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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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Open-loop printing of liquid metal for the low-cost rapid fabrication of soft sensors 5

Open-loop printing of liquid metal for the low-cost rapid fa...

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5th international conference on Soft Robotics (RoboSoft)

作者： Chen, Junda Shih, Benjamin Adibnazari, Iman Park, Yong-Lae Tolley, Michael Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA Harvard Univ Sch Engn & Appl Sci Boston MA USA Seoul Natl Univ Dept Mech Engn Seoul South Korea

ISBN: (纸本)9781665408288

Soft sensors consisting of highly conductive liquid metal, eutectic gallium-indium (eGaIn), embedded in a soft elastomer have been of great interest recently due to their stretchability for sensing temperature, pressure, and strain for a variety of applications. Previous studies have explored methods for patterning the liquid metal during the manufacture of soft sensors. Among these, direct printing of eGaIn is attractive as it is easily re-configurable for fabricating custom sensors. However, to print liquid metal traces with consistent widths, previous approaches require combinations of 1) high-precision feedback control of the stand-off distance and the extrusion rate, 2) a very low stage velocity, and/or 3) modification of the properties of liquid metal with additional components or processing steps. To address these limitations, we present two open-loop liquid metal printing methods to achieve high precision using a low-cost, open-source system without the need to optimize the properties of the liquid metal. We discuss how key print parameters-as well as a novel print head design inspired by a paintbrush-affect the printing quality, and how well the printing methods can resist variation in the height of the substrate. We then apply these methods to manufacture soft strain sensors, which we test and calibrate. In general, these printing methods represent easily accessible methods for manufacturing liquid metal sensors without the need for complex equipment or materials processing.

关键词： Printing Fabrication Temperature sensors Liquids Soft sensors Metals sensor phenomena and characterization

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Deep Learning-Based Real-Time Face Mask Detection for Human Using Novel YOLOv2 with Higher Accuracy 5th

Deep Learning-Based Real-Time Face Mask Detection for Human ...

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5th international conference on Computer and Communication Technologies, IC3T 2023

作者： Tun, Nay Kyi Myat, Aye Min Oo, Htet Ne thu, Amar Myo Win, Kyaw thu Faculty of Computer System and Technology Myanmar Information Institute of Technology Mandalay Myanmar Pyin Oo Lwin Myanmar Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Electronic Department Defence Services Academy Pyin Oo Lwin Myanmar

ISBN: (纸本)9789819997060

A novel deep learning-based face mask detection system is designed to enhance public safety in various environments. With the ongoing global health concerns, the need for efficient and accurate methods to identify individuals wearing or not wearing face masks has become crucial. By utilizing convolutional neural networks (CNNs) and transfer learning techniques, proposed model achieves impressive accuracy while maintaining high-speed processing capabilities. this paper outlines the architecture, training process, and performance evaluation of the proposed deep learning-based face mask detection system, highlighting its promising role in contributing to a safer and healthier society. the development of a vision-based safety system, the transfer of a small YOLO object detection model, and the creation of a CNN-based classification model are the key objectives of this study. According to experimental findings, proposed system is capable of real-time face mask detection and classification with an accuracy of over 98%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enhancing Face Recognition Between Two Sibling Sisters Based on Convolutional Neural networks 5th

Enhancing Face Recognition Between Two Sibling Sisters Based...

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5th international conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Dong, Bo Glasgow College University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819751808

In the age of the Internet of everything, facial recognition technology has become a critical element for security and identity verification. Especially in the identification of biological siblings with high facial similarity, this technology faces enormous challenges. this study delves into the application of Convolutional Neural networks (CNN) in this field, demonstrating its outstanding performance in complex and variable facial recognition tasks due to its advanced feature learning and abstraction capabilities. this study introduces data augmentation techniques to not only increase the data volume but also enrich its diversity. Consequently, the model can learn and adapt to a broader range of facial features and expressions. Furthermore, the optimization of regularization techniques and hyperparameter selection minimized the risk of overfitting and ensured the model’s generalization capability on unseen data. the experimental results are encouraging. With comprehensive adjustments to various hyperparameters and regularization techniques, on the validation set, the accuracy of the model was 95%. It also demonstrated 60% accuracy on a complex and challenging test set, validating its effectiveness and reliability in real-world application scenarios. this research not only proves the effectiveness of CNNs in handling challenging facial recognition tasks but also provides valuable insights and foundations for future studies and applications. It is believed that, with the further development and optimization of technology, the accuracy and application scope of facial recognition will be greatly expanded and enhanced. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Unsupervised Correlation- and Interaction-Aware Anomaly Detection for Cyber-Physical Production Systems based on Graph Neural networks 5

Unsupervised Correlation- and Interaction-Aware Anomaly Dete...

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5th international conference on Industry 4.0 and Smart Manufacturing, ISM 2023

作者： Goetz, Christian Humm, Bernhard G. Darmstadt University of Applied Sciences Department of Computer Science Schöfferstr. 3 Darmstadt64295 Germany Yaskawa Europe GmbH Philipp-Reis-Str. 6 Hattersheim am Main65795 Germany The Hessian Center for Artificial Intelligence hessian.AI Karolinenplatz 5 Darmstadt64289 Germany

Anomaly detection is essential for realising state-of-the-art and safe cyber-physical production systems. With the early detection of possible anomalies, the rise and the carryover of a failure throughout the entire manufacture can be prevented. Current anomaly detection approaches use sensor data from the live system only. therefore, many important characteristics, interactions, and correlations within the system are neglected. Graphs open a particular way of modelling cyber-physical production systems, while at the same time, they allow the inclusion of a wider variation and variety of data about the system. In this study, we introduce an unsupervised correlation- and interaction-aware anomaly detection concept for cyber-physical production systems based on graph neural networks. We define a concept that allows modelling the entire system as a graph, including external knowledge. We introduce a reconstruction-based graph neural network to detect and analyse the anomalies in these graph structures. the concept is evaluated in a real industrial cyber-physical production system. the test results confirm that the presented concept can be applied to detect anomalies while concurrently considering the correlations and interactions of the system. © 2024 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Anomaly detection cyber-physical production systems cyber-physical systems deep learning graph neural networks unsupervised learning

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A General-Purpose CMOS Vision sensor with In-Pixel 5-bit Convolutional Layer Computation 29

A General-Purpose CMOS Vision Sensor with In-Pixel 5-bit Con...

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29th IEEE international conference on Electronics, Circuits and Systems (IEEE ICECS)

作者： Garcia Lesta, D. Pereira Rial, O. Brea, V. M. Lopez, P. Cabello, D. Univ Santiago de Compostela Ctr Singular Invest Tecnol Intelixentes CiTU Santiago De Compostela Spain

ISBN: (数字)9781665488235

ISBN: (纸本)9781665488235

Hardware accelerators for deep convolutional neural networks (CNN) commonly reduce the bit-depth of weights and input feature maps to decrease both circuit complexity and power consumption. Nevertheless, even when the input image is codified with less number of bits, still an analog-to-digital conversion is required, being this one of the most energy-hungry parts of an image sensor datapath. In this work, a smart pixel with processing capabilities to process the first layer of a CNN is presented. In this architecture, information captured from the sensor is fed directly into the CNN accelerator, reducing the power consumption and input error, which is only limited by the analog processing circuitry non-idealities. Programmability includes stride configuration and kernel size selection between 3x3, 5x5 and 7x7. this paper provides data based on nominal simulations in standard 180 nm CMOS technology.

关键词： image sensor CNN hardware accelerator focal plane processing

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Autonomous and Adaptive Communications Systems in Mobile Ad-Hoc networks 5th

Autonomous and Adaptive Communications Systems in Mobile Ad-...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： J, Bhuvana Yadav, Dhyan Chandra Goyal, Manish Kumar Sharma, Himanshu Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Luckow India Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

Protocols, enabling communication, manipulation, and coordination among cell nodes. those techniques can efficiently use the community topology, connectivity, to-be-had assets, and consumer requirements to alter communication parameters and path selection for record transmissions robotically. Self-sufficient/adaptive systems also manage user requests and sensor measurements in real-time to dynamically regulate verbal exchange protocols and network configurations for higher verbal exchange performance. Furthermore, those structures also provide capabilities, including self-configuration, self-organization, fault-tolerance, and communique reliability. Various mechanisms may permit autonomous and adaptive verbal exchange in MANETs, including geo-vicinity-based total routing, location-established network coding, allotted garages, energy-conscious communication protocols, and allotted coordination techniques. Additionally, simulated annealing and particle swarm optimization can be used to optimize conversation and routing protocols. Self-sufficient and adaptive communications systems in cellular ad hoc networks have become a need in today’s international, as cell gadgets are used extra often and for more complex tasks. these structures are essential for preserving a clean float of verbal exchange without a centralized infrastructure or constant direction topologies. Self-reliant communications systems offer sturdy, self-configuring, self-healing, and self-organizing communication networks. Such structures are designed to constantly alter parameters primarily based on facts amassed from community resources and a person’s desired preference. Adaptive systems, on the other hand, dynamically create a superior topology by way of considering to-be-had assets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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A survey of attention mechanisms for wearable sensor-based human activity recognition 5

A survey of attention mechanisms for wearable sensor-based h...

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5th international Symposium on Advanced Technologies and Applications in the Internet of things, ATAIT 2023

作者： Wang, Xin Wang, Yan Geng, Yingrui Yu, Hongnian Yang, Hongmei Wen, Xiaoxu Wang, Aihui School of Electric and Information Zhongyuan University of Technology Zhengzhou450007 China School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

Attention mechanisms, widely used in many fields such as computer vision (CV) and natural language processing (NLP), enable deep learning networks to extract more important information from the input, thereby improving performance and efficiency. Recently, attention mechanisms are introduced to wearable sensor-based human activity recognition (WSHAR) for learning more robust feature representations. this paper investigates the attention mechanisms in WSHAR with a special focus on the principles of computing attention and the targets on which the attention works in a network and future directions. the aim is to provide readers with a clearer understanding of attention mechanisms in WSAHR and motivate more diverse work in the future. © 2023 CEUR-WS. All rights reserved.

关键词： Wearable sensors

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Deep Learning-Driven Sentiment Analysis in Textual Data 5

Deep Learning-Driven Sentiment Analysis in Textual Data

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kathiravan, M. Saravanan, S. Jagadeesh, M. Lakshmi, I. Durga, V. Sathya Raja, N. Bharathi Computer Science and Engineering Chennai India Computer Science and Engineering Saveetha School of Engineering Chennai Thandalam India Computer Science and Engineering Rajalakshmi Engineering College Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

Emotional recognition from text is essential in natural language processing with far-reaching consequences in areas such as Artificial Intelligence, Human-Computer Interaction, and others. Emotions are felt, thought-out physical responses to events. Analyzing these feelings independently of vocal and facial cues is critical to interpreting emotions. Despite these challenges, it is crucial to understand human emotions, especially as people become more comfortable expressing themselves through hate speech on sites like Facebook, Twitter, etc. this paper discusses how to classify many tweets based on their tone. Here, we use deep learning algorithms to determine whether an expression means a happy or sad emotion. there are four distinct negative states of mind: rage, indifference, loneliness, disdain, sadness, and despair. the subset of positive emotions includes zeal, joy, happiness, love, calm, pleasure, and wonder. Using three datasets, we validated and assessed the method's use of long short-term memory and recurrent neural networks to obtain high accuracy in emotion categorization. With an 85.07% predictability for positive and negative classification and an 87.23% and 86.3% accuracy for positive and negative subclasses, respectively, a detailed examination reveals that the system improves emotion prediction using the LSTM model. © 2024 IEEE.

关键词： Deep Learning techniques Emotion Classification Long Short-term memory Recurrent Neural networks

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Gateway malicious command detecting for intelligent fishery sensor networks Malicious instruction detection method based on deep learning 5

Gateway malicious command detecting for intelligent fishery ...

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5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Zhang, FengWei Tao, WenXin Sun, YingZe Information Technology Research Center Chinese Academy of Fishery Sciences Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510662520

Fisheries IoT realizes real-time online detecting and precise modulation of aquaculture process, which greatly enhances the informationization, automation and intelligence level of farms, reduces production costs, promotes the scale of fishery industry, and improves the quality level and market competitiveness of agricultural products. However, the key to large-scale fishery farming lies in accurate water environment modulation, the IoT gateway system is the key to realize the ability of intelligent fishery to achieve accurate water environment modulation. Once the aquatic product networking is attacked by malicious IoT commands, it may cause rapid disintegration of the farming environment ecosystem at any time, resulting in huge economic losses. Due to the size and power consumption of fishery IoT gateways, traditional security protection means are often difficult to be applied in such scenarios. therefore, this paper proposes an improved malicious command detection method for smart fisheries sensor gateways based on deep learning techniques, aiming to accurately and efficiently identify malicious control commands of smart fisheries sensor networks on IoT gateways. the main contributions of this paper are three: (1) Transforming various types of signals from gateways into feature texts, which are used as inputs for deep learning for command classification. (2) Based on the BiLSTM model, it is improved by adding a multi-headed self-attention mechanism to obtain a smaller size and higher accuracy malicious command detection model applicable to smart fisheries gateways (3) Experimental results show that compared with other models, highly confusing malicious commands elaborated for the command characteristics of short, small size and high change rate of smart fisheries sensor commands the improved BiLSTM model has a higher recognition rate. © 2023 SPIE.

关键词： Gateways (computer networks)

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Research on stamped character detection system based on TextBoxes 5

Research on stamped character detection system based on Text...

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2023 5th international conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Wei, Hao Cai, Limin Chen, Dong Zhao, Peizhi Lai, Zhongyuan Institute of Intelligent Manufacturing Jianghan University Hubei Province Wuhan430056 China School of Artificial Intelligence Jianghan University Hubei Province Wuhan430056 China

ISBN: (纸本)9781510667440

In order to realize the rapid detection of stenciled characters on castings in the industrial site, and to better classify and manage the castings, under the influence of complex environmental factors on the industrial site, the stenciled characters of castings have the characteristics of low contrast and inconspicuous edge features, etc., using deep learning. Methods An end-to-end OCR (Optical Character Recognition) casting steel stamp character recognition system was designed. By using the improved TextBoxes detection method on the input image to locate the target character area, combined with the CRNN (Convolutional Recurrent Neural Network) character recognition method to recognize and output the detected stamped characters, and at the same time augment the image data to improve the recognition accuracy. the results show that the model can reach 98.9% in recognition accuracy, and the average inspection time is 0.27S. It is superior to traditional template matching methods and other current mainstream deep learning target detection algorithms in recognition accuracy and speed. In terms of processing natural language information. It provides convenience and provides an effective means of human-computer interaction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Recurrent neural networks

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