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检索条件"任意字段=4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023"

共 108 条记录，以下是51-60 订阅

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To diagnose a slight and incipient fault in a power plant thermal system based on symptom zoom technology and fuzzy pattern recognition method

To diagnose a slight and incipient fault in a power plant th...

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4th international conference on machine learning and Cybernetics

作者： Ma, Liangyu Ma, Jin Ma, Yongguang Wang, Bingshu N China Elect Power Univ Sch Control Sci & Engn Baoding 071003 Peoples R China

ISBN: (纸本)3540335846

To diagnose a slight and incipient fault in a power plant thermal system correctly and timely, a new fault recognition approach is put forward by using fault symptom zoom technology(SZT) and fuzzy pattern recognition method. By studying the rules of the faults pertinent to energy and mass balance in a power plant thermal system, a new fault symptom preprocessing method, which is called "fault symptom zoom technology", is put forward to preprocess the fault characteristic parameters. the complexity of the thermal system fault knowledge library can be effectively reduced and the slight fault recognition ability can be. greatly enhanced with SZT. the fault fuzzy pattern recognition method is introduced. A new general-purpose fuzzy recognition function is given, which can fit for various kinds of fault symptoms and is with favorable fault classifying ability. Some examples for incipient and slight fault diagnosis for a power plant thermal system are given to verify the effectiveness of the method.

关键词： Electric power plants

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Scenario model advocating user-driven adaptive document recognition systems

Scenario model advocating user-driven adaptive document reco...

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Proceedings of the 1997 4th international conference on Document Analysis and recognition, ICDAR. Part 2 (of 2)

作者： Bapst, F. Zramdini, A. Ingold, R. Univ of Fribourg Fribourg Switzerland

Assisted document recognition systems have to integrate automatic recognition, manual edition and incremental learning in a single interactive environment. this paper raises the question of the organization of these three kinds of operations. When an analyzer has the ability to improve with use, there is a tradeoff between the benefits of enhancing the accuracy of automatic analysis, and the additional time spent in interacting for feedback communication. the global cost depends then on the sequence of processed entities, and on the relevance of the learning transactions. Notations are introduced to describe the evolution of a recognition session, and possible organization strategies are discussed. then a cost model is presented to allow the comparison between different organization schemes. We describe some concrete experiments of cost measures with the ApOFIS font identification tool and the ScanWorX OCR;the first results show that a user-driven approach can potentially save substantial effort in the recognition process, in comparison with machine-driven systems.

关键词： pattern recognition systems

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Repeating pattern discovery from audio stream

Repeating pattern discovery from audio stream

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4th international conference on machine learning and Cybernetics

作者： Du, Zhen-Long Li, Xiao-Li Lanzhou Univ Technol Lanzhou 730050 Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Peoples R China

ISBN: (纸本)3540335846

In this paper, an effective method to discover repeating pattern from audio is proposed. Since the previous feature extraction methods are usually process monophony audio, for extracting more descriptive features from polyphony audio, Gabor filters bank is introduced. Meanwhile the measure criteria is suggested for qualitatively and quantitatively weighting the discernibility of extracted features. In addition, the presented algorithm is based on the incremental match and has time complexity O(nlog(n)). Experimental evaluations show that our proposed method could extract complete and meaningful repeating patterns from polyphony audio.

关键词： pattern recognition

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Adaptive online multi-stroke sketch recognition based on Hidden Markov Model

Adaptive online multi-stroke sketch recognition based on Hid...

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4th international conference on machine learning and Cybernetics

作者： Sun, Zhengxing Jiang, Wei Sun, Jianyong Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China

ISBN: (纸本)3540335846

this paper presents a novel approach for adaptive online multi-stroke sketch recognition based on Hidden Markov Model (HMM). the method views the drawing sketch as the result of a stochastic process that is governed by a hidden stochastic model and identified according to its probability of generating the output. To capture a user's drawing habits, a composite feature combining both geometric and dynamic characteristics of sketching is defined for sketch representation. To implement the stochastic process of online multi-stroke sketch recognition, multi-stroke sketching is modeled as an HMM chain while the strokes are mapped as different HMM states. To fit the requirement of adaptive online sketch recognition, a variable state-number determining method for HMM is also proposed. the experiments prove both the effectiveness and efficiency of the proposed method.

关键词： pattern recognition

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Multidimensional Time Series Analysis for Anomaly pattern Detection and Interpretation 4

Multidimensional Time Series Analysis for Anomaly Pattern De...

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4th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Deng, Ziling Kang, Jiahua Wang, Xiaoxiao University of Electronic Science and Technology of China Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350359589

the interpretability of multivariate time series anomaly detection is crucial for understanding the reasons behind anomalies, enhancing the usability and credibility of models, and ensuring successful real-world applications. Although there has been considerable research on the interpretability of time series, most are based on black-box models and have certain limitations in revealing the underlying patterns of anomalies. To address this issue, this paper focuses on identifying anomaly patterns in multivariate time series using a white-box machine learning approach, which overcomes the interpretability limitations of traditional methods in high-dimensional dynamic data processing. this approach innovatively enhances the application of Dynamic Time Warping (DTW) in higher dimensions to effectively capture anomaly patterns of any length, while identifying both short-term and long-term anomalies. Experiments on the Yahoo dataset demonstrate its high accuracy and reliability in anomaly pattern detection, particularly in explaining the reasons and impacts of identified anomaly patterns. this innovation marks a breakthrough in anomaly pattern interpretation, showing significant practicality and interpretability in real-world applications, and provides vital support for decision-making and predictive modeling in time series analysis. © 2024 ieee.

关键词： Anomaly pattern Extraction Data Interpretability Multidimensional Time Series pattern recognition

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Metagenomic Face: Applying machine learning to Facial Microbiome Data for pattern recognition and Disease Classification 4

Metagenomic Face: Applying Machine Learning to Facial Microb...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Guru Prasad, M.S. Devadas, Raghavendra M Hiremani, Vani Jahan, Afsar Praveen Gujjar, J. Dahiya, Aryan Department of Computer Science and Engg Dehradun India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India University Pune India Department of Computer Applications Dehradun India Faculty of Management Studies Bengaluru India

ISBN: (纸本)9798350349900

this innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. this framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. the investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. these findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. the Support Vector machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. these additional insights contribute to a comprehensive understanding of the Metagenomic Face framework's efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis. © 2024 ieee.

关键词： Adversarial machine learning

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Research on Vibration Signal Diagnosis Platform System Based on Computer Fuzzy Logic 4

Research on Vibration Signal Diagnosis Platform System Based...

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4th ieee international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Bo, Liu Yang, Song Zhenwang, Lv Zhaoran, Meng Dalian Scientific Test and Control Technology Institute Dalian China

ISBN: (纸本)9798350361643

An on-line vibration detection method for power transformers is introduced. this design includes sensor module, real-time processing module, PC module. the sensor module collects the vibration, voltage, load voltage and other signals of the motor and transmits them to the processing unit on site. the real-time processing module transmits the processed information to the PC for display, storage, analysis and other functions. A real-time monitoring system for power transformer vibration based on virtual instrument /Compact RIO is developed. A new signal detection algorithm is established by combining wavelet energy information entropy theory with SVM. A frequency domain analysis algorithm based on wavelet analysis is proposed. the normalized feature vector is the input signal of support vector machine. then a support vector machine (SVM) multi-classifier model based on multi-class learning is proposed. Experimental results show that the SVM classifier proposed in this paper has high classification accuracy under multiple working conditions of power transformer. © 2024 ieee.

关键词： pattern recognition

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Research on YOLOv4 Object Detection Based on K-means Algorithm and Fusion Attention Mechanism 4

Research on YOLOv4 Object Detection Based on K-means Algorit...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Peng, Chufei Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798350308082

Object detection technology is a machine learning application based on deep learning and computer vision technology, which can replace error-prone manual inspections in various scenarios, such as mask recognition applications. In view of the problems of easy omission and difficult error detection in traditional object detection algorithms, this paper proposes a research on YOLOv4 object detection based on K-means and fusion attention mechanism, with mask recognition as the research object. Firstly, the preprocessing of the dataset is carried out. the K-means algorithm is used to cluster and analyze the face mask dataset, generate more suitable anchor box prior boxes, and use the Mosaic method for data augmentation. then, the SE module and SPP module of the channel attention mechanism are introduced to improve the basic YOLOv4 model, and the SE-SPP3-YOLOv4 detection model is constructed. the experiment shows that the proposed improved model has significantly improved the detection rate and accuracy of both face and mask targets, and can perform accurate recognition of the wearing of masks on faces in specific application scenarios. © 2023 ieee.

关键词： Deep learning

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IProceedings of the 4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

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Pathological Brain Detection Based on Online Sequential Extreme learning machine 4

Pathological Brain Detection Based on Online Sequential Extr...

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4th ieee international conference on Progress in Informatics and Computing (ieee PIC)

作者： Lu, Siyuan Wang, Hainan Wu, Xueyan Wang, Shuihua Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou 310027 Zhejiang Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Jilin Peoples R China State Stat Bur Key Lab Stat Informat Technol & Data Min Chengdu 610225 Sichuan Peoples R China CUNY City Coll New York Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781509034840

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. the experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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