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Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Pu, Zengxin Huang, Yu Weng, Min Meng, Yang Zhao, Yunbin He, Gengsheng Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang Peoples R China Guizhou Power Grid Co Ltd Guiyang Power Supply Bur Guiyang Peoples R China Guizhou Power Grid Co Ltd Duyun Power Supply Bur Duyun Peoples R China China Southern Power Grid Energy Dev Res Inst Guangzhou Peoples R China

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the denoising autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

关键词： deep learning non-intrusive load monitoring denoising autoencoder weather feature calendar feature

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Latent feature learning via autoencoder training for automatic classification configuration recommendation

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KNOWLEDGE-BASED SYSTEMS 2023年第1期261卷

作者： Deng, Liping Xiao, MingQing Southern Illinois Univ Carbondale Sch Math & Stat Sci Carbondale IL 62901 USA

The Combined Algorithm Selection and Hyperparameter Optimization problem, in short, CASH, seeks the most suitable classifiers and hyperparameters for the underlying classification problems. In current literature, the common approaches in dealing with CASH problem are conducted via search-based methods such as sequential model-based optimization (SMBO) along with various active tests. Different from current existing approaches, in this paper, we propose a new method by incorporating the so-called denoising autoencoder (DAE) approach into meta-learning (MtL) for automatic configuration (both algorithms and their hyperparameters) recommendation, which appears to be quite effective compared to standard search-based approaches. More specifically, we set up the configuration search space for CASH and produce the metadata, and generate the classification performance on a set of collected historical datasets. Then both encoder and decoder in the DAE system are trained with the masked metadata as inputs and the unmasked metadata as targets to extract the subtle latent variables of metadata and recover the unmasked inputs subsequently. Under our framework, the performance over the entire configuration space can be predicted effectively through two different settings, and the configuration with the highest predictive performance is thus recommended. The first recommendation approach is by inactivating some inputs and then to recover their entries via the trained encoder and decoder for new problems, while in the second approach, the relationship between the acquired latent variables and the meta-features of historical datasets via kernel multivariate multiple regression (MMR) is enacted, leading to the performance estimation of new datasets being pursued directly through MMR and the decoder of DAE without requiring any new configuration evaluations. An automatic classification configuration recommendation system, including 81 historical problems and 11 common classifiers with

关键词： Classifier and hyperparameter recommendation denoising autoencoder Meta-learning

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Gas path fault detection and isolation for aero-engine based on LSTM-DAE approach under multiple-model architecture

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MEASUREMENT 2022年 202卷

作者： Wang, Kun Guo, Yingqing Zhao, Wanli Zhou, Qifan Guo, Pengfei Northwestern Polytech Univ Sch Energy & Power Xian 710129 Peoples R China

Gas path fault diagnosis plays a critical role in the security guarantee and maintenance of aero-engines. In this paper, an approach based on a fusion neural network under multiple-model architecture for gas path fault detection and isolation is proposed. We develop a multi-channel long short-term memory network based on a sliding window to explore temporal and spatial relationships of data and capture the residuals of sensor mea-surements between predicted and observed values. Additionally, denoising autoencoders under a multiple-model architecture are introduced so as to perform fault detection and isolation based on the comparison of recon-structed prediction errors and isolation thresholds. Several simulation results verify that the diagnostic model has excellent robustness and diagnostic ability. The proposed method is compared with other common methods, and the advantages and functions of this method are presented.

关键词： Aero-engine Gas path fault diagnosis Long short-term memory network denoising autoencoder Multiple -model

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Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

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PEERJ COMPUTER SCIENCE 2018年第5期4卷 e154页

作者： Fernandes, Kelwin Chicco, Davide Cardoso, Jaime S. Fernandes, Jessica INESC TEC Inst Engn Sistemas & Comp Tecnol & Ciencia Porto Portugal Univ Porto Porto Portugal Princess Margaret Canc Ctr Toronto ON Canada Univ Cent Venezuela Caracas Venezuela

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

关键词： Dimensionality reduction Health-care informatics denoising autoencoder autoencoder Biomedical informatics Binary classification Deep learning Cervical cancer Artificial neural networks Health informatics

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Derivation of Kinetic Parameters and Lignocellulosic Composition From Thermogram of Biomass Pyrolysis Using Convolutional Neural Network

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2024年第1期2024卷

作者： Kim, Heeyoon Jo, Hyunbin Ryu, Changkook Sungkyunkwan Univ Sch Mech Engn Suwon 16419 South Korea Korea Inst Energy Res KIER Clean Fuel Res Lab Daejeon 34129 South Korea

A novel method employing a 1-dimensional convolutional neural network (1D-CNN) has been developed to deduce kinetic parameters for the three-parallel-reaction model (TPRM) and the lignocellulosic composition from the thermogram of biomass pyrolysis. This model was trained on differential thermogram (DTG) datasets created at various heating rates with rate constants randomly selected from expansive ranges. Furthermore, to enhance prediction accuracy, a denoising autoencoder (DAE) was crafted to eliminate noise from experimental data effectively. The 1D-CNN regression model forecasted kinetic parameters with mean errors of 1.52% for trained heating rates and 1.39%-3.19% for other heating rates. When tested on four biomass samples, the model precisely mimicked the DTG curves with R2 values ranging from 0.9956 to 0.9994. Relative to conventional numerical methods, this model delivers comparable prediction accuracy but through a significantly streamlined and expedited process. Enhancements are needed to broaden the model's applicability across various kinetic models and materials.

关键词： biomass convolutional neural network denoising autoencoder kinetics pyrolysis three-parallel-reaction model

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A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第10期34卷

作者： Fu, Qiming Liu, QingSong Gao, Zhen Wu, Hongjie Fu, Baochuan Chen, Jianping Suzhou Univ Sci & Technol Inst Elect & Informat Engn Suzhou 215009 Jiangsu Peoples R China Suzhou Univ Sci & Technol Jiangsu Key Lab Intelligent Bldg Energy Efficienc Suzhou 215009 Jiangsu Peoples R China McMaster Univ Fac Engn Hamilton ON L8S 0A3 Canada

With respect to the problem of the low accuracy of traditional building energy prediction methods, this paper proposes a novel prediction method for building energy consumption, which is based on the seamless integration of the deep neural network and transfer reinforcement learning (DNN-TRL). The method introduces a stack denoising autoencoder to extract the deep features of the building energy consumption, and shares the hidden layer structure to transfer the common information between different building energy consumption problems. The output of the DNN model is used as the input of the Sarsa algorithm to improve the prediction performance of the target building energy consumption. To verify the performance of the DNN-TRL algorithm, based on the data recorded by American Power Balti Gas and Electric Power Company, and compared with Sarsa, ADE-BPNN, and BP-Adaboost algorithms, the experimental results show that the DNN-TRL algorithm can effectively improve the prediction accuracy of the building energy consumption.

关键词： DNN-TRL feature transfer denoising autoencoder building energy prediction

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Enhanced recognition of bonding interface defects within CFRP-steel adhesive structures via thermal signals in low-power vibrothermography

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CONSTRUCTION AND BUILDING MATERIALS 2024年 456卷

作者： Zhang, Yubin Xu, Changhang Liu, Pengqian Xie, Jing Liu, Rui Zhao, Qing China Univ Petr East China Coll Mech & Elect Engn Qingdao 266580 Peoples R China China Univ Petr East China State Key Lab Chem Safety Qingdao 266580 Peoples R China

Carbon fiber reinforced polymers (CFRP) have been used as one of the options to strengthen steel structures through adhesive bonding, particularly in specific applications where traditional strengthening methods may not be suitable. Therefore, it becomes crucial to perform inspections on the resulting CFRP-steel adhesive structures (CSAS) to ensure their structural integrity and safety. However, the distinct physical properties of CFRP, epoxy resin, and steel pose significant challenges to accurately inspecting bonding interface defects of such special hybrid engineering structures. To address these challenges, a new approach, streamlined one-dimensional convolutional denoising autoencoder-low-power vibrothermography (SOCDAE-LVT), is proposed in this study to enhance the recognition of bonding interface defects within CSAS. This approach utilizes thermal signals from low-power vibrothermography (LVT) to enhance the recognizability of CSAS bonding interface defects. A lowpower vibrothermography inspection system was developed to acquire thermal signals on the surface of CSAS samples. A streamlined one-dimensional convolutional denoising autoencoder (SOCDAE) model was designed for robust representation extraction of the thermal signal at each pixel point. The study further investigated the impact of different types of added noise and signal pre-processing approaches on the performance of the SOCDAE-LVT, aiming to optimize its effectiveness. By comparing qualitatively and quantitatively with the stateof-the-art approaches, the results show that the proposed approach can better improve the recognizability of defects. The enhanced recognizability of bonding interface defects enables accurate assessment of the quality of CSAS, thereby contributing to the safety of such structures.

关键词： CFRP-steel adhesive structures Bonding interface defects Low-power vibrothermography Thermal signals Streamlined one-dimensional convolutional denoising autoencoder

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SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery

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PATTERN RECOGNITION 2020年 106卷 107472-107472页

作者： Madadi, Meysam Bertiche, Hugo Escalera, Sergio Comp Vis Ctr Edif OCampus UAB Catalonia 08193 Barcelona Spain Univ Barcelona Dept Math & Informat Gran Via Corts Catalanes Barcelona 58508007 Spain

In this paper we propose to embed SMPL within a deep-based model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 12 mm, respectively. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Deep learning 3D Human pose Body shape SMPL denoising autoencoder Volumetric stack hourglass

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Hybrid unsupervised web-attack detection and classification-A deep learning approach

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COMPUTER STANDARDS & INTERFACES 2023年 86卷

作者： Pillai, Seema Sharma, Anurag MATS Univ Arang 493441 Chhattisgarh India MATS Univ MATS Sch Engn Comp Sci Engn Arang India

Web requests made by users of web applications are manipulated by hackers to gain control of web servers. Moreover, detecting web attacks has been increasingly important in the distribution of information over the last few decades. Also, several existing techniques had been performed on detecting vulnerable web attacks using machine learning and deep learning techniques. However, there is a lack in achieving attack detection ratio owing to the utilization of supervised and semi-supervised learning approaches. Thus to overcome the afore-mentioned issues, this research proposes a hybrid unsupervised detection model a deep learning-based anomaly -based web attack detection. Whereas, the encoded outputs of De-Noising autoencoder (DAE), as well as Stacked autoencoder (SAE), are integrated and given to the Generative adversarial network (GAN) as input to improve the feature representation ability to detect the web attacks. Consequently, for classifying the type of attacks, a novel DBM-Bi LSTM-based classification model has been introduced. Which incorporates DBM for binary clas-sification and Bi-LSTM for multi-class classification to classify the various attacks. Finally, the performance of the classifier in terms of recall, precision, F1-Score, and accuracy are evaluated and compared. The proposed method achieved high accuracy of 98%.

关键词： SQL injection attack Cross -site scripting attacks denoising autoencoder Deep Boltzmann machine Binary long term short term memory

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Application of stack marginalised sparse denoising auto-encoder in fault diagnosis of rolling bearing

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1772-1777页

作者： Zhang, Junling Chen, Zhigang Du, Xiaolei Xu, Xu Yu, Miao Beijing Univ Civil Engn & Architecture Inst Elect & Mech & Vehicle Engn Beijing 100044 Peoples R China Construct Safety Monitoring Engn Technol Res Ctr Beijing 100037 Peoples R China

When a fracturing vehicle is working, it generally needs to bear high loads, media corrosion and erosion. For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto-encoder (SDAE). This method combines the sparse auto-encoder (SAE) and the denoising auto-encoder (DAE) and combines the characteristics of dimensionality reduction and robustness. The method adds marginalisation to optimise the SDAE. Finally, it uses a two-layer stacking method. The output results of the second marginalised SDAE are used as input to the softmax classifier for learning training and classification testing. This improved method (stack SDAE) improves the denoising ability, reduces the computational complexity, solves the problems of difficult parameter adjustment and slows training convergence. The experimental tests were carried out on the failure of pitting corrosion of the outer ring of the bearing, pitting failure of the inner ring, and cracking of the rolling element. The results show that the algorithm can effectively improve the accuracy of fault diagnosis of rolling bearings, and it has greatly improved than the algorithms of SAEs and DAE.

关键词： rolling bearings fault diagnosis mechanical engineering computing computational complexity corrosion failure analysis cracks fracture signal denoising media corrosion erosion rolling bearing fault diagnosis method two-layer stacking method SDAE stack marginalised sparse denoising autoencoder application fracturing vehicle sparse autoencoder SAE denoising autoencoder DAE softmax classifier learning training classification testing computational complexity pitting corrosion failure cracking

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