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检索条件"任意字段=5th International Conference on Machine Learning and Human-Computer Interaction, MLHMI 2024"

共 559 条记录，以下是341-350 订阅

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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...

引用

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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Exploring the Architecture and Functions of Intelligent Database Management Systems

Exploring the Architecture and Functions of Intelligent Data...

引用

Artificial Intelligence and computer Engineering (ICAICE), international conference on

作者： Xiaozhong Wang Yang Wang Guangchao Deng Yunzhi Shi Inspur Cloud Information Technology Co. Ltd. Jinan Shandong China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

this paper explores the architecture and functions of Intelligent Database Management Systems (IDBMS), which integrate advanced artificial intelligence (AI) technologies to enhance traditional database management. By addressing the limitations of conventional systems, IDBMS aim to improve query optimization, resource utilization, and user interaction through machine learning, predictive analytics, and natural language processing. the paper outlines the core components and architectural models of IDBMS, details their functionalities, presents case studies demonstrating their effectiveness, and discusses future trends and challenges in the field.

关键词： Adaptation models Query processing Face recognition computer architecture Market research Transformers Natural language processing Resource management Artificial intelligence Deductive databases

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Gesture Control System Using computer Vision Techniques

Gesture Control System Using Computer Vision Techniques

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： M. Mary Linda Karuppusamy. M Kaviya. M Udhayakumar. C Department of Electrical and Electronics Engineering KIT-Kalaignarkarunanidhini Institute of Technology Coimbatore

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

the purpose of the introduction of the gesture control technology was to close the gap between the physical and virtual worlds. Under the direction of mathematical algorithms, human interaction can converse with the digital world. Proposals and implementations of numerous algorithms and techniques have been made in an attempt to realize the objective of gesture recognition and digital world communication. Accelerometers and other devices can be used to track movements. this research work focuses on building and implementing a desktop computer using a webcam, a laptop running Windows 10, and the PyCharm program. In order to comprehend human expressions and facial expressions in response to commands, this project demonstrates technological advancements in computer vision, machine learning, and sensor technologies. this allows to easily power a variety of electronic devices, including game consoles, smart homes, smartphones and cars. With the use of gesture control systems, users can communicate with smart homes through freely interacting or easily understood motions, doing away with the need for more conventional physical controllers like remotes or Smartphone apps. this enables the user to wave hand in front of the computer or laptop to control specific functions. the main motive is to reduce the cost and increasing reliability by reducing the hardware usage. To be responsive and smooth, the system uses a mix of hardware and software, including IoT devices, machine learning algorithms, and in-depth cameras.

关键词： computer vision Portable computers Webcams Smart homes Control systems Hardware Software User experience Windows Smart phones

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A Healthcare Data Sharing Scheme Based on Homomorphic Encryption and Federated Metric learning

A Healthcare Data Sharing Scheme Based on Homomorphic Encryp...

引用

Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Haoming Zhang Zhentao Liu Lang Wu Han Li School of Applied Science Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Medical data is highly sensitive, often leading to “data silos” in sharing processes. To address this issue, this paper combines blockchain and federated metric learning technologies, and innovatively integrates them with homomorphic encryption technology to propose a privacy protection system suitable for application in medical data sharing scenarios. the system employs federated learning to train a global model across distributed client nodes without sharing raw data. Metric learning classifies clients as benign or malicious based on data similarity, only aggregating models from benign clients to improve efficiency and mitigate data distribution issues. Additionally, differential privacy and homomorphic encryption are used to further protect data privacy by preventing the original data from being reverse deduced and model parameter leakage during aggregation. Experimental results show that the system can achieve a prediction accuracy of 89.102564% under different data distributions, and its performance on medical datasets has been significantly improved compared to traditional federated learning.

关键词： Measurement Differential privacy Accuracy Federated learning Information sharing Data models Regulation Blockchains Protection Homomorphic encryption

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TL-DREN: Transfer learning Based Detection and Recognition of Electricity Nameplates

TL-DREN: Transfer Learning Based Detection and Recognition o...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Jining Li Bao Wen Kun Cai Yanming Li Ming Ma Xin Li Shufeng Tan Danqi Wu Juyuan Wang Guangdong Electric Power Research Institute Guangzhou China Guangdong Power Grid Co. Ltd. Guangzhou China South China University of Technology Guangzhou China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

this paper presents a novel approach that integrates traditional image processing techniques with deep learning to enhance the accuracy and robustness of fuzzy text detection and recognition in nameplate images of power equipment. Initially, traditional methods are employed to locate the nameplate and extract information, followed by perspective transformation to correct image distortions. Subsequently, deep learning models are utilized for text detection and recognition. Two specialized datasets were constructed for this study: one for text detection, comprising 841 high-resolution images captured under various environmental conditions, and another for text recognition, consisting of 42,556 cropped text region images derived from the corrected nameplate images. the PP-OCRv4 model was fine-tuned on our curated dataset to improve its performance on text detection and recognition tasks specific to power equipment nameplates. Experimental results demonstrate that our proposed method achieved an F1-score of 0.524 for text detection and an accuracy of 0.82 for text recognition, showcasing the potential of this approach to enhance the efficiency of automated management of power equipment.

关键词： Deep learning human computer interaction Image recognition Accuracy Text recognition Electricity Transfer learning Text detection Robustness Optimization

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Active learning in Industrial Defect Detection: A Dynamic Approach to Uncertainty and Representativeness

Active Learning in Industrial Defect Detection: A Dynamic Ap...

引用

Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Yuhao Shen Feifei Liu Zhen Zhang School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China School of Electrical Engineering and Automation Jiangxi University of Science and Technology Ganzhou China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

the storage and annotation of industrial data is a costly and time-intensive work in developing a defects detector. To overcome this challenge, this study proposes an effective annotation workflow and an active learning strategy that integrates uncertainty and representativeness to construct a reliable defects detection model. the annotation workflow involves both the advisor and the labeling team for the inspection of industrial data that depends on specialized knowledge in a particular area. the active learning strategy combines the uncertainty-based and the representativeness-based sampling metrics flexibly to assess the informativeness of the unlabeled data. Besides, a training strategy for active learning models is used to address not only data uncertainty but also the generalization of feature distribution across all data. We benchmark the proposed method on Bottled Baijiu Defect Detection Dataset, utilizing RetinaNet as the baseline detector. Results demonstrate that the proposed method significantly reduces labeling costs and data storage requirements, which is crucial for industrial defect detection.

关键词： Training Measurement Uncertainty Costs Annotations Active learning Memory Labeling Reliability Defect detection

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Enhancing Brain Tumor Diagnosis with MedMamba: A Novel Application in Neural Imaging Analysis

Enhancing Brain Tumor Diagnosis with MedMamba: A Novel Appli...

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Intelligent Computing and human-computer interaction (ICHCI), international conference on

作者： Xinrui Chen School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Brain tumors pose a great threat to human health and need to be diagnosed at an early stage. MRI plays an important role in assisting diagnosis due to its clear soft tissue visualization. Currently, the application of deep learning techniques in medical imaging shows the possibility of outperforming humans in terms of diagnostic accuracy and speed. this study explores the application of transfer learning and MedMamba models for brain tumor diagnosis, with a particular focus on their role in performing MRI brain tumor image classification. the MedMamba model is introduced and evaluated, which is a new adaptation of the Mamba framework optimized for the medical imaging field. this study not only compares the performance of MedMamba models with traditional deep learning models such as Convolutional Neural Network and Vision Transformer, but also highlights the effectiveness of transfer learning in improving these models for specialized tasks in medical diagnosis. the main purpose is to describe the performance optimization capabilities provided by transfer learning and to demonstrate the inherent advantages of the MedMamba model. the model combines an innovative design with a streamlined architecture for efficient image processing. the positive role of transfer learning in the task of brain tumor image classification and the potential of MedMamba to set a new benchmark in this area are highlighted through comprehensive testing and evaluation of results on a brain MRI image dataset.

关键词： Deep learning Adaptation models Analytical models Accuracy Magnetic resonance imaging Transfer learning Brain tumors Brain modeling Medical diagnostic imaging Image classification

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Detecting Feet's Bending Patterns With Smart Insoles

Detecting Feet's Bending Patterns With Smart Insoles

引用

Pattern Recognition and machine learning (PRML), IEEE international conference on

作者： Yuchen Zhong Qijun Ying Xiaohui Cai dept. of Computer Science and Technology University of Science and Technology of China (USTC) Hefei China dept. of Data Science University of Science and Technology of China (USTC) Hefei China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. the smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring.

关键词： Legged locomotion Measurement units three-dimensional displays machine learning algorithms Measurement uncertainty Inertial navigation Bending Recording Monitoring Foot

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Exploring Predictive Models for Stress Detection: A machine learning Approach

Exploring Predictive Models for Stress Detection: A Machine ...

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： V Kalai Vani Fathima Ghouse Department of Computer Science and Engineering Adhiyamaan College of Engineering Krishnagiri district Tamilnadu India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

the world today is full of conflicts. Furthermore, the intensity and impact of stress can vary widely from person to person, influenced by factors such as personality, coping skills, and overall mental and physical well-being. A questionnaire-based approach is used, where users answer a series of questions about their experiences and feelings. While applying machine learning algorithms to analyze collected data for stress levels can provide valuable insights, it’s crucial to consider ethical considerations, privacy concerns, and the need for a holistic approach that incorporates both quantitative data and qualitative understanding of an individual’s experiences. Additionally, the classification task is to determine if people are currently suffering from stress. However, it’s important to approach the implementation of such interventions with caution, ensuring that ethical guidelines are followed, and prioritizing individual privacy and consent in order to strike a balance between technological advancements and ethical considerations.

关键词： Ethics Analytical models Privacy machine learning algorithms Image processing Stacking human factors

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Gesture Recognition on Textile-Based Pressure Sensor Array

Gesture Recognition on Textile-Based Pressure Sensor Array

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Electronic Engineering, Information Technology & Education (EEITE), international conference in

作者： Ilknur Celik Munire Sibel Cetin Kadir Ozlem Cetin Ozgur Atalay Asli Tuncay Atalay Gökhan Ince Computer Engineering Istanbul Technical University Textile Engineering Istanbul Technical University

ISBN: (数字)9798350372878

ISBN: (纸本)9798350372885

Wearable technologies are in pursuit of designing portable devices worn by people. In this paper, gesture recognition on a sleeve having a capacitive textile-based pressure sensor array has been developed. machine learning models are generated to recognize a set of gestures by accumulating capacitance values read from a sensor array during the execution of gestures. the performance of the gesture recognition system is evaluated in the experiments conducted with human test subjects. Real-time gesture prediction is achieved using the trained model. the performed gestures and predicted data are collected and analyzed, resulting in accuracies up to 86'.

关键词： Pressure sensors Performance evaluation Accuracy Education Gesture recognition machine learning Predictive models

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