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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Sy, Abib Jaafar, Fehmi Bouchard, Kevin Univ Quebec Chicoutimi Dept Math & Comp Sci Chicoutimi PQ Canada

ISBN: (纸本)9798350373981;9798350373974

There are various traditional methods used for securing sensitive data, such as cryptography algorithms like AES-HMAC-SHA256, Twofish, and Chacha20. However, several studies showed that these cryptography algorithms suffer from security vulnerabilities. In this paper, we explore the use of a cryptography model based on a deep convolutional autoencoder and we compare its performances to the cryptography algorithms. We report the results of a comparative study based on several metrics. We incorporate more nuanced metrics such as cosine similarity, entropy, Kendall and Spearman rate, and Mean Squared Error (MSE) for a comprehensive assessment of model performance and security, in addition to encryption and decryption time metrics. The results obtained are very promising. Our model performs the best on two essential metrics, entropy and MSE. We obtain a decrypted file entropy of 8.01, compared to 7.99 for the three other standard models, with a very low MSE of 0.003, compared to 105.43 for AES, which remains the most efficient compared to the other algorithms.

关键词： deep convolutional autoencoder AES-HMAC-SHA-256 Twofish Chacha20

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Pilot study to generate image features by deep autoencoder for computer-aided detection systems

Pilot study to generate image features by deep autoencoder f...

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International Forum on Medical Imaging in Asia

作者： Nemoto, Mitsutaka Ushifusa, Kazuyuki Tanaka, Atsuko Yamada, Takahiro Kimura, Yuichi Hayashi, Naoto Kindai Univ Fac Biol Oriented Sci & Technol Nishimitani 930 Wakayama 6496493 Japan Univ Tokyo Hosp Dept Computat Diagnost Radiol & Prevent Med CDRPM Bunkyo Ku 7-3-1 Hongo Tokyo 1138655 Japan

ISBN: (数字)9781510627765

ISBN: (纸本)9781510627765

We propose an automatic feature generation by deep convolutional autoencoder (deep CAE) without lesion data. The main idea of the proposed method is based on anomaly detection. deep CAE is trained by only normal volume patches. Trained deep CAE calculates low-dimensional features and reproduction error from 2.5 dimensional (2.5D) volume patch. The proposed method was evaluated experimentally with 150 chest CT cases. By using both previous features and the deep CAE based features, an improved classification performance was obtained;AUC=0.989 and ANODE=0.339.

关键词： deep convolutional autoencoder anomaly detection image feature 2.5 dimensions computer-aided detection

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Smart Meter Data Compression and Reconstruction Using deep convolutional autoencoders 52

Smart Meter Data Compression and Reconstruction Using Deep C...

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52nd North American Power Symposium (NAPS)

作者： Yuan, Yuxuan Zhang, Qianzhi Dehghanpour, Kaveh Bu, Fankun Wang, Zhaoyu Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)9781728181929

Advanced Metering Infrastructure (AMI) enables utilities to gather vast amount of smart meter (SM) data, which can facilitate demand-side control, fault detection, and system monitoring. However, due to the bottlenecks caused by the burdensome data transmission and storage, taking full advantage of SM measurements is challenging. In response to this problem, this paper presents a deep convolutional autoencoder (DCA)-based data compression method to significantly reduce the volume of SM data while providing an efficient way to restore the original data for various power system applications. The temporal-spatial correlations within SM data are exploited in our method to enhance DCA performance. Further, a sensitivity analysis is conducted to obtain a tradeoff between the data compression ratio and reconstruction error. The proposed method has been tested and verified using real utility data.

关键词： deep convolutional autoencoder data compression smart meter temporal-spatial correlation

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PredMaX: Predictive maintenance with explainable deep convolutional autoencoders

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ADVANCED ENGINEERING INFORMATICS 2022年 54卷

作者： Hajgato, Gergely Weber, Richard Szilagyi, Botond Tothpal, Balazs Gyires-Toth, Balint Hos, Csaba Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Telecommun & Media Informat Muegyet Rkp 3 H-1111 Budapest Hungary Budapest Univ Technol & Econ Fac Mech Engn Dept Hydrodynam Syst Muegyet Rkp 3 H-1111 Budapest Hungary Budapest Univ Technol & Econ Fac Chem Technol & Biotechnol Muegyet Rkp 3 H-1111 Budapest Hungary PETROLSZOLG Maintenance & Serv Provider Ltd Budapest Hungary

A novel data exploration framework (PredMaX) for predictive maintenance is introduced in the present paper. PredMaX offers automatic time period clustering and efficient identification of sensitive machine parts by exploiting hidden knowledge in high-dimensional, unlabeled temporal data. Condition monitoring systems often provide such data, which is further analyzed by human experts or used for training predictive *** reduces data dimensionality in two steps: An explainable deep convolutional autoencoder is applied on the data first, followed by principal component analysis. The automatic clustering is performed in the latent space of the autoencoder, ensuring higher accuracy than the clustering in the space of principal components. If clusters of normal and abnormal operation are known, the reasoning module is able to reveal the measurement channels that contributed the most to the latent representation moving from normal to abnormal *** the detailed presentation of the PredMaX approach, the paper presents the case study of identifying the most important signals that can be used for predicting oil degradation in an industrial gearbox. The case study is performed on a data-driven basis with minimal human assistance and without preliminary knowledge of the machine.

关键词： Explainable neural networks Explainable artificial intelligence Predictive maintenance Unsupervised learning Automatic cluster identification deep convolutional autoencoder

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A deep convolutional neural network architecture for interstitial lung disease pattern classification

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2020年第4期58卷 725-737页

作者： Huang, Sheng Lee, Feifei Miao, Ran Si, Qin Lu, Chaowen Chen, Qiu Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai Peoples R China Kogakuin Univ Grad Sch Engn Tokyo Japan

Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity among the clinical manifestations of these diseases. In order to assist the radiologists, computer-aided diagnosis systems have been developed. Besides, the potential of deep convolutional neural networks (CNNs) is also expected to exert on the medical image analysis in recent years. In this paper, we design a new deep convolutional neural network (CNN) architecture to achieve the classification task of ILD patterns. Furthermore, we also propose a novel two-stage transfer learning (TSTL) method to deal with the problem of the lack of training data, which leverages the knowledge learned from sufficient textural source data and auxiliary unlabeled lung CT data to the target domain. We adopt the unsupervised manner to learn the unlabeled data, by which the objective function composed of the prediction confidence and mutual information are optimized. The experimental results show that our proposed CNN architecture achieves desirable performance and outperforms most of the state-of-the-art ones. The comparative analysis demonstrates the promising feasibility and advantages of the proposed two-stage transfer learning strategy as well as the potential of the knowledge learning from lung CT data. The framework of the proposed two-stage transfer learning method.

关键词： Interstitial lung diseases (ILDs) convolutional neural networks (CNNs) deep convolutional autoencoder Transfer learning

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Robust deep fuzzy K-means clustering for image data

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PATTERN RECOGNITION 2024年 153卷

作者： Wu, Xiaoling Yu, Yu-Feng Chen, Long Ding, Weiping Wang, Yingxu Guangzhou Univ Dept Stat Guangzhou Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent Co Jinan Peoples R China

Image clustering is a difficult task with important application value in computer vision. The key to this task is the quality of images features. Most of current clustering methods encounter the challenge. That is, the process of feature learning and clustering operates independently. To address this problem, several researchers have been dedicated to performing feature learning and deep clustering together. However, the obtained features lack discriminability to address high -dimensional data successfully. To deal with this issue, we propose a novel model named as robust deep fuzzy K -means clustering (RD-FKC), which efficiently projects image samples into a representative embedding space and precisely learns membership degrees into a combined framework. Specifically, RD-FKC introduces Laplacian regularization technique to preserve locality properties of data. Moreover, by using an adaptive loss function, the model becomes more robust to diverse types of outliers. Furthermore, to avoid the latent space being distorted and make the extracted features retain the original information as much as possible, the model introduces reconstruction error and adds regularization to network parameters. Finally, an effective algorithm is derived to solve the optimization model. Numerous experiments have been conducted, illustrating the advantages and superiority of RD-FKC over existing clustering approaches.

关键词： Locality preserving deep convolutional autoencoder Laplacian regularization Unsupervised image clustering

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A Method of Complementing Missing Power Data in Low-Voltage Stations Based on Improved deep convolutional Self-Encoding Network

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IEEE ACCESS 2022年 10卷 57565-57573页

作者： Zhao, Hongshan Cui, Yangyang Song, Wei Qu, Yuehan Sun, Mengxue North China Elect Power Univ Sch Elect & Elect Engn Baoding 071003 Peoples R China

The irregularities in the collection and transmission of user power data in the low-voltage power distribution station area have led to errors in the subsequent application analysis of the station area. In order to ensure the integrity of power data in low-voltage stations, a multi-user power missing data complement method based on improved deep convolutional self-encoding is proposed. First, according to the characteristics of the lack of multi-user power data in the low-voltage station area, the power data is formed into a spatio-temporal tensor data format that can be used for one-dimensional convolution operations. Then use the encoding and decoding capabilities of the improved deep convolutional self-encoding network to realize the reconstruction of missing data, and optimize the network structure by introducing residual learning and batch normalization (BN). Finally, based on the proposed method, two cases of random and continuous loss of user power data in a certain area are complemented. The results show that the method can accurately complete 40% of randomly missing data and 2 consecutive days of missing data. The proposed method has improved completion accuracy compared with traditional methods to varying degrees.

关键词： Intelligent distribution network low-voltage power distribution station area deep convolutional autoencoder residual learning missing data completion

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Dynamic programming approach to visual place recognition in changing environments

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ELECTRONICS LETTERS 2017年第6期53卷 391-393页

作者： Oh, J. H. Lee, B. H. Seoul Natl Univ ASRI Dept Elect & Comp Engn 1 Gwanak Ro Seoul South Korea

Recognising places under extreme perceptual changes is a challenging problem. A new dynamic programming method to align sequences of image features extracted from a deep convolutional auto-encoder to efficiently solve this problem is proposed. As this method considers not only environmental variations, but also the motion constraint of the mobile robot, places from changing environment can be successfully recognised by finding the most likely path sequence. Experimental results show improved precision-recall performance compared with other algorithms.

关键词： dynamic programming feature extraction SLAM (robots) mobile robots robot vision dynamic programming approach visual place recognition image feature extraction deep convolutional autoencoder mobile robot precision-recall performance

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Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A deep Learning Approach

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IEEE WIRELESS COMMUNICATIONS LETTERS 2021年第7期10卷 1503-1507页

作者： Dehmollaian, Eshagh Etzlinger, Bernhard Torres, Nuria Ballber Springer, Andreas Johannes Kepler Univ Linz Inst Commun Engn & RF Syst A-4040 Linz Austria

This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM systems using channel state information. We treat the physical tamper attack problem as a semi-supervised anomaly detection problem and utilize a deep convolutional autoencoder (DCAE) to tackle it. The past observations of the estimated channel state information (CSI) are used to train the DCAE. Then, a post-processing is deployed on the trained DCAE output to perform the physical tamper detection. Our experimental results show that the proposed approach, deployed in an office and a hall environment, is able to detect on average 99.6% of tamper events (TPR = 99.6%) while creating zero false alarms (FPR = 0%).

关键词： Receivers Radio transmitters Training Convolution Wireless networks Decoding Channel state information OFDM channel state information deep learning deep convolutional autoencoder physical tamper attack

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deep Reconstruction Error Based Unsupervised Outlier Detection in Time-Series 12th

Deep Reconstruction Error Based Unsupervised Outlier Detecti...

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12th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Amarbayasgalan, Tsatsral Lee, Heon Gyu Pham Van Huy Ryu, Keun Ho Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea GAION Big Data Res Ctr Seoul South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chung Buk Natl Univ Coll Elect & Comp Engn Dept Comp Sci Cheongju 28644 South Korea

ISBN: (纸本)9783030420581;9783030420574

With all the advanced technology nowadays, the availability of time-series data is being increased. Outlier detection is an identification of abnormal patterns that provide useful information for many kinds of applications such as fraud detection, fault diagnosis, and disease detection. However, it will require an expensive domain and professional knowledge if there is no label which indicates normal and abnormality. Therefore, an unsupervised novelty detection approach will be used. In this paper, we propose a deep learning-based approach. First, it prepares subsequences according to the optimal lag length using Autoregressive (AR) model. The selected lag length for time-series analysis defines the data context in which further analysis is performed. Then, reconstruction errors (RE) of the subsequences on deep convolutional autoencoder (CAE) models are used to estimate the outlier threshold, and density-based clustering is used to identify outliers. We have compared the proposed method with several publicly available state-of-the-art anomaly detection methods on 30 time-series benchmark datasets. These results show that our proposed deep reconstruction error based approach outperforms the compared methods in most of the cases.

关键词： Outlier Anomaly Autoregressive model deep convolutional autoencoder Time-series data

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