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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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IoT Data Collection and processing System Using LoRa WAN Technology 5

IoT Data Collection and Processing System Using LoRa WAN Tec...

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5th IEEE international conference on Advanced Information and Communication Technologies, AICT 2023

作者： Vulchak, Pavlo Brych, Mykola Yaremko, Oleh Strykhalyuk, Bohdan Dumych, Stepan Lviv Polytechnic National University Department of Telecommunications Lviv Ukraine Lviv Polytechnic National University Department of Radioelectronic Devices and Systems Lviv Ukraine

ISBN: (纸本)9798350372571

this paper delves into the burgeoning field of the Internet of things (IoT), focusing on the pivotal role of LoRaWAN (Long Range Wide Area Network) technology in constructing long-range, energy-efficient IoT networks. We provide an in-depth analysis of LoRaWAU technology and compare it with other technologies like NB-IoT, LTE-M, and SigFox, shedding light on its relative strengths and weaknesses. In particular, we develop a full software-defined LoRaWAN network with the gateway, end devices, network and application servers, the developed network is integrated with the things Network (TTN) for device configuration and data visualization. the experimental analysis of the network's transmission range across different propagation factors, has been conducted to prove the efficacy and reliability of LoRaWAN networks for IoT implementations across diverse domains. © 2023 IEEE.

关键词： Wide area networks

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sensor Embedding and Variant Transformer Graph networks for Multi-source Data Anomaly Detection 5th

Sensor Embedding and Variant Transformer Graph Networks for ...

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5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Ma, Liwei Huang, Zhe Peng, Bei Zhang, Mingquan He, Wangpeng Wang, Yu Xi An Jiao Tong Univ Sch Mech Engn Xian Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China Xidian Univ Sch Aerosp Sci & Technol Xian Peoples R China

ISBN: (纸本)9789819770007;9789819770014

With the rapid development of sensor technology and the proliferation of multi-source data, anomaly detection of multi-source time series data has become more and more important. In the past, anomaly detection methods often deal with the temporal information and spatial information contained in the data separately, which makes the spatio-temporal information in the data unable to be fully utilized by the model. To this end, this paper proposes a fusion of sensor embedding and temporal representation networks to solve this problem. In addition, we adopt graph neural network to better model multi-source heterogeneous data, and enhance the accuracy of anomaly detection by combining the double loss function of reconstruction loss and prediction loss. this approach not only facilitates the learning of normal behavior patterns from historical data but also enhances the model's predictive capabilities, allowing for more accurate anomaly detection. Experimental results on four multi-source sensor datasets show the superiority of the proposed method compared with the existing models. Further analysis show that the model enhances the interpretability of anomaly detection through the analysis of anomaly associated sensors.

关键词： multi-source time series anomaly detection graph neural network model interpretability

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Fall Detection Technique for Older Individuals based on Deep Layered Neural networks Embedded with Transfer Learning 5

Fall Detection Technique for Older Individuals based on Deep...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Deepika, J. Akilandeswari, J. Sona College of Technology Department of Information Technology India

ISBN: (纸本)9781665474672

the vital priority of the society is to render helping hand to most fragile category of people such as older individuals, neurogenerative disease affected patients, etc. to lead their normal routine social life being physically active without lowering their quality of life and not losing their autonomy. As ageing progress, the elderly adults experience falls and those consequences lead to lesser quality of life due to the injuries occurred L this further adds to the fear of falling and reduced physical activity. the first solution of protection against the negative effects of falling is to identify fragility and high fall risk at an early stage. the development of novel techniques to identify potential fallers before any fall occurs would be the second solution of protection. the Artificial lntelligence (AD technique is used to examine and prevent falls from the signal-hised image gathered from the gyroscope or the accelerometer sensor information. the several layers of convolutional neural network are used with different dilation rates for extracting different categories of feature selection. the performance evaluation hised on the experimental results on the signal image dataset proves higher accuracy of classification rate. © 2023 IEEE.

关键词： Convolutional neural networks

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Carbon Monoxide Concentration Monitoring System for Automating Air Filters 5

Carbon Monoxide Concentration Monitoring System for Automati...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Ghadge, Ameya Juvekar, Ayush Wakode, Madhuri Kale, Geetangali Pune Institute of Computer Technology Department of Computer Engineering Pune411043 India

ISBN: (纸本)9781665475242

As urbanization continues to accelerate at a break-neck pace, the need for smart cities has become increasingly pressing. this need is particularly evident in developing countries, such as India, where the rapid pace of growth has led to significant problems, including the issue of air pollution. Inhaling pollutants over extended periods of time can be harmful to human health, and can even be life-threatening. Carbon monox-ide (CO) is one such pollutant, and its detection and control in residential and industrial environments is critical to avoid potential health problems. To address this issue, sensor networks can be deployed in affected areas to measure the concentrations of different gases and to take necessary action to reduce their levels in the air. Wireless sensor networks (WSNs) are currently a popular research area due to their potential applications, and this paper discusses the implementation of WSNs for monitoring CO concentration in order to automate the filtration process using the Zigbee Protocol for communication. By deploying sensor networks and leveraging the power of the Zigbee Protocol, we can help to mitigate the damaging effects of air pollution and improve the quality of air in the cities. © 2023 IEEE.

关键词： sensor nodes

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Detecting Anomalous Events in Object-Centric Business Processes via Graph Neural networks 5th

Detecting Anomalous Events in Object-Centric Business Proces...

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5th international conference on Process Mining (ICPM)

作者： Niro, Alessandro Werner, Michael Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783031561061;9783031561078

Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However, many real-world process executions exhibit a graph-like structure, where events can be associated with multiple cases. Flattening event logs requires selecting a single case identifier which creates a gap with the real event data and artificially introduces anomalies in the event logs. Object-centric process mining avoids these limitations by allowing events to be related to different cases. this study proposes a novel framework for anomaly detection in business processes that exploits graph neural networks and the enhanced information offered by object-centric process mining. We first reconstruct and represent the process dependencies of the object-centric event logs as attributed graphs and then employ a graph convolutional autoencoder architecture to detect anomalous events. Our results show that our approach provides promising performance in detecting anomalies at the activity type and attributes level, although it struggles to detect anomalies in the temporal order of events.

关键词： Object-centric Process Mining Graph Neural networks Anomaly Detection

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A Demonstration Platform for Large-Scaled Point Cloud Network Based on 28nm 2D/3D Unified Sparse Convolution Accelerator 5

A Demonstration Platform for Large-Scaled Point Cloud Networ...

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5th IEEE international conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Feng, Xiaoyu Sun, Wenyu Fan, Shupei Tang, Chen Yang, Yixiong Yue, Jinshan Liao, Qingmin Yang, Huazhong Liu, Yongpan Tsinghua University China Tsinghua Shenzhen International Graduated School China Chinese Academy of Sciences China

ISBN: (纸本)9798350332674

3D point cloud processing plays an important role in many emerging applications such as autonomous driving, visual navigation, and virtual reality. It calls for hardware acceleration of multiple key operations, including 3D Submanifold SCONV, 3D non-Submanifold SCONV, and 2D SCONV. this work presents a 2D/3D unified sparse convolution accelerator for large-scale voxel-based point cloud networks. the chip is fabricated in TSMC 28nm CMOS technology to achieve 3.3-16.9 FPS running from 60-400MHz when computing the SECOND network on KITTI dataset. this work has been included by ISSCC2023 [1]. A demonstration is given to show the real-time 3D processing with a lidar sensor. © 2023 IEEE.

关键词： Convolution

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Advanced Communication in Cyber Physical System Infrastructure, Protocols, and Challenges 5

Advanced Communication in Cyber Physical System Infrastructu...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Haldorai, Anandakumar Ravishankar, C.V. Mahdi, Qaysar S. Nehru, G. Jawaharlal Sri Eshwar College of Engineering Coimbatore India Sambhram Institute of Technology Karnataka Bangalore India Tishk International University-Erbil Faculty of Engineering Kurdistan Iraq ST. Martins Engineering College Telangana Secunderabad India

ISBN: (纸本)9781665493604

In contemporary developments like Cyber Physical System (CPS) and the Internet of things (IoT), computerized integrated connection is seen as a trend. Some of the unique research issues in CPS include security, confidentiality, business intelligence, participatory monitoring, and smart decision making. A secure infrastructure, reliable protocols, and high-quality solutions are just a few of the other Wireless sensor networks (WSN) obstacles that must be overcome. this paper provides a critical survey of advanced communications and sensing in CPS infrastructures, protocols as well as challenges. A CPS framework, including its protocols and implementations, are described in this work. Mobile sensor Information Agent (MSIA) signifies a software-centric mobile sensor system. It operates as a plug-in with the CPS framework and scalability mobile apps. With the MSIA, data from numerous external sensors may be collected and sent to the cloud as needed. Integration with the real environment is essential to CPS's stability and security as well as its dependability and resilience. Emerging Software-Defined Networking (SDN) technologies may be coupled with CPS architecture as a communications network to build this system. thereby, it is feasible to enhance the performance of the system utilizing an SDN-based CPS architecture © 2023 IEEE.

关键词： Cyber Physical System

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Wi-SensiNet: through-Wall Human Activity Recognition Based on WiFi Sensing 5

Wi-SensiNet: Through-Wall Human Activity Recognition Based o...

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5th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Fu, Xiaoyi Wang, Chenlu Li, Shenglin College of Artificial Intelligence Southwest University Chongqing China

ISBN: (纸本)9798331506612

With the advancement of Wi-Fi sensing technology, its significant benefits in operational convenience and privacy protection have become increasingly evident, particularly in fields such as smart homes, medical monitoring, and security surveillance, where Human Activity Recognition (HAR) technology shows great promise. this study investigates a novel approach to Human Activity Recognition (HAR) using Wi-Fi Channel State Information (CSI), with a particular focus on complex conditions such as Non-Line-of-Sight (NLoS) scenarios, including through-Wall transmissions. Traditionally, most research has focused on HAR in Line of Sight (LoS) paths, which are highly sensitive to environmental changes, while NLoS signals, especially through-wall signals, suffer from signal attenuation due to walls. To address this issue, we propose Wi-SensiNet, an innovative deep learning-based method that combines the spatial feature extraction capabilities of Convolutional Neural networks (CNN) with the temporal sequence processing power of Bidirectional Long Short-Term Memory (BiLSTM) networks. this method also incorporates an attention mechanism to enhance the accuracy of human activity recognition in complex environments. Wi-SensiNet is specifically designed for through-wall scenarios, effectively managing the complexity of CSI data and achieving accurate through-wall human activity detection. In our experiments, we collected a through-wall CSI dataset comprising seven common activities, including running, sitting, standing, squatting, falling, punching, and walking, and verified that Wi-SensiNet achieved an average accuracy exceeding 99% on the original test set. these results demonstrate the model's robustness and high accuracy in handling HAR tasks in complex environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Self-Attention Soft sensor Model Based on Long-Short Time Span 5

Self-Attention Soft Sensor Model Based on Long-Short Time Sp...

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5th international conference on Industrial Artificial Intelligence, IAI 2023

作者： Xiong, Qiluo Ren, Yanhui Yang, Fan Tsinghua University Department of Automation Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350325294

Quality monitoring of industrial process exhibits a real-time characteristic, which necessitates the use of process variables to predict values of quality variables. this approach is commonly known as soft sensor modeling. Many previous data-driven methods have shown limitations in handling nonlinearity and temporal relationships. Utilizing deep learning to extract features reflecting the chronological relationship is an ideal approach, yet it requires concerted efforts to fully exploit the time dependence of data. therefore, a self-attention soft sensor model based on long-short time span (LSTS-SA) is proposed. It is composed of an autoencoder that utilizes long short-term memory neural networks to extract shallow long-term features, and a self-attention mechanism to focus on shallow features from the time dimension to obtain deep features. In addition, it utilizes short-term features extracted by a time window and maps the fused features into the prediction space. the superiority of proposed method in dynamic soft sensor modeling is demonstrated in experiments on simulated dataset, the Tennessee-Eastman Process (TEP) dataset. © 2023 IEEE.

关键词： Deep learning

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Research on Intelligent English-Chinese Translation Proofreading System based on Gated Feedback Recurrent Neural networks 5

Research on Intelligent English-Chinese Translation Proofrea...

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5th Asia-Pacific conference on Image processing, Electronics and Computers, IPEC 2024

作者： Li, Qian Heilongjiang University of Technology School of International Education Jixi China

ISBN: (纸本)9798350374407

Addressing the issue of the low precision inherent in conventional algorithmic approaches, this paper introduces an astute model for English translation identification, devised upon an enhanced Gated Feedback Recurrent Neural Network (GF-RNN) framework. this innovative model employs the refined algorithm to amass a substantial bilingual lexicon corpus, thus facilitating the automatic retrieval of phrases. In addition, this paper formulates and outlines the systematic execution of an astute English translation identification model by aggregating, refining, and deploying data. the recommended technique meticulously gathers and refines the input signals of English lexemes, isolating the characteristic parameters to empower the astute discernment in English translations. this paper conducts a rigorous experimental assessment of the fashioned English translation identification model and documents the empirical evidence. the ensuing scrutiny corroborates that the model not only attains superior precision but also adeptly meets the exigencies of translation endeavors in English. © 2024 IEEE.

关键词： Recurrent neural networks

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