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An intelligent Classification Method for Vibration Signals Based on Random Forest 7

An Intelligent Classification Method for Vibration Signals B...

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7th IEEE International conference on Electronic information and Communication Technology, ICEICT 2024

作者： Zhen, Yao Li, Kun Yang, LiXia Anhui University School of Electronic and Information Engineering Hefei China

ISBN: (纸本)9798350384437

Due to the complexity of fiber optic vibration signals, using traditional classification methods to classify signals is inaccurate. Effective fiber optic sensor data classification has become an important research topic. Machine learning methods have been proposed for intelligent signal recognition and classification. This study aims to explore the use of machine learning methods to recognize and classify collected signals intelligently. The vibration signal data collected from the fiber optic sensors is first preprocessed, including removing trend terms and applying smoothing filters. Then, time-domain and frequency-domain features are extracted separately to construct a dataset for training and testing purposes. This paper focuses on the classification results of the Random Forest (RF) classifier and compares it with three other machine learning algorithms: Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Radial Basis Function Neural Network (RBF). The results show that the Random Forest model optimized through feature selection outperforms other models in terms of classification accuracy, recall rate, and F1 score. © 2024 IEEE.

关键词： Radial basis function networks

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RESEARCH ON MULTILAYER PERCEPTION AND CONTROL OF intelligent VEHICLES 19

RESEARCH ON MULTILAYER PERCEPTION AND CONTROL OF INTELLIGENT...

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19th IEEE International Computer conference on Wavelet Active Media Technology and information processing (ICCWAMTIP)

作者： Tao, Xie Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China

ISBN: (纸本)9781665493895

With the rapid development of computer and microelectronics technology, intelligent vehicle technology has become the latest development frontier of the automobile industry. In order to deeply explore and study the control problems of intelligent vehicles, we have carried out research in the following aspects: the construction of electric four-wheel steering and four-wheel drive mobile platform, the configuration of multi-layer sensing sensors on the mobile platform, and the Development and experimental validation of vehicle motion control algorithms. Based on the preliminary design of the mobile platform construction and sensor configuration scheme, and the completion of the power part construction, we study the motion model of the mobile platform, and add a PID controller to realize the stable control of the longitudinal and steering of the mobile platform. We have finished the structure construction of the mobile platform and the configuration of the sensors and have completed the preliminary debugging of the platform. And eventually we realize the basic performance of the platform, and verify the developed algorithm on the mobile platform.

关键词： intelligent vehicle Multilayer perception system PID control algorithm

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Two Kinds of New Flexible Eddy Current sensors for Curved Surface Defect Detection 1

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4th International conference on intelligent Systems in Production Engineering and Maintenance , ISPEM 2023

作者： Zhou, Xinhua Wu, Bin Liu, Xiucheng Wang, Yujue He, Cunfu Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (数字)9783031450211

ISBN: (纸本)9783031450204

The surface of special equipment is prone to crack after long-term service bringing great potential danger to the safe operation of the equipment. The detection of cracks and the distinction of the orientation of cracks are useful for analyzing the causes of cracks and then optimizing the structural design of special equipment components. This paper proposes two kinds of new flexible eddy current sensors. The sensors are designed based on double-layer flexible PCB circuits, all of which have 8 circular detection coils and specific array element arrangements. They can characterize the orientation of the defects while detecting the surface defects of the specimen through corresponding algorithm processing. Experiments are carried out using two eddy current sensors on curved specimens containing defects with different orientations to verify the sensors’ ability. They can detect the defects and identify their orientation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Surface defects

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A routing method with the approach of reducing energy consumption in WSNs with the Jellyfish Search (JS) optimizer algorithm and unequal clustering 13

A routing method with the approach of reducing energy consum...

引用

13th International conference on Computer and Knowledge Engineering, ICCKE 2023

作者： Gholami, Ehsan Hamidzadeh, Javad Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran

ISBN: (纸本)9798350330151

Wireless sensor networks are among the most common communication tools used in military and civilian fields. These networks consist of many tiny devices called sensor nodes. Each node has processing capability and different types of memory, transmitter, radio frequency receiver, and power supply. In addition, nodes house sensors and actuators. Batteries usually power sensor nodes and need to operate longer without their presence. In most cases, changing or recharging the battery for sensor nodes is very difficult or even impossible. Designing routing protocols in wireless sensor networks is challenging due to network limitations, with an emphasis on energy efficiency. The proposed method uses a hierarchical routing mechanism to optimize routing using asymmetric clustering. The sensor nodes are asymmetrically clustered in the proposed method, and the cluster heads are selected using the Jellyfish Search (JS) optimizer algorithm. The problem of optimal clustering is an optimization problem, so in this research, the Jellyfish Search algorithm is used to provide the most optimal clusters in routing. Due to the utilization of a search mechanism based on chaos theory in the Jellyfish Search optimization, the proposed method exhibits high accuracy in clustering and optimal routing within wireless sensor networks. Evaluations indicate that the proposed method achieves lower energy consumption and longer network lifetime compared to GWO, MO-PSO, WGWO, and HMBCR algorithms. © 2023 IEEE.

关键词： Energy utilization

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sensor Placement for Freeway Traffic State Estimation based on System Observability 12

Sensor Placement for Freeway Traffic State Estimation based ...

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12th International conference on Transportation and Traffic Engineering, ICTTE 2023

作者： Meng, Shaoyin Hu, Wenqin Zhang, Yuyu Zhao, Mingming Wang, Yibing Zhang, Yujie Luo, Xi Zhejiang Communications Investment Group Co. Ltd. ITS Branch Hangzhou China Institute of Intelligent Transportation Systems Zhejiang University Hangzhou China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Hangzhou Institute of Technology Xidian University Hangzhou China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781839539954

Traffic monitoring capability on smart freeways depends highly on the placement of traffic sensors. Observability is a quality measure of a dynamic system, which tells if the full information of state evolution of the dynamic system can be identified or reconstructed by observing the partial state information of the system. Only the sensor placement that ensures the system observability has practical significance, which guarantees all unobserved system state variables can be inferred from the ones observed with sensors. This paper focuses on a unit freeway stretch of a pair of on-ramp and off-ramp, studies its sensor placement schemes, and analyzes the corresponding observability properties. Combining techniques from both control theory and matrix theory, this paper attempts to develop a general and yet practical approach to traffic network observability analysis and observable sensor placement scheme generation. This paper evaluates the effectiveness of the proposed method for a real freeway stretch of multiple on-ramps and off-ramps. © The Institution of Engineering & Technology 2023.

关键词： Observability

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Multi-data source trajectory post-fusion based on sliding clustering in the Internet of Vehicles

Multi-data source trajectory post-fusion based on sliding cl...

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International conference on intelligent Traffic Systems and Smart City (ITSSC)

作者： Huang, Yichao Li, Jinglin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Sci & Technol Commun Networks Lab Beijing Peoples R China

ISBN: (数字)9781510652071

ISBN: (纸本)9781510652071;9781510652064

In the Internet of Vehicles project, multiple sensors are used to detect the trajectory of the vehicle. Due to the detection error of the sensors, the detection results of each group of sensors will not be completely consistent. At the same time, since the local clock of each sensor has a time difference, the detection time of different sensors cannot be strictly consistent. This leads to the problem of temporal and spatial inconsistency of trajectory data. In order to solve the problem of temporal and spatial inconsistency in the data, it is necessary to perform post-fusion processing on the trajectory data. This paper proposes an algorithm based on sliding clustering to post-fusion of multiple data source trajectories. The experimental results prove that the fusion trajectory data with lower error can be obtained in real time using this algorithm.

关键词： Internet of Vehicles multiple data sources trajectory clustering trajectory fusion

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Human Activity Recognition Based on a Cascade Network Architecture Composed of DFSMN and ACGAN 15

Human Activity Recognition Based on a Cascade Network Archit...

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15th International conference on intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Xie, Jinfeng Xiao, Liang Xing, Cangju College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9798350326178

Currently, human activity recognition (HAR) has been applied in many fields, including healthcare, smart homes, and sports tracking. The commonly-used LSTM-based methods have the disadvantage of having a relatively large number of parameters. This article proposes a HAR method based on a cascade architecture of deep feedforward sequential memory networks (DFSMN) and auxiliary classifier generative adversarial networks (ACGAN). In this research, a self-collected dataset was constructed through sampling data from inertial sensors worn by humans. DFSMN was used to extract temporal features from sensor data, and then in ACGAN, the extracted features were used to enhance the generator's ability to generate temporal data while using a discriminator for classification of activities. The model combining DFSMN with ACGAN can effectively improve the model's generalization ability and classification accuracy The experimental results indicate that this method can accurately recognize a wide range of human activities, such as walking, running, sitting, standing, going upstairs, going downstairs, and so on. This shows that the proposed method has a considerable application potential in practical scenarios. © 2023 IEEE.

关键词： Generative adversarial networks

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Research on VMD-BP intelligent Agriculture Corn Yield Prediction in the Internet of Things Environment 7

Research on VMD-BP Intelligent Agriculture Corn Yield Predic...

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7th International conference on Electronic information Technology and Computer Engineering (EITCE)

作者： Zhou, Jinghe Shi, Jinmei Cai, Jiajing Chen, Yunhao Chen, Mengyao Hainan Vocat Univ Sci & Technol Haikou Hainan Peoples R China

ISBN: (纸本)9798400708305

Starting from the Internet of Things technology, based on smart agriculture, this paper uses sensor networks to collect data, and puts forward opinions on maize growth monitoring based on rapid and accurate prediction of maize yield based on variational mode decomposition (VMD) and neural network (BP), to meet farmers' high requirements for the practicality, simplicity, and reliability of smart agriculture. An IoT-based corn yield prediction and analysis system was designed. The system collects environmental data (such as soil temperature, humidity, light intensity, etc.) during the growth process of corn by building a sensor network and cloud platform, and processes and analyzes the data in real time. Using the VMD-BP model algorithm, combining historical and real-time data, future corn yield predictions are made and presented to users through a visual interface. Experimental results show that the system has chief advantages in prediction accuracy and real-time and can effectively improve the efficiency and yield of maize planting. The research results of this paper will help promote the application and development of IoT technology in the agricultural field.

关键词： Variational Modal Decomposition BP Neural Network Production Forecast Temperature and Humidity sensor Photosensitive sensors CO2 sensor

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Efficient Segmentation for wsn Connectivity Technologies

Efficient Segmentation for wsn Connectivity Technologies

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2023 International conference on Power Energy, Environment and intelligent Control, PEEIC 2023

作者： Vivek, V. Savita Dore, Aruna Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

In wireless sensors networks, the chargeable capacity of sensor nodes serves as the primary energy source. In WSNs, grouping has emerged as a crucial strategy to save energy use and lengthen network lifespan. Transport energy is inversely correlated to the amount of distance across transmitter and destination nodes, which supports clustering. In this paper, we provide a novel fuzzy logic model for the cluster head (CH) choice, a key step in clustering in WSNs. The recommended model includes five characteristics to assess each node's suitability for CH status. The closeness to the starting place, volume, topographical suitability, and remaining power are some of these criteria. This fuzzy logic methodology is used to propose the Fuzzy Reasoning-based Energy-Efficient Grouping for WSN, with emphasis on establishing the shortest possible distance across CHs (FL-EEC/D). As a further metric of clustered methods' energy use, we also evaluate how efficiently they distribute energy among the sensors inside the WSN using the Gini index. We compare our proposed FL-EEC/D approach with the Low Energy Adaptation Clustered Architecture (LEACH), a fuzzy logic-based clustering approach, and various techniques. For various network sizes and topologies, simulation findings show considerable improvements in energy efficiency, network longevity, and balanced energy consumption across sensor nodes. Our results show that initial node depletion and half node depletion have significantly improved on average. © 2023 IEEE.

关键词： Cluster Head Election Clustering Algorithms Energy Efficiency Fuzzy Inference Systems Fuzzy Logic Model Gini Index Measurement Network Longevity Routing Protocols Separation Distance Wireless sensor networks

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

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

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Image processing and Computer Applications (ICIPCA), IEEE International conference on

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

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

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 devel

关键词： Accuracy Grounding Lightning protection Graph neural networks Real-time systems sensor systems Complex systems Monitoring intelligent sensors Optimization

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