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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Fault pattern recognition Method of Rolling Bearing Based on MTF-2DWDCNN 3

Fault Pattern Recognition Method of Rolling Bearing Based on...

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3rd international conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xun, Zhiwen Miao, Xiaodong Yu, Hu Gu, Yinji Nanjing Tech University School of Mechanical and Power Engineering Nanjing China Nanjing Tech University School of computer science and technology Nanjing China

ISBN: (纸本)9781665492812

2-D fault feature images have advantages as neural network input for fault pattern recognition, but the most deep image recognition algorithms are non-sensitive to the time-series features of images. This paper proposes a new algorithm named MTF-2DWDCNN based on Markov transfer field(MTF) jointly improved deep convolutional neural networks with wide first-layer Kernel(WDCNN). Firstly, the original 2-D sequence signal is converted into a 2-D time series image by MTF, used as the input of the neural network;secondly, the convolution mode of the original WDCNN is improved to adapt to 2-D time series;finally, the accuracy and error experiments are carried out to verify its effectiveness. Experimental results show that the method proposed in this paper has certain advantages over traditional algorithms regarding recognition accuracy. © 2022 IEEE.

关键词： Bearing deep learning fault diagnosis MTF

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Robust Fault Diagnostics of Industrial Motors: The Signal-to-Image Approaches for Sensor data Using Multi-Task Transformers with Diverse Attention Mechanism

Robust Fault Diagnostics of Industrial Motors: The Signal-to...

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international conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Sudipta Mukhopadhyay Indrajit Kar Zonunfeli Ralte Zensar Technologies Bangalore India

Vibration analysis serves as a valuable method to identify defects within mechanical systems. This process involves monitoring the auditory signals generated by the system and discerning patterns that signify potential issues. Nevertheless, due to the intricate nature of motors, relying solely on one modality (such as acoustic signals) might prove inadequate for comprehensively detecting all faults. Our research introduces a novel approach to enhance fault detection in mechanical systems, specifically focusing on motors. A more comprehensive system health assessment is achieved by utilizing multi-modal vibration analysis, combining acoustic signals and heat signatures. Effective information fusion is facilitated by integrating attention-based transformers, capable of capturing cross-modal correlations. The proposed neural network employs classification and regression loss functions for fault identification and severity assessment respectively. Evaluation on a motor fault dataset demonstrates significant classification accuracy improvements compared to conventional deep and machine learning methods. Incorporating a signal-to-image transformation further aids in fault pattern recognition. This method offers a promising avenue for accurate and comprehensive fault diagnosis in mechanical systems, showcasing the potential of multi-modal analysis and attention mechanisms for industrial equipment maintenance.

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Optimizing GIS Spatial Analysis Method by Using Big data mining Technology

Optimizing GIS Spatial Analysis Method by Using Big Data Min...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yao Chen Wuhan Donghu University Wuhan China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the advent of the era of big data, geographic information system (GIS) is facing the challenge of massive, multi-source and heterogeneous data when processing and analyzing spatial data. This paper discusses how to use big data mining technology to optimize GIS spatial analysis methods to improve the efficiency and accuracy of spatial data processing. Firstly, the research emphasizes the importance of data preprocessing and cleaning, and ensures the quality of data through data integration, transformation and specification. Then, the paper introduces the process of spatial data mining in detail, including data import, feature extraction, and the selection and application of mining algorithms, such as cluster analysis, association rule mining and classification prediction. Through practical case study, this paper shows how to apply big data mining technology to identify the spatial pattern and temporal variation law of urban traffic flow, which provides a scientific basis for urban planning and management. Finally, this paper points out that although big data mining technology has made remarkable progress in GIS spatial analysis, it still needs to face challenges such as spatial heterogeneity, spatial scale effect, data integration and spatial data visualization. Future research needs to comprehensively use spatial statistics, machine learning, data mining and other methods, combined with domain knowledge for in-depth research and analysis. Through these methods, big data mining technology can be used more effectively to optimize GIS spatial analysis methods and provide more accurate spatial data analysis support for decision makers.

关键词： Accuracy data analysis Urban planning data integration Big data Prediction algorithms Spatial databases data mining Traffic congestion Geographic information systems

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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

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2022 3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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An Impact of Poisoning Attacks on machine learning Algorithms

An Impact of Poisoning Attacks on Machine Learning Algorithm...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Lourdu Mahimai Doss P M Gunasekaran Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

machine learning models, ubiquitous in domains like natural language processing and image recognition, are vulnerable to adversarial poisoning attacks, where malicious actors manipulate training data to induce erroneous predictions. This study delves into the susceptibility of Support Vector machine (SVM) models to such attacks, employing the Gradient Ascent Poisoning (GAP) approach. The findings reveal a substantial impact of GAP on SVM performance, leading to a notable decline in accuracy from 81% to 73%. Beyond presenting empirical evidence, the paper highlights the broader challenges of detecting and mitigating poisoning attacks, underscoring the need for robust security measures in machine learning systems. By illuminating the effectiveness of adversarial poisoning techniques and their ramifications, this research contributes to the ongoing endeavor to enhance the resilience of machine learning models in practical applications.

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Integrating Bi-LSTM and Extended Geological Constraints Method for Automatic Logging Curve Stratification

Integrating Bi-LSTM and Extended Geological Constraints Meth...

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Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Xiang Li XiaLin Zhang ZhangLin Li ZhengPing Weng School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Logging curve stratification plays a crucial role in the interpretation of geological data. As the volume of logging data increases, logging curve stratification becomes increasingly challenging and time-consuming. Current automated log stratification methods primarily rely on pattern recognition techniques, which are limited to processing single curves and overlook the complex nonlinear relationships between different logging curves, resulting in suboptimal performance in thin layer stratification. In this study, we propose a deep learning-based approach that simultaneously handles multiple logging curves while incorporating geological constraints to enhance the impact of spatial features on logging curve stratification. Furthermore, we optimize the loss function and introduce a new evaluation metric to better assess the model's performance. Experimental results demonstrate that our proposed method performs better in terms of stratification error and thin layer detection.

关键词： Measurement Deep learning Cloud computing Accuracy Geology Computational modeling Big data applications data models pattern recognition Software engineering

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A Review of Healthcare Recommendation Systems Using Several Categories of Filtering and machine learning-Based Methods 3

A Review of Healthcare Recommendation Systems Using Several ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Kumar, Pardeep Kumar, Ankit Baba Mastnath University Asthal Bohar Rohtak India

ISBN: (纸本)9781665462006

Health-care Recommendation Systems (RSs) is a famous application of AI (artificial intelligence) that involves the investigators worldwide. Several ML methods are utilized to develop health-care RSs. Selecting the best ML method to give customers a service/product is an interesting task in health-care RSs. Currently, it is observed in the buying pattern of individuals from in-shop to online the outcome in accessibility of OI (online information) which is exponentially improved day by day. In shopping scenarios, these RSs must be capable of advising reliable solutions to the customers. The medical health-care RSs have to manage a large quantity of data by cleaning the reliable data depending on the data analysis, generated on the user behaviour made by the customers during their online terms. RSs can suggest suitable items to customers based on their interests and existing favourites, leading to improved sales. This paper described the overview of health-care RS, phases of RSs such as IC (information collection), learning, and recommender or predictor. This paper presents an overview of three main methods used to construct health-care RSs that are content-based, collaborative filtering (CF)-based and hybrid (HB)-based filtering methods and discuss several ML-based methods used in health-care RSs with their performance comparison. The ML-based methods include;MFM (matrix factorization model), CNN, MLP, SVD, etc. © 2022 IEEE.

关键词： Health care

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Harmonic Representation for CNN-LSTM Automatic Chord recognition 3

Harmonic Representation for CNN-LSTM Automatic Chord Recogni...

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3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Ito, Tsuyoshi Arai, Shuichi Tokyo City Univ Grad Sch Engn Setagaya Ku 1-28-1 Tamazutsumi Tokyo 1588557 Japan

ISBN: (纸本)9781665425803

Paper Since Chord progression is the element that determines the harmony of a piece of music, Automatic Chord recognition (ACR) from audio signals is a crucial task in the field of Music Information Retrieval(MIR). Recently, various models using deep learning have been proposed, but there are few studies on their input features. Notes parts of the chord are the fundamental note, and its overtone ringed simultaneously. In order to model these audio signals efficiently, feature transforms such as "Constat-Q-Transform(CQT)" is used. However, due to the super-position of fundamental notes and overtones of various instruments in polyphonic music, it is considered difficult to model chords even by deep learning. Therefore, we focused on the structure, including fundamental notes are on the logarithm and its overtones are on the linear. In this paper, we propose a feature representation that can represent overtone structure for each fundamental note. Based on these feature representations, data-driven approach to learn the chord by CNN-LSTM model. We evaluated performance using 383 songs with publicly available annotations, and achieved the same performance with approximately one-tenth of the number of parameters than the existing methods.

关键词： Deep learning Signal Processing pattern recognition Music Information Retrieval Automatic Chord recognition

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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification 4

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Ananthi, K. Balakrishnan, M. Pandi, M. Senthil Madasamy, N. Dr.Mahalingam College of Engineering and Technology Artificial Intelligence &data Science Tamil Nadu Coimbatore642003 India Dr.Mahalingam College of Engineering and Technology Computer Science and Engineering Tamil Nadu Coimbatore642003 India

ISBN: (纸本)9781665474368

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. The tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. The problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. The experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library. © 2022 IEEE.

关键词： Support vector machines

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SGBCD: A data Discretization Algorithm Based on Granular-ball Computing and Attribute Significance

SGBCD: A Data Discretization Algorithm Based on Granular-bal...

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Electrical Engineering, Big data and Algorithms (EEBDA), IEEE international conference on

作者： Weiqi Chen Chongqing Vocational College of Transportation Chongqing China

As an important method in data preprocessing, discretization can effectively reduce the size of data, generate concise semantic representation, obtain valuable knowledge and information contained in big data, which is of great significance in the fields of data mining and machine learning. Nevertheless, most of the traditional discretization algorithms don't consider the distribution of sample and difficulty in setting parameters during interval partitioning, which leads to a decrease in efficiency and classification accuracy. A data discretization algorithm based on granular-ball computing and attribute significance is proposed in this paper, which is a multi-granularity method. Firstly, by introducing the granular-ball computing, the difficulty of parameter optimization is reduced and the efficiency of the algorithm is improved. At the same time, interval partitioning adaptively fits the original data distribution, reducing the occurrence of uneven interval partitioning and improving classification accuracy. Then, the randomness of attribute selection leads to instability in interval partitioning results. By introducing attribute significance and prioritizing the selection of attributes with high importance, this paper further improves the classification accuracy. Compared to other excellent discretization algorithms in the experiments, the proposed algorithm shows an ideal performance.

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