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High-precision steel plate corrosion rate monitoring model based on dilated convolution 3

High-precision steel plate corrosion rate monitoring model b...

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3rd international conference on intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024

作者： Gao, Ruixin Ma, Yisong Xu, Hongxing Niu, Ben Cen, Kaixin Wang, Li Liu, Peng Shenzhen Power Supply Bureau Co. Ltd. Shenzhen518000 China School of Automation Engineering Northeast Electric Power University Jilin132012 China

ISBN: (纸本)9781510683105

Steel plate corrosion causes huge economic losses to industrial manufacturing environments and may induce accidents. Therefore, monitoring the corrosion rate of steel plates is very important in the industrial field. However, the accuracy of traditional steel plate corrosion rate monitoring is difficult to meet the requirements of industrial sites. For this reason, it is particularly important to design a high accuracy steel plate corrosion rate monitoring model. In order to solve this problem, this paper proposes a high accuracy steel plate corrosion rate monitoring model based on Dilated convolution, named HDCNN. Starting from the fine-tuning paradigm of Dilated convolution, a new plug-and-play self-attention mechanism module is proposed, named JAM module, to help the model extract more effective information, thereby improving the monitoring accuracy of the model. At the same time, in order to verify the effectiveness of the model, this article self-made a set of data sets with multiple complex working conditions (different lights and different experimental scenarios), and conducted comparative experiments under the data sets with multiple complex working conditions. Experimental results show that among the three groups of mainstream models, the HDCNN model has the highest test accuracy (Accuracy: 93.1785 %). © 2024 SPIE.

关键词： Steel corrosion

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Third international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

Third International Conference on Digital Technologies, Opti...

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3rd international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

ISBN: (纸本)9781510681590

The proceedings contain 40 papers. The topics discussed include: calculation of soft magnetic high-entropy alloys systems;enhancement of tribological properties of tool metal-ceramic materials by high-power ion beams;some features of the formation of gas hydrates in solutions of common salt;study of the features of formation of gas hydrates in solutions of common salt and calcium chloride;development of mathematical models for nonlinear nonstationary random processes;detecting anomalies in forest input data in a supply chain system using machine learning;grid approximation in the problems of synthesis of UAV control systems;development of an emotion and age recognition model based on the combination of local binary patterns and squeeze-and-excitation block method;and simulation modeling as a decision support system tool for improving the environmental safety of road transport.

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Design of Modern Open Distance Education System based on Random Sampling Speech Algorithm and intelligent Action Acquisition Algorithm 3

Design of Modern Open Distance Education System based on Ran...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Zhao, Jinfeng Wuxi Open University Wuxi214011 China

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Stochastic resonance can use noise energy to amplify weak signals and suppress noise. Based on this principle, a method for extracting weak speech signals based on adaptive stochastic resonance is proposed, which is combined with subsampling to realize the detection of weak speech signals under strong noise background.. The learning support service system is designed and implemented using MVC architecture and AJAX and other computer network technologies. The operation of the system was analyzed during two years of system operation. An intelligent data acquisition algorithm based on time slide and circular queue is proposed, with the ultimate goal of ensuring the quality of online education, the overall program planning of the learning support service system is carried out, and through continuous evaluation and modification. © 2022 IEEE.

关键词： Stochastic systems

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Analysis of Approaches for Identifying Threats in the Internet of Medical Things (IoMT) and Exploration of Research Frontiers 3

Analysis of Approaches for Identifying Threats in the Intern...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Mishra, Abhinav Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350343274

The contemporary healthcare landscape has witnessed a rapid integration of the Internet of Things (IoT), particularly within the realm of medical devices, forming the Internet of Medical Things (IoMT). While IoMT offers transformative benefits, it also introduces significant security challenges. This study presents an innovative and holistic approach to comprehensively analyze and identify threats in the IoMT environment. The proposed method involves a wellstructured process encompassing data collection, preprocessing, feature extraction, and threat identification through advanced machine learning algorithms. Raw data, often noisy and redundant, is initially collected from various IoMT devices, which primarily consist of crucial medical data such as vital signs and diagnostic information. Preprocessing is then applied to clean, normalize, and transform this raw data into a suitable format for subsequent analysis. Feature extraction is pivotal to capture meaningful information, essential for effective threat identification, and involves the identification of pertinent features related to patient health, device behavior, and system interactions. Utilizing machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Convolutional Neural Network (CNN), the proposed method showcases superior performance in comparison to traditional methods. Additionally, the proposed method exhibits significantly lower execution times, highlighting its efficiency and suitability for real-time threat detection, a crucial requirement in the dynamic IoMT landscape. This research stands as a significant contribution to bolstering the security and reliability of IoMT systems, ensuring a safer healthcare environment for patients and practitioners. © 2023 IEEE.

关键词： Feature extraction

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IOT Management Programmable Telemetry and Machine learning for Cyber Security 3

IOT Management Programmable Telemetry and Machine Learning f...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Takveer Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350399264

As more and more Network connected Parts of life (IoT) gadgets are acquired from a wide variety of vendors and put into widespread use, the cyber security risks they face are rapidly expanding. The need for telecom operators to exert authority over these endpoints has therefore grown. The data analysis used by current network monitoring systems is often time-consuming, expensive, and inflexible since it requires specialized accelerators on network switches or extensive packet inspection in software regulate IoT devices based on the behaviour of the network. This is accomplished by combining the benefits of configurable. Here are the three main things we've added: We analyse the traffic traces of 17 genuine consumer IoT devices collected in our lab over the course of six months, and we find a collection of car traffic (per-device) whose time-series characteristics calculated at numerous scales (from a minute to with an hour) describe the network behaviour of different IoT device kinds, and their operational phases (i.e., booting, actively engaging with the user, or being inactive);Our second contribution is the creation of a multi-step Thirdly, we provide quantitative analysis of the practical use of our monitoring system in detecting behavioral shifts. Using actual traffic traces, we train and validate our models. © 2023 IEEE.

关键词： Internet of things

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intelligent Recording Live Broadcast of Practical Training Based on AI Technology 3

Intelligent Recording Live Broadcast of Practical Training B...

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3rd international conference on Artificial Intelligence and Computer engineering, ICAICE 2022

作者： Yu, Changle Li, Wenwen Wan, Jia Xu, Dahai Bu, Jin Skill training center of State Grid Liaoning Electric Power Co. Ltd Liaoning Jinzhou121000 China

ISBN: (纸本)9781510663473

At present, the use of artificial intelligence in the front line of teaching has begun to take shape. Artificial Intelligence (AI) technology has triggered a new wave, and the innovation of science and technology has brought about the diversification of teaching methods. Fragmentary learning is everywhere all the time, online and offline are organically integrated, and knowledge breakpoints inside and outside the classroom are seamlessly linked up. Our classroom is changing with each passing day. This paper introduces AI technology to carry out the research of intelligent recording live broadcast of practical training, aiming at the problem of high picture stutter rate in the current process of recording live broadcast of practical training, which affects the subjective experience. Aiming at the practical training courseware with poor compatibility, the courseware is converted into H5 form courseware. On this basis, the data in the courseware is encoded and decoded. Through the real-time processing of guidance and control, the synchronization of video and audio recording, playing, and switching is realized. By using AI technology, live subtitle bars are automatically output. Through the comparative analysis of the application effect of the new recording live broadcast method, it is concluded that this method can effectively reduce the picture stutter rate and bring a better subjective experience to users. © 2023 SPIE.

关键词： Artificial intelligence

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Efficientnet for Brain Tumor Detection from MRI 3

Efficientnet for Brain Tumor Detection from MRI

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Gayathri, G. Sindhu, S. Nss College of Engineering Department of Cse Palakkad India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The human brain is the primary controller of the humanoid system. The unusual expansion of the brain tissues leads to brain tumor. The continuous escalation of brain tissue leads to brain cancer. Computer vision plays an inevitable role in the field of medical science, and, in it, magnetic resonance imaging techniques are used to detect brain tumors. In the realm of image categorization, deep learning is a core topic. It currently has quite a promising potential in terms of brain tumor classification and segmentation. This work's key principle is to build a deep convoultional neural network for detecting brain tumors. In the proposed model, the tumor region is first segmented from the MR images. Second, data augmentation is used to allow effective training, and, subsequently, a fine-tuned model EfficientNet is used for detecting multi-class brain tumor. The model is trained using brain tumor dataset. The method achieved an average accuracy of 97.35%. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Usability Study of a Virtual Reality-Based Ecological Battery for Memory Assessment: A Pilot Study 3

Usability Study of a Virtual Reality-Based Ecological Batter...

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Mancuso, Valentina Arlati, Sara Saritas, Selen Naz Borghesi, Francesca Di Santo, Simona Gabriella Cavallo, Marco Pedroli, Elisa Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - Stiima National Research Council of Italy - Cnr Italy University of Turin Department of Psychology Turin Italy Irccs Fondazione Santa Lucia Department of Clinical and Behavioral Neurology Roma Italy ECampus University Faculty of Psychology Novedrate Italy Irccs Istituto Auxologico Italiano Department of Geriatrics and Cardiovascular Medicine Milan Italy

ISBN: (纸本)9798350378009

The reliability and validity of conventional neuropsychological assessments have been called into question because they have a narrow ecological perspective. Traditional paper and pencil approaches frequently fail to accurately simulate real-world situations, which can result in possible misunderstandings of cognitive abilities. Virtual Reality and 360° videos offer potential remedies to these restrictions by offering immersive, lifelike environments for cognitive evaluations. The objective of this study is to assess the usability of the ecological and automated memory battery (V-MEB) which incorporates 360° movies and head-mounted displays. Eight participants were subjected to one memory tasks in a virtual city setting. Their feedback on usability was gathered using the System Usability Scale, Senior Technology Acceptance Model, Thinking Aloud Protocol, and the Independent Television Commission Sense of Presence Inventory (ITC-SOPI). The quantitative data demonstrated a high score for usability, however the qualitative data from TAP identified particular usability difficulties pertaining to interface design and interaction style. It is crucial to address these difficulties in order to enhance the user experience and effectiveness of cognitive evaluations that rely on virtual reality technology. © 2024 IEEE.

关键词： Virtual environments

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automated Prediction and Occurence of Tachyarrhythmia using Deep learning Techniques 3

Automated Prediction and Occurence of Tachyarrhythmia using ...

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Naresh, D. Reddy, G. Himaja Kondiparthi, Tharuni Jashmika, V.S. Kumari, Ch. Usha Griet Dept. of Ece Hyderabad India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Cardiac arrest is the top global cause of death. Diagnosis and treatment is too late due to lack of awareness among the people. Tachyarrhythmia is the abnormal heart rate which may lead to cardiac arrest in the worst cases. This project aims to detect, predict and find the occurrence of Ventricular Tachyarrhythmia using Neural Networks such as CNN and DNN. Convolutional Neural Networks(CNN) were prepared and tried with two competences, HRV and QRS complex shape, for cross validation. The prospective model highlights the High Rate Variability(HRV) which is extricated from electrocardiograms (ECGs) as the contribution to prepare AI calculations. High Rate Variability(HRV) pulses are time variants with two progressive QRS buildings (Q, R, and S waves in ECG). Deep learning calculations utilize a foreordained coherent architecture to break down information and arrive at comparative resolutions as people. Profound learning accomplishes this with brain organizations, which are multifaceted forecasts. HRV is used to define the variation in time interval over two successive QRS waves. Deep learning uses multi-layered algorithms, also called Neural Networks to teach the computers to work in patterns similar to the human brain. The prospective approach will be deployed to assist doctors in VTA detection and hence expedited, which is critical for effective treatment will be improved by preprocessing, integrating the data, that helps in better accuracy to predicate over different features extraction, so it is possible to increase prediction accuracy to 83.8%. © 2022 IEEE.

关键词： Electrocardiograms

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Deep learning Techniques to Detect learning Disabilities Among children using Handwriting 3

Deep Learning Techniques to Detect Learning Disabilities Amo...

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3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Vilasini, V. Banu Rekha, B. Sandeep, V. Charan Venkatesh, Vishnu Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India Psg College of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

In today's world, we come across children facing certain disabilities which pose as obstacles and hinder their academic growth. Some of these disabilities are explicitly visible to the common eye, whereas some are hard to find and need extra attention. One such condition is Motor Dysgraphia which challenges an individual's ability to write. The common practice that is followed to identify such a condition among children is quite expensive and creates a mental strain on them. There are many intelligent computational methods that have been proposed with bearing a wide range of performances, however they are not quite standardized for assessment. Fortunately the advancements in Deep learning techniques have been proven beneficial in automating this identification task. In this study, learning Disability Detection system is built using Deep learning techniques. The project's application is mainly focused on the pre-school and primary school children. This model analyses the child's handwriting and classifies whether the child is subjected to such a disorder or not. Deep learning models - Convolutional Neural Networks (CNN) and Vision Transformers are adapted and their Disability Detection performances are analyzed and compared. © 2022 IEEE.

关键词： Convolutional neural networks

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