sen...

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21st ACM/IEEE International conference on information processing in sensor networks (IPSN)

作者： Ma, Fei Gu, Weixi Ni, Shiguang Zhang, Lin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China China Acad Ind Internet Dept Intelligent Ind Res Beijing Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781665496247

With the development of sensing technology, multiple sensors are widely used in Internet of Things (IoT) devices. A key challenge is to learn feature representations from multimodal sensor data to combine the information of different sensors. Although progress has been made by previous works, the correlation between different sensors is still not well exploited, which may limit the performance of representation learning. To address this problem, we propose a deep learning approach to learn representations from multimodal sensor data with maximally correlated autoencoders (MCA). It can efficiently capture high dependence between different modalities at different feature levels. The learned representations are further used for the recognition task. Experimental results on the real-world RGB-D dataset demonstrate the high effectiveness of MCA.

关键词： Representation learning Performance evaluation Deep learning Correlation Multimodal sensors information processing Semisupervised learning

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