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Transient Identification in Nuclear Power Plants by PCA Based Neural networks 5th

Transient Identification in Nuclear Power Plants by PCA Base...

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5th international conference on Reliability, Safety and Hazard, ICRESH 2024

作者： Meghana, G. Santhosh, T.V. Ambili, P.S. Vinod, Gopika Chattopadhyay, J. School of Computer Science and Applications REVA University Bangalore India Reactor Safety Division Bhabha Atomic Research Centre Mumbai India

ISBN: (纸本)9789819730865

Nuclear power plants are complex engineering systems that are maintained and operated by human operators. When a transient occurs in an NPP, it is important for the operator to quickly identify and initiate corrective actions in order to mitigate the consequences, if any, and also to maintain the safety of the plant. As simultaneous generation of many alarms and annunciations in the control room may alert the operator on the onset of the transient it is quite possible that operator get confused to quickly act and identify the correct transient. By continuously analysing sensor data and detecting early warning signs, artificial intelligence models can provide timely alerts and enable operator to initiate proactive actions to mitigate the consequences of the transients. this paper presents a machine learning based transient identification model which assists the operator to identify the transient and initiate corrective action well within the stipulated time. the artificial neural networks based transient identification model has been developed for detecting loss of coolant accidents and main steam line break events in standard 220MWe Indian nuclear power plants. Several important input signals have been identified from the safety analysis reports and transient data has been generated from thermal–hydraulic simulation codes for training neural networks. the major outputs of neural network model are the size of the break, location of the break, etc. As the dimension of the input signals is significantly large, principal component analysis technique has also been employed to improve the performance of neural network model. the performance of neural network model with and without the application of principal component analysis is discussed in this paper. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nuclear power plants

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Vehicle Movement based Innovative Smart Highway Lighting System 5

Vehicle Movement based Innovative Smart Highway Lighting Sys...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Susan, Sophiya S. Raj, Aakash Praveen Mishra, Ashutosh thambi, Vachan CMR Inst Technol Dept E&C VTU RC Bengaluru India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Road networks are a vital part of modern travel;there are currently 21 million kilometers of road networks throughout the world. Using a regression model with an adjusted R2 of 0.90, it is estimated that by 2050, an extra 3.0 - 4.7 million km of road will be paved. this brings up the well-explored domain of automating lighting systems. there have been numerous works trying to optimize road lighting systems, but most of them are statically programed or face problems with upscaling. the goal of this paper is to design and implement advanced development in embedded systems for energy conservation in street lights. this work uses a decentralized approach which enables us to control each node /street light individually as well as a group through the use of NRF 24L01 Transceiver module), the Sensing Unit (Ultrasonic sensor HC-SR04), Microcontroller Unit (Arduino Nano), and Lighting System. the design flow takes care of scenarios where if one or more of the nodes/ sensors fail it will not affect the entire row of streetlights. this work aims at creating a dynamically programed automatic street lighting system with the main focus on scalability. Our goal is to create a synchronized array of nodes that may accept new nodes without requiring any changes to the existing nodes. this allows the ability to increase the size of the automation-network ***, the system was conceived and implemented successfully as a prototype for Smart highway lighting system.

关键词： Dynamic streetlights NRF 24L01 Transceiver module) the Sensing Unit (Ultrasonic sensor HC-SR04)

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Application of IoT in 5G Wireless Communication: A Detailed Review 5th

Application of IoT in 5G Wireless Communication: A Detailed ...

引用

5th international conference on Advanced Informatics for Computing Research, ICAICR 2021

作者： Bhardwaj, Bharti Vanita Kumar, Suresh University Institute of Engineering and Technology MD University Haryana Rohtak India

ISBN: (纸本)9783031094682

Internet Of things (IoT) based wireless technologies have gained a lot of popularity recently all around the world and 5G technology has emerged as a particularly difficult and intriguing research field. this article examines 5th Generation (5G) wireless technologies based on IoT. Internet in 5G is the coming future. Cutting-edge Wi-Fi infrastructure and automation will be enabled by this. Recent cellular networks, such as LTE, will be insufficient or inefficient in meeting the needs of connection to more than one devices with higherrate of data transmission, increased capacity, and lower latency. High-quality service with minimal disruption. 5G technology is the most promising solution to these all issues. this paper gives an in-depth study of the obstacles and future objectives of several communication businesses in the context of 5G. Many features of 5G systems are thoroughly discussed and examines in depth emerging and enabling technologies, which enables the IoT. Control techniques, wider area with low power networks and security considerations are also considered. this paper discusses the role of augmented reality in the 5G future. It also goes into details of IoT application areas in 5G. © 2022, Springer Nature Switzerland AG.

关键词： Wireless sensor networks

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Accurate thoracic Disease Classification via Ensemble networks 2022

Accurate Thoracic Disease Classification via Ensemble Networ...

引用

5th international conference on Image and Graphics processing, ICIGP 2022

作者： Capuchino Antioquia, Arren Matthew Department of Software Technology De la Salle University Manila Philippines

ISBN: (纸本)9781450395465

Convolutional Neural networks (CNNs) are being adapted to various computer-aided diagnosis applications, including recognizing thoracic diseases. To improve classification performance, recent solutions alter the structure of existing networks or require additional prior information for training. In this paper, we propose an ensemble network to accurately recognize thoracic diseases, without additional layers nor extra input data for training. To this end, we perform an exhaustive set of experiments involving various single CNN models for thoracic disease classification. We then form an ensemble from the most accurate classifier based on these experiments. Our approach achieves state-of-the-art average AUROC score of 79.32% on the ChestX-ray14 dataset, which is 2.08% higher than the previous best result. Additionally, we also attain the highest AUROC for 12 of the 14 classes. Our code and trained models are publicly available at https://***/arvention/ChestXRay14-Classification-PyTorch. © 2022 ACM.

关键词： Image classification

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DOA Estimation Using Deep Neural Network with Regression 5

DOA Estimation Using Deep Neural Network with Regression

引用

5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Xiong, Yutao Liu, Aifei Gao, Xuyu Yauhen, Arnatovich The School of Software Northwestern Polytechnical University Xi'an China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781665485890

In this paper, we propose a direction-of-arrival (DOA) estimation method, which uses deep neural network (DNN) with regression to learn the mapping from the array covariance matrix to the DOAs of multiple source signals. Simulation results demonstrate that the proposed method is efficient in both training and testing stages over the existing DNN based classification method. In particular, the training time and the testing time spent by the proposed method are about 9 times and 2 times less than those by the DNN based classification methods, respectively. Furthermore, it performs significantly better than the classical multiple signal classification method (MUSIC) in terms of efficiency. On the other hand, the root mean square error (RMSE) of DOA estimates by the proposed method is much higher than that by the MUSIC method and approaches to the CRB in harsh conditions such as low SNRs and/or closely spaced source signals. © 2022 IEEE.

关键词： Deep neural networks

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IoT-Based Edge Computing and Image processing for Occupancy Detection 11

IoT-Based Edge Computing and Image Processing for Occupancy ...

引用

2023 IEEE 11th international conference on Information, Communication and networks, ICICN 2023

作者： Guo, Chengpeng Hu, Bintao Zhang, Wenzhang Gao, Yuan Liu, Hengyan Xi'An Jiaotong-Liverpool University School of AI and Advanced Computing Suzhou China Xi'An Jiaotong-Liverpool University School of Internet of Things Suzhou China Shanghai University School of Communication and Information Engineering Shanghai China

ISBN: (纸本)9798350314014

A primary strategy for the energy-efficient operation of commercial office buildings is to offer dynamic building services, including lighting, heating, ventilating, and air conditioning (HVAC). therefore, it is necessary to propose an effective and high-accuracy guaranteed real-time video occupancy detection optimisation algorithm according to a model predictive control (MPC) system. In this paper, we propose a YOLOv5 occupation detection algorithm to enhance the accuracy of making informed decisions and control actions on video occupancy detection systems, where the MPC system uses multiple cameras to capture and average the number of people, and a DHTII sensor will be used to measure temperature and humidity and sent to the MPC program through a Raspberry Pi-based script. Simulation results demonstrate the effectiveness of deep learning-based methods in enhancing the accuracy and real-time performance of video occupancy detection systems, paving the way for more efficient and intelligent building management solutions. © 2023 IEEE.

关键词： Model predictive control

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Neural NILM Learning Paradigms: From Centralised to Decentralised Learning 5

Neural NILM Learning Paradigms: From Centralised to Decentra...

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5th international conference on Signal processing and Information Security, ICSPIS 2022

作者： Bousbiat, Hafsa Klemenjak, Christoph Himeur, Yassine Elmenreich, Wilfried Amira, Abbes Mansoor, Wathiq Atalla, Shadi University of Klagenfurt Institute of Networked and Embedded Systems Klagenfurt Austria University of Dubai College of Engineering and IT Dubai United Arab Emirates University of Sharjah Department of Computer Science United Arab Emirates

ISBN: (纸本)9781665492652

Non-intrusive Load Monitoring (NILM) is becoming a paramount in both industrial and residential sectors to achieve efficient energy consumption. thus, research on this matter flourished in recent years, where deep neural networks gained the highest interest from the research community, commonly referred to as neural NILM. As a predominant practice, neural NILM models follow a centralised based learning scheme where the energy data is assumed to be available in a central node for training. However, this practice and the enormous amount of data required by these algorithms raise privacy and security concerns from the consumer's side since energy data can reveal in-home activities and occupancy records if intercepted. Federated Learning (FL), also referred to as collaborative learning, is seen as viable solution to address these issues. Nonetheless, its application in neural NILM is still in its infancy and many challenges are yet to be addressed. the current paper presents an overview of neural NILM models following both a centralised and a federated learning paradigm. Furthermore, it identifies the main challenges with regard to both learning paradigms along with potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry. © 2022 IEEE.

关键词： Deep neural networks

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Classification of Coverage Algorithms in Multimedia Wireless sensor networks 5

Classification of Coverage Algorithms in Multimedia Wireless...

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5th IEEE international conference on Networking and Advanced Systems, ICNAS 2021

作者： Bouhali, Manel Bendjeddou, Amira Ghoualmi-Zine, Nacira Badji Mokhtar-Annaba University Networks and System Laboratory Computer Science Department Annaba Algeria

ISBN: (纸本)9781665428217

In recent years, with the technological development that we are witnessing, the multimedia wireless sensor networks (WMSNs) are attracting more and more interest from researchers, these later are used in various applications for their advantages and reliability. However, the WMSNs have different problems that become the focus of attention of researchers who strived to make various proposals to address these problems. the best-known and popular problem for this type of network is improving and optimizing their coverage. In this paper, several approaches for the coverage optimization problem in the multimedia wireless sensor networks are presented and the advantages and limitations of each one of them are highlighted. then, a classification of these approaches is made. © 2021 IEEE.

关键词： Heuristic algorithms

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Customizable and Flexible Pressure sensor Matrix 5

Customizable and Flexible Pressure Sensor Matrix

引用

5th Scientific School on Dynamics of Complex networks and their Applications, DCNA 2021

作者： Antipov, Vladimir Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Lobachevsky University Nizhny Novgorod Russia

ISBN: (纸本)9781665442824

the paper presents a method of creating and manufacturing customizable, deformable pressure sensor matrix. the purpose of the development is creating and implementing a variety of matrix sensors with integrated electronics and software to determine the distributed pressure on different types of surfaces, as well as further use of this sensor in medical, robotics and sports. Experimental studies of the individual components and the overall sensor error were conducted. As a result of the work, the developed experimental sample was investigated, a flexible metallized base for the new sensor was developed, and also the board of onboard electronics for processing of the sensor matrix was designed and manufactured. © 2021 IEEE

关键词： Pressure sensors

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Bringing Touch to the Edge: A Neuromorphic processing Approach For Event-Based Tactile Systems 5

Bringing Touch to the Edge: A Neuromorphic Processing Approa...

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

作者： Patel, Harshil Vanarse, Anup Carlson, Kristofor D. Osseiran, Adam Brainchip Research Institute Perth Australia Brainchip Inc. Laguna Hills United States University of Western Australia Brainchip Research Institute Perth Australia

ISBN: (纸本)9798350332674

the rise of neuromorphic applications has highlighted the remarkable potential of biologically-inspired systems. Despite significant advancements in audio and visual technologies, research directed towards tactile sensing has not been as extensive. We propose a neuromorphic tactile system for sensing and processing that presents promising results for edge devices and applications. In this study, a neuromorphic tactile sensor, two data encoding techniques, and a two-layer spiking neural network (SNN) deployed on the AKD1000 Akida Neuromorphic System on Chip (NSoC) were used to demonstrate the system's capabilities. Results from experiments on the ST-MNIST dataset showed high accuracy, with the complement-coded variant achieving 93.1%, outperforming previous state-of-the-art models for this dataset. Additionally, an exploratory study showed that early classification was possible, with most samples requiring only 38% of the available events to classify correctly, reducing the amount of data that needs to be processed. the low power consumption and high throughput of both SNN models, with an average dynamic power consumption of 6.37 mW and 7.76 mW and an average throughput of 586 and 589 frames-per-second respectively, make the proposed system suitable for edge devices with limited power and processing resources. Overall, the proposed tactile sensing system presents a promising solution for edge applications that require high accuracy, low power consumption, and high throughput. © 2023 IEEE.

关键词： System-on-chip

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