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Electricity theft detection for energy optimization using deep learning models

ENERGY SCIENCE & ENGINEERING 2023年第10期11卷 3575-3596页

作者： Pamir, S. Javaid, Nadeem Javed, Muhammad Umar Abou Houran, Mohamad Almasoud, Abdullah M. M. Imran, Muhammad COMSATS Univ Islamabad Dept Comp Sci Islamabad 44000 Pakistan Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China Prince Sattam bin Abdulaziz Univ Coll Engn Dept Elect Engn Al Kharj Saudi Arabia Federat Univ Inst Innovat Sci & Sustainabil Brisbane Australia

The rapid increase in nontechnical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes up a major part of NTL. It causes losses for the DSOs and also deteriorates the quality of electricity. The introduction of advanced metering infrastructure along with the upgradation of the traditional grids to the smart grids (SGs) has helped the electric utilities to collect the electricity consumption (EC) readings of consumers, which further empowers the machine learning (ML) algorithms to be exploited for efficient electricity theft detection (ETD). However, there are still some shortcomings, such as class imbalance, curse of dimensionality, and bypassing the automated tuning of hyperparameters in the existing ML-based theft classification schemes that limit their performances. Therefore, it is essential to develop a novel approach to deal with these problems and efficiently detect electricity theft in SGs. Using the salp swarm algorithm (SSA), gate convolutional autoencoder (GCAE), and cost-sensitive learning and long short-term memory (CSLSTM), an effective ETD model named SSA-GCAE-CSLSTM is proposed in this work. Furthermore, a hybrid GCAE model is developed via the combination of gated recurrent unit and convolutional autoencoder. The proposed model comprises five submodules: (1) data preparation, (2) data balancing, (3) dimensionality reduction, (4) hyperparameters' optimization, and (5) electricity theft classification. The real-time EC data provided by the state grid corporation of China are used for performance evaluations via extensive simulations. The proposed model is compared with two basic models, CSLSTM and GCAE-CSLSTM, along with seven benchmarks, support vector machine, decision tree, extra trees, random forest, adaptive boosting, extreme gradient boosting, and convolutional neural network. The results exhibit that SSA-GCAE-CSLSTM yields 99.45% precision, 95.93% F1 score, 92.25% accuracy,

关键词： convolutional autoencoder deep learning electricity theft detection long short-term memory smart grids weighting

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Digital twins-based process monitoring for wastewater treatment processes

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年第1期238卷

作者： Liu, Wentao He, Sudao Mou, Jianpeng Xue, Ting Chen, Hongtian Xiong, Weili Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong 999077 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

Digital twins are a significant way to achieve fault detection of various smart manufacturing, which provide a new paradigm for complex industrial process monitoring. Wastewater treatment processes play a crucial role in water recycling, its failures may cause risks of adverse environmental impacts. This paper studies the digital twins fault detection framework based on the convolutional autoencoder for wastewater treatment processes monitoring. The designed digital twins fault detection framework can simulate the sludge bulking failure and the toxic impact failure conditions in the virtual space to construct the simulation data with continuous updating through wastewater data. The simulation data is divided into rate of change information sub-block, original sub-block, and cumulative information sub-block using the multi-block modeling strategy to fully explore the hidden information. Further, the sliding window method is utilized to resample the reconstructed sub-blocks to enhance the effects of the detection performance. Bayesian fusion is adopted, and the final decision is made based on the fused statistical value and the control limit. The comparison experiments tested on the digital twins fault detection framework demonstrate the superiority and feasibility of detection performance.

关键词： Digital twins Fault detection Wastewater treatment processes convolutional autoencoder

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A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion

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EVOLUTIONARY INTELLIGENCE 2021年第1期14卷 133-149页

作者： Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam Maulana Abul Kalam Azad Univ Technol Kolkata 700068 India Univ Calcutta Kolkata 700098 India Netaji Subhash Engn Coll Kolkata 700152 India

This paper proposes a deep unsupervised learning based denoising autoencoder model for the restoration of degraded mammogram with visual interpretation of breast lumps or lesion in mammography images (called SSDAE). The proposed model attempts to intensify the underexposed and abnormal structural regions through noise elimination in mammography image. A deep stacked convolutional autoencoder is designed by combining the autoencoder and the deconvolution network which conjointly reduces noisy artifacts and improves image details in mammogram. The proposed SSDAE model takes large noisy mammogram image patches as input and extracts relevant features from target batches. The suggested model can extract relevant features and reduce the dimensionality through sparsity property of the image data while preserving the key features that have been applied to restore image data in feature space. In order to reconstruct a deafening mammogram, the proposed model is carried out through a patched base training on samples to suppress noise thereby preserving structural details in mammography imaging. Experimental results authenticate that the suggested SSDAE model outplays a number of state-of-the-art methods for both X-ray mammogram and ultrasonographic mammogram. The execution speed for target noisy images increases with fine tuning of the network when compared to other algorithms.

关键词： Deep learning Mammogram restoration convolutional autoencoder Sparse autoencoder KL divergence

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MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using convolutional Neural Network

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ICT EXPRESS 2021年第4期7卷 521-529页

作者： Sutramiani, Ni Putu Suciati, Nanik Siahaan, Daniel Inst Teknol Sepuluh Nopember Fac Intelligent Elect & Informat Technol Dept Informat Surabaya Indonesia Univ Udayana Fac Engn Dept Informat Technol Badung Indonesia

The lontar manuscript is an ancient Balinese cultural heritage written using Balinese characters on palm leaves. The recognition of Balinese characters in lontar is challenging because it has noise and limited data availability. To solve these problems, data augmentation is needed to increase the variety and amount of data to improve recognition performance. In this study, we collected Balinese character images from 50 lontar manuscript writers. We proposed MAT-AGCA that combines Adaptive Gaussian Thresholding and convolutional autoencoder for data augmentation. Based on experiments using InceptionResnetV2, DenseNet169, ResNet152V2, VGG19, and MobileNetV2, our proposed method achieved the best performance with 96.29% accuracy. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.

关键词： Balinese character Lontar manuscript Data augmentation Adaptive Gaussian Thresholding convolutional autoencoder

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A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 166卷 107534-107534页

作者： Yao, Yu Powell, Maria White, Jules Feng, Jian Fu, Quchen Zhang, Peng Schmidt, Douglas C. Northeastern Univ Coll Informat Sci & Engn Wenhua Rd 3-11 Shenyang 110819 Liaoning Peoples R China Vanderbilt Univ Med Ctr 1211 Med Ctr Dr Nashville TN 37232 USA Vanderbilt Univ Dept Elect Engn & Comp Sci 2301 Vanderbilt Pl Nashville TN 37235 USA

Background: It remains hard to directly apply deep learning-based methods to assist diagnosing essential tremor of voice (ETV) and abductor and adductor spasmodic dysphonia (ABSD and ADSD). One of the main challenges is that, as a class of rare laryngeal movement disorders (LMDs), there are limited available databases to be investigated. Another worthy explored research question is which above sub-disorder benefits most from diagnosis based on sustained phonations. The question is from the fact that sustained phonations can help detect pathological voice from healthy ***: A transfer learning strategy is developed for LMD diagnosis with limited data, which consists of three fundamental parts. (1) An extra vocally healthy database from the International Dialects of English Archive (IDEA) is employed to pre-train a convolutional autoencoder. (2) The transferred proportion of the pre-trained encoder is explored. And its impact on LMD diagnosis is also evaluated, yielding a two-stage transfer model. (3) A third stage is designed following the initial two stages to embed information of pathological sustained phonation into the model. This stage verifies the different effects of applying sustained phonation on diagnosing the three sub-disorders, and helps boost the final diagnostic ***: The analysis in this study is based on clinician-labeled LMD data obtained from the Vanderbilt University Medical Center (VUMC). We find that diagnosing ETV shows sensitivity to sustained phonation within the current database. Meanwhile, the results show that the proposed multi-stage transfer learning strategy can produce (1) accuracy of 65.3% on classifying normal and other three sub-disorders all at once, (2) accuracy of 85.3% in differentiating normal, ABSD, and ETV, and (3) accuracy of 77.7% for normal, ADSD and ETV. These findings demonstrate the effectiveness of the proposed approach.

关键词： Laryngeal movement disorders convolutional autoencoder Multi -stage transfer Limited data Sustained phonation

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Predicting the spatiotemporal characteristics of atmospheric rivers: A novel data-driven approach

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GLOBAL AND PLANETARY CHANGE 2023年 231卷

作者： Meghani, Samarth Singh, Shivam Kumar, Nagendra Goyal, Manish Kumar VIT Bhopal Univ Sch Comp Sci & Engn Bhopal 466114 Madhya Pradesh India Indian Inst Technol Indore Dept Civil Engn Indore 453552 Madhya Pradesh India Indian Inst Technol Indore Dept Comp Sci & Engn Indore 453552 Madhya Pradesh India

Atmospheric Rivers (ARs) are narrow bands of high-water vapor content in the low troposphere of mid-latitude regions through which most of the poleward moisture is being transported. ARs have been represented statistically as the regions of intense vertically integrated horizontal water vapor transport (IVT) in the atmosphere. These ARs have been found positively correlated with extreme precipitation and flood events at some coastal mid-latitude regions and thus have been linked to several socioeconomic implications. The robust and accurate forecasts of AR availability at a significant lead time can be a useful tool for managing AR-associated floods and water resources. To enhance the knowledge of data-driven methods for modelling nonlinear atmospheric dynamics associated with ARs, we have explored some popular deep-learning architectures for predicting AR availability. AR availability maps derived from the statistical characterization of IVT using ERA5 reanalyses data of ECMWF from the testing dataset are taken as ground truth for the prediction. The predictions of the models have been analyzed based on popularly adopted performance evaluation metrics structural similarity index measure (SSIM), mean square error (MSE), root mean square error (RMSE), and peak signal-to-noise ratio (PSNR). Our proposed autoencoder model outperforms the conventional convolutional neural network (CNN) and Conv-LSTM model. We have got comparatively higher scores (average) of SSIM (0.739) and PSNR (64.424) as well as lower scores (average) of RMSE (0.155) and MSE (0.025) for the predictions which signify the ability of our model to learn spatiotemporal features linked with AR-dynamics.

关键词： Atmospheric rivers Deep learning convolutional autoencoder Floods Integrated water vapor transport Machine learning

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CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023年第1期231卷 107399-107399页

作者： Tang, Suigu Yu, Xiaoyuan Cheang, Chak Fong Ji, Xiaoyu Yu, Hon Ho Choi, I. Cheong Macau Univ Sci & Technol Fac Innovat Engn Sch Comp Sci & Engn Ave Wai Long Taipa Macau Peoples R China Kiang Wu Hosp Rua Coelho do Amaral Macau Peoples R China

Background and Objective: A deep learning-based intelligent diagnosis system can significantly reduce the burden of endoscopists in the daily analysis of esophageal lesions. Considering the need to add new tasks in the diagnosis system, a deep learning model that can train a series of tasks incrementally using endoscopic images is essential for identifying the types and regions of esophageal lesions. Method: In this paper, we proposed a continual learning-based esophageal lesion network (CLELNet), in which a convolutional autoencoder was designed to extract representation features of endoscopic images among different esophageal lesions. The proposed CLELNet consists of shared layers and task-specific lay-ers. Shared layers are used to extract common features among different lesions while task-specific layers can complete different tasks. The first two tasks trained by the CLELNet are the classification (task 1) and the segmentation (task 2). We collected a dataset of esophageal endoscopic images from Macau Kiang Wu Hospital for training and testing the CLELNet. Results: The experimental results showed that the classification accuracy of task 1 was 95.96%, and the Intersection Over Union and the Dice Similarity Coefficient of task 2 were 65.66% and 78.08%, respectively. Conclusions: The proposed CLELNet can realize task-incremental learning without forgetting the previous tasks and thus become a useful computer-aided diagnosis system in esophageal lesions analysis.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Continual learning convolutional autoencoder Classification Esophageal endoscopic images Segmentation

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CAE-CNN-Based DOA Estimation Method for Low-Elevation-Angle Target

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REMOTE SENSING 2023年第1期15卷 185-185页

作者： Zhao, Fangzheng Hu, Guoping Zhou, Hao Zhan, Chenghong AF Engn Univ Grad Sch Xian 710043 Peoples R China AF Engn Univ Air & Missile Def Coll Xian 710043 Peoples R China

For the DOA (direction of arrival) estimation of a low-elevation-angle target under the influence of a multipath effect, this paper proposes a DOA estimation method based on CAE (convolutional autoencoder) and CNN (convolutional neural network). The algorithm firstly inputs the signal covariance matrix of the array of the low-elevation target containing direct and reflected waves into the convolutional autoencoder to realize the de-multipath, and uses the spatial features extracted by the convolutional autoencoder as the input of the extreme learning machine to realize the DOA preclassification of direct waves;based on the preclassification results, one branch of the three parallel convolutional neural nets is selected, and the output of the convolutional autoencoder is used as the input of this branch to realize DOA estimation. The simulation results show that the algorithm has better estimation accuracy and efficiency than the conventional algorithms, especially when the DOA of the target is in the lower range. The analysis of the simulation results shows that the algorithm is effective, in which the convolutional autoencoder can effectively realize the de-multipath, and the use of parallel convolutional neural networks can avoid overfitting and underfitting and realize DOA estimation more accurately.

关键词： low-elevation-angle target DOA estimation convolutional autoencoder parallel convolutional neural networks preclassification

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State of health estimation of lithium-ion battery with automatic feature extraction and self-attention learning mechanism

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JOURNAL OF POWER SOURCES 2023年 556卷

作者： Jiang, Yiyue Chen, Yuan Yang, Fangfang Peng, Weiwen Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Peoples R China Guangdong Prov Key Lab Fire Sci & Intelligent Emer Guangzhou Peoples R China Sci & Technol Reliabil Phys & Applicat Technol Ele Guangzhou Peoples R China

Accurate state of health (SOH) estimation is significantly important to ensure the safe and reliable operation of lithium-ion battery. Most existing data-driven estimation methods are based on feature engineering and rely heavily on expert experience and manual operation. However, manually extracting qualified health features requires rich prior knowledge, and these highly-designed features for one specific application may not generalize well to other situations. In this work, an automatic feature extraction method combining convolutional autoencoder and self-attention mechanism is proposed for battery SOH estimation. With preprocessed data fed into the convolutional autoencoder, efficient features characterizing battery health are automatically extracted without human intervention. A self-mechanism module is then further employed to map these high-dimensional abstract health features into battery SOH. Finally, experimental study of battery aging is implemented to demonstrate the proposed method, and comparisons of the proposed method with existing data-driven ap-proaches and the manual feature-based methods have also been presented. With the help of the convolutional autoencoder and self-attention module, the proposed method replaces the conventional manual feature engi-neering with automatic feature extraction, and reaches 0.0048 average test root-mean-squared error (RMSE) and 0.46% mean-absolute-percentage error (MAPE) on our dataset and 3.69% on the NASA public dataset.

关键词： Lithium -ion battery State of health estimation Automatic feature extraction convolutional autoencoder Self-attention mechanism

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Machine learning enhanced evaluation of semiconductor quantum dots

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Corcione, Emilio Jakob, Fabian Wagner, Lukas Joos, Raphael Bisquerra, Andre Schmidt, Marcel Wieck, Andreas D. Ludwig, Arne Jetter, Michael Portalupi, Simone L. Michler, Peter Tarin, Cristina Univ Stuttgart Inst Syst Dynam Stuttgart Germany Univ Stuttgart Inst Halbleiteropt & Funkt Grenzflachen Stuttgart Germany Univ Stuttgart Res Ctr SCoPE Stuttgart Germany Univ Stuttgart Ctr Integrated Quantum Sci & Technol Stuttgart Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany Ruhr Univ Bochum Lehrstuhl Angew Festkorperphys Bochum Germany

A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the fir

关键词： Quantum technology Semiconductor quantum dot Single photon source Machine-learning-based evaluation convolutional autoencoder Neural network regression

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