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12th International Conference on Intelligent Systems and Knowledge Engineering (IEEE ISKE)

作者： Du, Yimin Wu, Guixing Tang, Guolin Univ Sci & Technol China Sch Software Engn Hefei Anhui Peoples R China Beijing Univ Technol Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781538618295

Clustering plays an important role in data mining and machine learning. Then, intuitionistic fuzzy sets (IFSs) are flexible and practical in dealing with vagueness and uncertainty problems. To cluster the information expressed by intuitionistic fuzzy data, this paper proposes the joint training auto-encoder based intuitionistic fuzzy clustering algorithm. Firstly, we propose the auto-encoder based intuitionistic fuzzy clustering by utilizing similarity measure of IFSs, auto encoder and k-means algorithm. Then, we propose the joint training auto-encoder based intuitionistic fuzzy clustering algorithm by utilizing the proposed auto encoder based intuitionistic fuzzy clustering and two kinds of similarity measures for the clustering analysis of intuitionistic fuzzy data. Lastly, several experiments are provided to verify the effectiveness of the proposed intuitionistic fuzzy clustering algorithms.

关键词： intuitionistic fuzzy set similarity measure auto-encoder clustering

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auto-encoder and LSTM-Based Credit Card Fraud Detection

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SN Computer Science 2023年第5期4卷 1-8页

作者： Sehrawat, Deepthi Singh, Yudhvir UIET MDU Rohtak India Computer Science and Engineering UIET MDU Rohtak India

The increased fraud risk due to the most recent methods of paying with a credit card, such as real-time payments and cards with near-field communication (NFC) capabilities, makes detecting credit card fraud an essential topic of study. Deep learning has shown encouraging results in recent years, whenever it is used to detect credit card fraud. There have been several deep learning-based models presented in this field of research. Still, not all of them have proven to be the most effective because each technique is best used with a particular dataset, and fraudsters constantly refine their methods to evade detection by the systems in place today. This work's primary focus is detecting credit card fraud using an auto-encoder with GRU and LSTM models. This is an efficient mechanism;first, data are passed to auto-encoder without the labels, and after that, the output generated by the auto-encoder is passed to the LSTM model as input with labels to detect fraud. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： auto-encoder Credit card Gated recurrent unit (GRU) Long short-term memory (LSTM) Performance metrics

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Attentive auto-encoder for Content-Aware Music Recommendation

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CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION 2022年第1期4卷 76-87页

作者： Li, Le Tao, Dan Zheng, Chenwang Gao, Ruipeng Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Software Beijing 100044 Peoples R China

Mobile Internet allows consumers the access to all musics on the mobile platform. Since it is not feasible to manually select music due to the size constraint of mobile devices, music recommendation has become a popular research topic in recent years, and researchers have proposed many effective methods such as collaborative filtering. However, with the tremendous increase of mobile users and music resources, customized music recommendations still face two challenges: (1) how to model complicated relations from user-music interaction data, and (2) how to integrate heterogeneous content information of musics. In this paper, we propose an Attentive auto-encoder for Content-Aware Music Recommendation (A(2)CAMR), which effectively integrates user behaviour records, music content, and similar musics of the target. In particular, we design a hierarchical attention-based encoder layer to learn fine-grained user-user and music-user relationships, thus produce behavior-based hidden representation of musics. We also employ an embedding layer to produce the content-based music representation, and cluster the similar music sets of the target music to predict users' preferences in the decoder. We conduct extensive experiments on real-world dataset, and the results demonstrate the effectiveness of our model.

关键词： Music recommendation auto-encoder Attention mechanism Content features

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Graph-Collaborated auto-encoder Hashing for Multiview Binary Clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 10121-10133页

作者： Wang, Huibing Yao, Mingze Jiang, Guangqi Mi, Zetian Fu, Xianping Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China Changzhou Univ Sch Comp Sci & ArtificialIntelligence Changzhou 213164 Jiangsu Peoples R China

Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on auto-encoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on auto-encoders for multiview binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called graph-collaborated auto-encoder (GCAE) hashing for multiview binary clustering. Specifically, we propose a multiview affinity graphs' learning model with low-rank constraint, which can mine the underlying geometric information from multiview data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we use an alternating iterative optimization scheme to obtain the multiview clustering results. Extensive experimental results on five public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.

关键词： Binary codes Clustering algorithms Feature extraction Semantics Data models Task analysis Quantization (signal) auto-encoder binary code graph-collaborated multiview clustering

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Liquid level detection in porcelain bushing type terminals using piezoelectric transducers based on auto-encoder networks

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MEASUREMENT 2019年 141卷 12-23页

作者： Hong, Xiaobin Zhang, Bin Liu, Yuan Zhou, Ziyi Ye, Ming Qi, Hongchang South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China Guangzhou Power Supply Bur Transmiss Management Inst Guangzhou 510310 Guangdong Peoples R China

Liquid level of internal silicone oil is about the safety operation of high voltage porcelain bushing type (PBT) terminals, and its regular inspection can effectively prevent the security risks caused by oil leaks. In this paper, a deep learning detection method based on local wavelet features and unsupervised feature fusion is proposed to quantitatively detect the internal liquid level of high voltage PBT terminals. Firstly, ultrasonic guided wave signals are divided into many segments by a sliding window and all segments are transformed into wavelet domain to catch the local time-frequency information. After preliminary selection according to the monotonic trends, the features are fed into a deep learning method named autoencoder networks for unsupervised feature fusion. Finally, a two-layer neural network is employed to regress the liquid level based on fused features. The experiments were conducted in different liquid level, and detection data are divided into model training set and testing set. The mean detection error of proposed method is only 0.034 m when the accuracy of training set is 0.1 m, and 0.0543 m when the accuracy of training set is 0.2 m. In liquid level detection experiments, proposed method also shows good robustness in limited training samples condition and low label accuracy condition, and better detection performance than PCA and STFT method. Experimental results demonstrate that proposed method can directly diagnosis the internal liquid level in PBT terminals and provide an effective maintenance policy. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Intelligent detection Guided wave Wavelet transform auto-encoder Deep learning

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A Novel Feature Extraction Method Based on Legendre Multi-Wavelet Transform and auto-encoder for Steel Surface Defect Classification

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IEEE ACCESS 2024年 12卷 5092-5102页

作者： Zheng, Xiaoyang Liu, Weishuo Huang, Yan Chongqing Univ Technol Sch Artificial Intelligence Chongqing 400054 Peoples R China

Effective steel surface defect classification with low computational cost is essential for online quality inspection. The challenge of this task is large intra-class differences and unclear inter-class distances shown in various surface defects. This paper proposes a novel feature extraction method based on Legendre multi-wavelet transform and auto-encoder network (LWT-AE) for effectively recognizing the steel surface defects categories. More precisely, the finite element approximation theory is implemented to address the strong capacity of LW bases with various regularities and vanish moments for thoroughly matching the complex geometry characteristics of the steel surface defects, which provides strict theory foundation for the feature extraction in LWT frequency domain without losing any defect information due to its orthogonality. Then, the statistical and texture parameters are utilized to sparsely extract the defect features from LWT frequency domain, resulting in removing the redundancy components corresponding to the defects. AE network is utilized to further reduce the dimension of the extracted features and automatically select the most valuable defect features. Furthermore, two classifiers (SVM and BPNN) are used to rectify the generalization ability of the proposed method. Finally, extensive experiments are conducted on two datasets to verify the effectiveness and robustness of the proposed method. The highest classification accuracies 99.44% and 95.37% compared with other methods are attained on the NEU-CLS dataset and X-SDD dataset, respectively. To summary, the proposed method not only has simple structure, but also can reliably identify different types of the steel surface defects, which provides a suitable online application technique for the actual steel surface defect classification.

关键词： Classification steel surface defects Legendre multi-wavelet transform auto-encoder

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Degradation prediction of proton exchange membrane fuel cell using auto-encoder based health indicator and long short-term memory network

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第82期47卷 35055-35067页

作者： He, Kai Liu, Zhongyong Sun, Yuning Mao, Lei Lu, Shouxiang Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei Peoples R China Univ Sci & Technol China State Key Lab Fire Sci Hefei Peoples R China

As durability of proton exchange membrane fuel cell (PEMFC) remains as the main obstacle for its larger scale commercialization, predicting PEMFC degradation progress is thus an effective way to extend its lifetime. To realize reliable prediction, a novel health indicator (HI) extraction method based on auto-encoder is proposed in this paper, with which PEMFC future voltage can be predicted by long short-term memory network (LSTM). The effec-tiveness and robustness of proposed approach is investigated with test data simulating vehicle operation conditions, and accurate prediction performance can be observed, with the maximum root mean square error (RMSE) of 0.003513. Moreover, by comparing with two commonly prognostic methods including attention-based gated recurrent unit network and polarization model-LSTM, the proposed method can provide better pre-dictions under various operating conditions. Furthermore, with the proposed method, the degradation mechanism of PEMFC can also be analyzed. Therefore, the proposed prog-nostic method can predict reliable PEMFC degradation progress and its corresponding degradation mechanisms, which will be beneficial in practical PEMFC systems for taking appropriate strategies to guarantee PEMFC durability.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cell Dynamic operating conditions Degradation analysis auto-encoder Long short-term memory network

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Image label transfer: Short video labelling by using frame auto-encoder

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The Journal of China Universities of Posts and Telecommunications 2020年第1期27卷 92-99页

作者： Lü Chaohui Huang Yiyang School of Information and Communication Engineering Communication University of ChinaBeijing 100024China Beijing Key Laboratory of Modern Entertainment Technology Communication University of ChinaBeijing 100024China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and TourismBeijing 100024China

Short videos on the Internet have a huge amount, but most of them are unlabeled. In this paper, a rough short video labelling method based on the image classification neural network is proposed. Convolutional auto-encoder is applied to train and learn unlabeled video frames, in order to obtain feature in the specific level. With these features, the video key-frames are extracted by the feature clustering method. These key-frames which represent the video content are put into an image classification network, so that the labels of every video clip can be got. In addition, the different architectures of convolutional auto-encoder are estimated, and a better performance architecture through the experiment result is selected. In the final experiment, the video frame features from the convolutional auto-encoder are compared with those from other extraction methods, where it illustrates remarkable results by the proposed method.

关键词： image feature video labelling convolutional neural network auto-encoder cluster key-frame

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Robust and structural sparsity auto-encoder with L21-norm minimization

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NEUROCOMPUTING 2021年 425卷 71-81页

作者： Li, Rui Wang, Xiaodan Quan, Wen Song, Yafei Lei, Lei Air Force Engn Univ Coll Air & Missile Def Xian 710051 Peoples R China Air Force Engn Univ Coll Air Traff Control & Nav Xian 710051 Peoples R China

The mean square error (MSE), as the most commonly used cost function for auto-encoder, is sensitive to outliers or impulsive noises in real-world application, which may misguide the training process. At the same time, stacked auto-encoder(SAE) is indeed a totally fully connected network, the parameters exponentially increase as the nodes and layers increase, which may cause over-fitting, huge computa-tional complexity and storage overhead. So the robustness and sparseness problem of the auto-encoder need to be further investigated. In this paper, we develop a robust and structural sparsity stacked auto-encoder with L21-norm loss function and regularization (LR21-SAE). Our L21-norm loss function can al-leviate the negative impact of outlier samples, thus show superior robust performance. Our L21-norm regularization can enforce some rows/columns of weight matrix shrink to zero entirely, thus promote to learn sparse features and choose compact network. We have validated our LR21-SAE model on several common datasets. Experimental results show that LR21-SAE is significantly robust to outlier noises for real-world data, it also can get sparse nodes connection deep neural network with notable less number of parameters than what the original non-sparse network has, while maintain outstanding performance. (c) 2020 Elsevier B.V. All rights reserved.

关键词： auto-encoder L21-norm regularization L21-norm loss Sparsity Robust

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A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

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PERSONAL AND UBIQUITOUS COMPUTING 2019年第3-4期23卷 521-529页

作者： Wei, Pengcheng Zhao, Yu Chongqing Univ Educ Sch Math & Informat Engn Chongqing Peoples R China

Since the contextual information has an important impact on the speaker's emotional state, how to use emotion-related context information to conduct feature learning is a key problem. The existing speech emotion recognition algorithms achieve the relatively high recognition rate;these algorithms are not very good application to the real-life speech emotion recognition systems. Therefore, in order to address the abovementioned issues, a novel speech emotion recognition algorithm based on improved stacked kernel sparse deep model is proposed in this paper, which is based on auto-encoder, denoising auto-encoder, and sparse auto-encoder to improve the Chinese speech emotion recognition. The first layer of the structure uses a denoising auto-encoder to learn a hidden feature with a larger dimension than the dimension of the input features, and the second layer employs a sparse auto-encoder to learn sparse features. Finally, a wavelet-kernel sparse SVM classifier is applied to classify the features. The proposed algorithm is evaluated on the testing dataset, which contains the speech emotion data of spontaneous, non-prototypical, and long-term. The experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms in speech emotion recognition.

关键词： Contextual information Emotion recognition auto-encoder Kernel sparse Sub-utterance-level Support vector machine Hidden feature Deep learning

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