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23rd International Conference on Electrical Machines and Systems (ICEMS)

作者： Bao, Liwen Yao, Gengsheng Chen, Songtao Wang, Zhimin Hu, Xiangping Huang, Yunkai Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China SaierNico Elect & Automat Ltd Zhenjiang 212000 Jiangsu Peoples R China

ISBN: (纸本)9784886864192

This paper proposes a single-phase inter-turn fault diagnosis method for high-speed permanent magnet synchronous motor and realizes real-time monitoring. The proposed method takes the resistance and inductance obtained from the online parameter identification and the second harmonic of control voltage as fault feature quantities. Then sparse auto-encoder is taken to implement fault detection. The proposed method avoids the installation of additional sensors and can be integrated with the motor control system. The results of the simulation implemented in MATLAB/Simulink verified the feasibility of the proposed method.

关键词： auto-encoder Fault Detection High-Speed Machine Inter-turn Fault

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Unsupervised representation learning with Laplacian pyramid auto-encoders

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APPLIED SOFT COMPUTING 2019年 85卷 105851-000页

作者： Zhao Qilu Li Zongmin Dong Junyu Ocean Univ China Dept Comp Sci & Technol 238 Songling Rd Qingdao Shandong Peoples R China China Univ Petr East China Dept Comp Applicat 66 Changjiang West Rd Qingdao Shandong Peoples R China

Scale-space representation has been popular in computer vision community due to its theoretical foundation. The motivation for generating a scale-space representation of a given data set originates from the basic observation that real-world objects are composed of different structures at different scales. Hence, it is reasonable to consider learning features with image pyramids generated by smoothing and down-sampling operations. In this paper we propose Laplacian pyramid auto-encoders, a straightforward modification of the auto-encoder framework, for unsupervised representation learning. The method uses multiple encoding-decoding sub-networks within a Laplacian pyramid framework to reconstruct the original image and the low pass filtered images. The last layer of each encoding sub-network also connects to an encoding layer of the sub-network in the next level, which aims to reverse the process of Laplacian pyramid generation. Experimental results showed that Laplacian pyramid leaded to a more stable and efficient training procedure and improved the performance of the learned representation with scale information. (C) 2019 Published by Elsevier B.V.

关键词： Unsupervised representation learning auto-encoder Scale-space representation Laplacian pyramid Convolutional neural networks

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Aspect Extraction from Bangla Reviews Through Stacked auto-encoders

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DATA 2019年第3期4卷 121页

作者： Bodini, Matteo Univ Milan Dipartimento Informat Giovanni Degli Antoni Via Celoria 18 I-20133 Milan Italy

Interactions between online users are growing more and more in recent years, due to the latest developments of the web. People share online comments, opinions, and reviews about many topics. Aspect extraction is the automatic process of understanding the topic (the aspect) of such comments, which has obtained huge interest from commercial and academic points of view. For instance, reviews available in webshops (like eBay, Amazon, Aliexpress, etc.) can help the customers in purchasing products and automatic analysis of reviews would be useful, as sometimes it is almost impossible to read all the available ones. In recent years, aspect extraction in the Bangla language has been regarded more and more as a task of growing importance. In the previous literature, a few methods have been introduced to classify Bangla texts according to the aspect they were focused on. This kind of research is limited mainly due to the lack of publicly available datasets for aspect extraction in the Bangla language. We take into account the only two publicly available datasets, recently published, collected for the task of aspect extraction in the Bangla language. Then, we introduce several classification methods based on stacked auto-encoders, as far as we know never exploited in the task of aspect extraction in Bangla, and we achieve better aspect classification performance with respect to the state-of-the-art: the experiments show an average improvement of 0.17, 0.31 and 0.30 (across the two datasets), respectively in precision, recall and F1-score, reported in the state-of-the-art works that tackled the problem.

关键词： text classification aspect-based sentiment analysis aspect extraction Bangla language auto-encoder

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Deep Learning Based Algorithms for Welding Edge Points Detection 21st

Deep Learning Based Algorithms for Welding Edge Points Detec...

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21st International Conference on Intelligent Data Engineering and automated Learning

作者： Muniategui, Ander del Barrio, Jon Ander Zurutuza, Xabier Angulo, Xabier Silanes, Inaki Irastorza, Uxue de la Yedra, Aitor Garcia Moreno, Ramon LORTEK BRTA Arranomendi Kalea 4A Ordizia 20240 Spain

ISBN: (纸本)9783030623647;9783030623654

Low defective rates and high variability in industrial processes, make difficult to develop accurate, reliable and fast automatic quality control systems. This paper presents two novel methods for the Quality Control of welded parts based on computer vision. The first method, BG, uses a modification of the bisection method, and a tuned version of the GoogleNet. The second method, BGN, is an ensemble of GoogleNet CNNs and a Convolutional auto-encoder to absorbe observed data variability. The CAE is used to select the best CNN from the ensemble to use for each input image. Both methods have been tested on more than 10(5) images and run in less than 0.2 s in a standard i7 CPU with mae values around 0.04-3.63 pixels, standard deviation of absolute errors around 1.15-7.48 pixels, and a percentage of correct predictions between 95-99.97%.

关键词： Deep learning Convolutional neural networks auto-encoder Edge detection Weld Quality control

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Two-Tier Enhanced Hybrid Deep Learning-Based Collaborative Filtering Recommendation System for Online Reviews

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Computational Intelligence 2025年第3期41卷

作者： Harsh Khatter Pooja Singh Anil Ahlawat Ajay Kumar Shrivastava Department of Computer Science and Information Technology KIET Group of Institutions Delhi-NCR Ghaziabad Uttar Pradesh India Department of Computer Science and Engineering School of Engineering Shiv Nadar Institution of Eminence Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad Uttar Pradesh India

Collaborative filtering-based recommender systems have recently attracted audiences due to the precise prediction of user interests and provide recommendations accordingly. The user-specific interests are the main requirement to build any recommendation model that produces the desired recommendation list. But the users' interests are sometimes unpredictable due to the fluctuating nature of the arrival of newer products. To resolve this problem and achieve better recommendation outcomes, a two-tier enhanced hybrid collaborative filtering based recommendation system (EHCFR) is constructed in this work based on deep learning. Initially, users in the dataset are segmented based on their age stratification to obtain users' interests based on age. Then, the major features are extracted from the dataset using the word learning enhanced variational auto-encoder (EVAE). These features are provided along with the rating matrix as the input to the deep belief network (DBN) for rating prediction. Based on the predicted ratings, the top N1 recommendation list is generated. Then, a time window strategy is adapted in the model to determine the dynamic fluctuations of user interests. Another list called the top N2 recommendation list is generated based on these fluctuations. Finally, both these lists are concatenated to provide accurate and favorable recommendations to the users. The proposed model is tested on the user dataset and provides competitive performance against the existing state-of-the-art techniques. Also, a reliable comparison is made with the existing popular datasets, such as Movielens 100k and Jester, and the results prove the efficacy of the proposed method.

关键词： age stratification auto-encoder collaborative filtering deep belief network deep learning feature extraction rating prediction recommendation system

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DEDD: Deep encoder with Dual Decoder Architecture for Stability and Specificity Preserving Encoding and Translation of Embedding between Domains

DEDD: Deep Encoder with Dual Decoder Architecture for Stabil...

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作者： Rajesh Ranjan Debasmita Das Ram Ganesh V Yatin Katyal Tanmoy Bhowmik Mastercard AI Garage

We propose a deep learning-based encoder with a dual decoder system to enrich the expressive power of embeddings pre-trained on two different corpora along with switching representation between domains. There are two scenarios:（a） Each of the corpora is pertaining to the different subject matter or topic of interests and（b） One corpus is a vast super-domain with generic and non-specific embeddings while the second one pertains to one specific sub-domain. In either case, the criterion for high-quality training would be to have enough common words between them. The mapping of contextual embeddings from both the corpus into the common latent space blends the semantic richness of both the corpus-specific learning while maintaining embedding stability. Furthermore, there is one dedicated decoder for either of the domains for generating the representation from common latent space. We evaluated our method for cross-learning between generalized GLOVE embedding and a very specialized skill-embedding developed by random-walk on a graphbased Skills Hierarchy. We demonstrate that our method preserves the stability of the generic embedding, the specificity of the skill domain as well as enriches the semantic representation of either domain through switching enabled by the encoder-to-duel-decoder path.

关键词： Natural Language Processing Artificial Intelligence Neural Networks auto-encoder Word Embeddings

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Normalized Non-Negative Sparse encoder for Fast Image Representation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第7期29卷 1962-1972页

作者： Zhang, Shizhou Wang, Jinjun Shi, Weiwei Gong, Yihong Xia, Yong Zhang, Yanning Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aerosp Ground Ocean Big Xian 710072 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

Image representation based on sparse coding generalizes the bag of words model. Although it reduces the reconstruction error for local features to achieve the state-of-the-art image classification performance, the large computational cost hinders the application of sparse coding-based image features. In this paper, we propose approximating a sparse code using the output of a simple neural network. The resulting parameter learning model for the neural network automatically incorporates non-negative and shift-invariant constraints, leading to an efficient normalized non-negative sparse coding ((NSC)-S-3) sparse encoder. Without the use of the traditional iterative process to solve the sparse coding objective, the sparse encoder directly "converts" each local feature into a sparse code. We also introduce a method for training the encoder based on the auto-encoder method. In addition, we formally propose the corresponding sparse coding scheme called (NSC)-S-3, which enforces both the non-negative constraint and the shift-invariant constraint in addition to the traditional sparse coding criteria. As demonstrated by several experiments, the obtained (NSC)-S-3 encoder requires only 3%-10% of the processing time for image feature extraction compared with the standard sparse coding scheme. At the same time, the features extracted using the exact solutions of the (NSC)-S-3 coding scheme and the (NSC)-S-3 encoder offer superior image classification accuracy compared to the accuracy of many existing sparse coding-based representations.

关键词： Sparse coding image representation auto-encoder non-negative constraint shift-invariant constraint

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Machine Learning for In-Region Location Verification in Wireless Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2019年第11期37卷 2490-2502页

作者： Brighente, Alessandro Formaggio, Francesco Di Nunzio, Giorgio Maria Tomasin, Stefano Univ Padua Dept Informat Engn I-35131 Padua Italy

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) with typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets. For a finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoder NNs and one-class SVMs which, however, are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical examples support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading.

关键词： auto-encoder in-region location verification machine learning neural network support vector machine

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Cross-Modality Retrieval by Joint Correlation Learning

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2019年第2期15卷 56-56页

作者： Wang, Shuo Guo, Dan Xu, Xin Zhuo, Li Wang, Meng Hefei Univ Technol Sch Artificial Intelligence Sch Comp Sci & Informat Hefei 230601 Anhui Peoples R China Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Hubei Peoples R China Beijing Univ Technol Signal & Informat Proc Lab Beijing Peoples R China

As an indispensable process of cross-media analyzing, comprehending heterogeneous data faces challenges in the fields of visual question answering (VQA), visual captioning, and cross-modality retrieval. Bridging the semantic gap between the two modalities is still difficult. In this article, to address the problem in cross-modality retrieval, we propose a cross-modal learning model with joint correlative calculation learning. First, an auto-encoder is used to embed the visual features by minimizing the error of feature reconstruction and a multi-layer perceptron (MLP) is utilized to model the textual features embedding. Then we design a joint loss function to optimize both the intra- and the inter-correlations among the image-sentence pairs, i.e., the reconstruction loss of visual features, the relevant similarity loss of paired samples, and the triplet relation loss between positive and negative examples. In the proposed method, we optimize the joint loss based on a batch score matrix and utilize all mutual mismatched paired samples to enhance its performance. Our experiments in the retrieval tasks demonstrate the effectiveness of the proposed method. It achieves comparable performance to the state-of-the-art on three benchmarks, i.e., Flickr8k, Flickr30k, and MS-COCO.

关键词： Cross-modality retrieval joint loss auto-encoder MLP

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Block-chain abnormal transaction detection method based on generative adversarial network and autoencoder

High-Confidence Computing

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High-Confidence Computing 2025年

作者： Ao Xiong Chenbin Qiao Wenjing Li Dong Wang Da Li Bo Gao Weixian Wang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China State Grid Digital Technology Holding Co. Ltd. Beijing 100053 China State Grid Jiangsu Electric Power CO.. LTD. Nanjing 210018 China State Grid Beijing Electric Power Company Beijing 100031 China

Anomaly detection in blockchain transactions faces several challenges, the most prominent being the imbalance between positive and negative samples. Most transaction data are normal, with only a small fraction of anomalous data. Additionally, blockchain transaction datasets tend to be small and often incomplete, which complicates the process of anomaly detection. When using simple AI models, selecting the appropriate model and tuning parameters becomes difficult, resulting in poor performance. To address these issues, this paper proposes GANAnomaly, an anomaly detection model based on Generative Adversarial Networks (GANs) and autoencoders. The model consists of three components: a data generation model, an encoding model, and a detection model. Firstly, the Wasserstein GAN (WGAN) is employed as the data generation model. The generated data is then used to train an encoding model that performs feature extraction and dimensionality reduction. Finally, the trained encoder serves as the feature extractor for the detection model. This approach leverages GANs to mitigate the challenges of low data volume and data imbalance, while the encoder extracts relevant features and reduces dimensionality. Experimental results demonstrate that the proposed anomaly detection model outperforms traditional methods by more accurately identifying anomalous blockchain transactions, reducing the false positive rate, and improving both accuracy and efficiency.

关键词： Blockchain Anomaly detection auto-encoder Generative adversarial network

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