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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Slotted Cubic design for 2.4 GHZ Antenna 5

Slotted Cubic design for 2.4 GHZ Antenna

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5th IEEE international conference on Advanced Systems and Emergent Technologies (IC_ASET)

作者： Gahgouh, Salem Saidi, Imen Gharsallah, Ali Univ Tunis El Manar Microwave Elect Res Lab Merlab Fac Sci Tunis Tunis Tunisia

ISBN: (纸本)9781665427357

In this paper, we have presented, cubical antenna works at ISM 2.45GHz frequency band for the sake of compatibility with most of wireless sensor network protocols as well as telecommunication facilities. We considered the case study of a monopoly antenna in a cubic slot cavity. the proposed antenna is a robust design and proves to be an excellent candidate for WSN applications.

关键词： Wireless networks 3D antenna reconfigurable antenna cubic antenna slots and cavity

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Anomaly Detection with Autoencoders as a Tool for Detecting sensor Malfunctions 5

Anomaly Detection with Autoencoders as a Tool for Detecting ...

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IEEE 5th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Liebert, Artur Weber, Wolfgang Reif, Sebastian Zimmering, Bernd Niggemann, Oliver Helmut Schmidt Univ Inst Automat Technol Hamburg Germany Helmut Schmidt Univ Chair Struct Anal Hamburg Germany Helmut Schmidt Univ Hamburg Germany

ISBN: (纸本)9781665497701

One possibility to extend the service life of engineering structures is to provide adequate maintenance based on Structural Health Monitoring (SHM). Typically, SHM involves a sensor network which is spatially distributed at the surface or within the structure to be monitored. Each sensor measures at least one physical quantity, the data of all sensors then have to be properly evaluated to derive the health state and to predict the remaining service life. Health issues may be detected by machine learning methods by looking for anomalous behaviour in sensor data. Hereby the problem is that malfunctions differ excessively in the representation of the data collected by sensors such that specialisation of methods on anomaly types is required. the current contribution suggests the simulation of sensor malfunction based on established criteria by creating different types of artificial anomalous data indicating different types of issues. Several proposed autoencoder approaches are verified for different anomaly representations, which are artificially introduced in a set of data. the final solutions are different autoencoder specialized on different types of simulated anomaly data, making the conclusions drawn from the measured data more reliable. As a case study, data of a numerical experiment of fibre pull-out are considered.

关键词： Runtime Neural networks Machine learning Maintenance engineering Cyber-physical systems Probabilistic logic Data models

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5th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

5th International Conference on Artificial Intelligence and ...

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5th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

ISBN: (纸本)9781839539848

the proceedings contain 76 papers. the topics discussed include: vibration signal-based fault prediction using few-shot learning in the digital twin ecosystem;design of bolt defect recognition system based on machine vision;development study on artificial intelligence target detection technology with ant colony optimization;data statistics acceleration calculation method based on massive measuring point scene in industrial field;design and research of UAV maritime search and rescue system based on multi-source information fusion;simulation research on single joint control of day-lily manipulator based on error sign integral robust control;optimization of joint mechanism for assisted exoskeleton robot;visual sensor aided by image processing based on optimization algorithm research of mobile robot localization;and design of an intelligent reminder system based on EEG monitoring.

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Segment Anything Model for Scan-to-Structural Analysis in Cultural Heritage 5

Segment Anything Model for Scan-to-Structural Analysis in Cu...

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5th international conference in Electronic Engineering, Information Technology and Education, EEITE 2024

作者： Galanakis, Demitrios Lucho, Stuardo Maravelakis, Emmanuel Bolanakis, Nikolaos Konstantaras, Antonios Vidakis, Nectarios Petousis, Markos Treuillet, Sylvie Desquesnes, Xavier Brunetaud, Xavier Hellenic Mediterranean University Department of Electronic Engineering Chania Greece Polytech Orléans PRISME Orléans France Hellenic Mediterranean University Department of Mechanical Engineering Heraklion Greece Polytech Orléans LaMé Orléans France IUT de l'Indre PRISME Issoudun France

ISBN: (纸本)9798350372878

Segment anything model (SAM) proposed by META Artificial Intelligence (AI) has disrupted the space of Deep Learning (DL) and Machine Learning (ML) and claims performance that outrivals conventional Convolutional Neural networks (CNNs). the well recognized success of foundation models paved the way towards Natural Language processing (NLP), which in computer vision domain, rests upon the concept of vision transformers (ViT). the claimed scalability and zero-shot predictability of the proposed mode has spurred extensive research among different fields. In this sense, this paper aims to investigate Segment Anything Model (SAM) potential in cultural heritage (CH) inclined scenarios. this is part of an ongoing project that seeks to implement scan-to-structural analysis at stone level. Based on the findings of this research, SAM zero-shot segmentation ability is quite promising for complex scene understanding. In addition, its tendency to favor small objects may provide a unique opportunity for damage detection and defects classification. Finally, even without any pre-training, SAM exhibits remarkable performance in edge detection and masking, without sacrificing generalizability and its accuracy in segmentation correlates to spatial resolution and background separation. © 2024 IEEE.

关键词： Convolutional neural networks

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Student Behavior Detection Method Based on Improved YOLOv5 Algorithm 5

Student Behavior Detection Method Based on Improved YOLOv5 A...

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5th international conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Hu, Haitao Yang, Meihong Guo, Ying Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350373820

When using computer vision technology to identify students in the classroom, it will be affected by the inconsistency of the seating layout in different classrooms, the complexity of the background, as well as the existence of back row students with smaller portraits, mutual occlusion and other problems resulting in poor recognition of student behavior and other problems. In order to solve this problem, this paper firstly takes the real classroom video of a university, summarizes five common student behaviors, intercepts the video frame by frame, labels the data, and constructs the dataset, and then proposes a target detection technique based on the improved YOLOv5 algorithm, which improves the YOLOv5 algorithm by replacing the SPP module in the original network with the BasicRFB module and introducing the SACA attention mechanism. algorithm, which enhances the feature extraction capability of the model and ensures the detection speed at the same time;in the post-processing stage of the model, Soft-NMS is used to replace the original NMS non-maximal value suppression method, which reduces the probability of the model leakage detection. Finally, through the ablation experiment, it is verified that the improved method proposed in this paper has better detection effect for real classroom scenarios, which can help teachers grasp the learning status of students in time and optimize the teaching methods accordingly, and at the same time, it provides solid support for the dynamic evaluation of classroom teaching. © 2024 IEEE.

关键词： Computer vision

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Soft sensor of Effluent Ammonia Nitrogen Concentration Based on Modular Stochastic Configuration networks 5

Soft Sensor of Effluent Ammonia Nitrogen Concentration Based...

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5th international conference on Industrial Artificial Intelligence, IAI 2023

作者： Li, Kang Yang, Cui-Li Li, Da-Peng Qiao, Jun-Fei Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798350325294

this paper proposes a soft sensing method based on a brain-inspired modular stochastic configuration network for the real-time and accurate measurement of effluent ammonia nitrogen concentration in wastewater treatment processes. this method first uses grey relational analysis to extract the main variables affecting the effluent ammonia nitrogen concentration. then, based on the fuzzy clustering method, the task is decomposed to alleviate the complexity of each modeling task. After that, following the principle of divide and conquer, a corresponding stochastic configuration network sub-model is constructed for each sub-task, and the results of each sub-model are integrated to improve the measurement performance of effluent ammonia nitrogen concentration. Finally, the effectiveness and accuracy of the proposed method are verified by simulation based on actual water quality data. © 2023 IEEE.

关键词： Effluents

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Forensic sketch-to-photo transformation with improved Generative Adversarial Network (GAN) 5

Forensic sketch-to-photo transformation with improved Genera...

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5th international conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Khatoon, Shadma Umar, Mohammad Sarosh Aligarh Muslim Univ Dept Comp Engn Zakir Husain Coll Engn & Technol Aligarh Uttar Pradesh India

ISBN: (纸本)9781665476478

Many of the limitations of Convolutional Neural networks started to become apparent with the development of "Capsule neural networks", and more research is now being done on Capsule networks since it is believed that it will be the state-of-the-art for image classification tasks in the future. In this paper, the discriminator inside a GAN for face sketch to photo translation employs capsule neural network layers rather than the traditional convolutional layers. Incorporating the Capsule design into the Discriminator will improve the classification loss and speed up the convergence compared to using traditional convolutions. With 188 hand-drawn sketch-photo pairings available in the CUHK dataset and constructed 1000 sketch-photo pairs of CelebA HQ dataset, our proposed method is evaluated. the results of the experiments demonstrate that the proposed approach can generate images that are visually extremely near to the real photos and have rich texture information and features. the translation of face sketch to photo can greatly help the law enforcement agencies to track and arrest the criminals.

关键词： Generative adversarial networks face sketchphoto generation Capsule networks CUHK database CelebA HQ database pixel loss

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Objective Functions in High-Density Internet of things networks - A Performance Evaluation 5th

Objective Functions in High-Density Internet of Things Netwo...

引用

5th international conference on Computing Science, Communication and Security

作者： Gul, Safia Malik, Bilal Ahmad Banday, M. Tariq Univ Kashmir Srinagar India

ISBN: (纸本)9783031751691;9783031751707

the Internet of things has sparked a profound revolution in networking by enabling the collection of vast sensor data, serving as the foundation for smart applications such as homes, cities, and industries. However, achieving such solutions is challenging due to resource constraints such as limited computational power, storage, and memory in IoT devices. the routing protocol (RPL), the most commonly utilized routing algorithm, employs objective functions (OFs) to determine path selection based on diverse metrics. the accurate evaluation and testing of these OFs are paramount in developing effective routing solutions. this paper thoroughly analyses the results of testing standard objective functions, focusing on addressing load imbalance and congestion issues. the analysis aims to ascertain the suitability of existing standardized objective functions as optimal routing solutions for IoT networks, given the results of the challenges posed by load balancing. this paper evaluates the efficacy of various objective functions used in RPL, providing insights to improve the routing performance and evaluate IoT devices' reliability, energy efficiency, load balancing, and overall network performance. the results of testing standard objective functions using the Contiki OS tool are meticulously compared. the analysis seeks to assess the suitability of the existing standardized objective functions as the most optimal routing solution for IoT networks.

关键词： RPL DODAG OF0 MRHOF Congestion

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Research on intelligent decision-making mechanism of the elderly care system based on fuzzy neural network 8

Research on intelligent decision-making mechanism of the eld...

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8th international conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Tang, Lijun Chongqing Vocational Institute of Engineering Big Data and Internet of Things School Chongqing China

ISBN: (纸本)9798350302455

In order to solve the problem that the existing detection mechanism only using the fall sensors easily leads to the elderly care system receiving false fall help signals, an intelligent decision-making mechanism based on a fuzzy neural network is proposed. After receiving the fall help signal reported by the person label, the mechanism first reads the sensor data collected by the person label at that moment, then standardizes the sensor data, and finally inputs the standardized sensor data into the fuzzy neural network model to analyze and judge the authenticity of the fall help signal. the results show that the intelligent decision-making mechanism based on the fuzzy neural network can effectively eliminate the false fall help-seeking signal caused by the false judgment of the fall sensor, and the judgment accuracy of the fall help-seeking signal can reach 97. 5%. © 2023 IEEE.

关键词： Fuzzy neural networks

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Research on air interface interference recognition in mobile communication networks based on LSTM autoencoder 5

Research on air interface interference recognition in mobile...

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5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Sun, Hongwei Hu, Mingwei Wang, Baoji Chen, Yue Fu, Jingwen Huang, Jia Jiangsu Automation Research Institute Lianyungang222006 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100094 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Sankuai Online Technology Co. Ltd. Beijing100102 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Due to the diverse nature of interference signals in mobile communication frequency bands, feature extraction is challenging, and the rapid and automated identification of behaviors such as illegal frequency usage and malicious interference are difficult. Moreover, evaluating the precise impact of interference signals on communication networks is challenging. In response to this problem, this paper proposes an air interface interference recognition technology for communication links based on base station measurement reporting data. It constructs an interference recognition model to achieve automatic interference identification and evaluates the accuracy of interference recognition. On the base station side, this paper utilizes channel measurement data such as signal strength, signal quality, and signal-to-noise ratio exported by the base station. It uses a Long Short-Term Memory (LSTM) autoencoder model to learn normal signal patterns for interference discrimination and evaluates the impact of interference on communication networks. this method achieves precise identification of fixed-frequency interference on the base station side and evaluates the impact of interference on communication networks. Experimental results show that the base station-side approach achieves an F1 score of 0.99 in identifying fixed-frequency interference, outperforming One-Class Support Vector Machine (OCSVM), Principal Component Analysis (PCA), and Isolation Forest (IForest) methods under similar conditions. © 2025 SPIE.

关键词： Base stations

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