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Residential Customer Baseline Load Estimation Using stacked autoencoder With Pseudo-Load Selection

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2020年第1期38卷 61-70页

作者： Wang, Xinan Wang, Yishen Wang, Jianhui Shi, Di GEIRI North Amer San Jose CA 95134 USA Southern Methodist Univ Dept Elect & Comp Engn Dallas TX 75205 USA

Accurate estimation of customer baseline load (CBL) is a key factor in the successful implementation of demand response (DR). CBL technologies implemented at utilities currently are primarily designed for large industrial and commercial customers. The U.S. Federal Energy Regulatory Commission (FERC) order 745 states that DR owners, including residential customers, can sell their load reduction in the wholesale market. However, since residential load is random and un-schedulable, this tends to inherently degrade the effectiveness of existing CBL technologies. In this paper, a novel SAE based CBL method for residential customers that uses the data reconstruction capability of a stacked autoencoder (SAE) is described. In the model, two SAEs are synchronously trained-one SAE generates a pseudo-load pool and the second one is used to select a pseudo-load to reconstruct a residential CBL. A support vector machine (SVM) classifier is self-trained to conduct the pseudo-load selection. The proposed strategy is validated using a real data set consisting of 328 residential customers' smart meter readings. Benchmarks from other machine learning techniques and existing CBL methods are compared with the proposed method. Test results show that the accuracy of the residential CBL reconstruction significantly improves when compared with existing methods, such as HighXofY and exponential moving average.

关键词： Load modeling Estimation Training Support vector machines Image reconstruction Noise measurement Decoding Machine learning residential customer baseline load stacked autoencoder demand response unsupervised

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Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第8期83卷 23489-23508页

作者： Bai, Yang Sun, Xiyan Ji, Yuanfa Fu, Wentao Zhang, Jinli Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin Peoples R China Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & Locat Serv Guilin Peoples R China

Deep learning models have been widely used in hyperspectral images classification. However, the classification results are not satisfactory when the number of training samples is small. Focused on above-mentioned problem, a novel Two-stage Multi-dimensional Convolutional stacked autoencoder (TMC-SAE) model is proposed for hyperspectral images classification. The proposed model is composed of two sub-models SAE-1 and SAE-2. The SAE-1 is a 1D autoencoder with asymmetric structre based on full connection layers and 1D convolution layers to reduce spectral dimensionality. The SAE-2 is a hybrid autoencoder composed of 2D and 3D convolution operations to extract spectral-spatial features from the reduced dimensionality data by SAE-1. The SAE-1 is trained with raw data by unsupervised learning and the encoder of SAE-1 is employed to reduce spectral dimensionality of raw data. The data after dimension reduction is used to train the SAE-2 by unsupervised learning. The fine-tuning of SAE-2 encoder and the training of classifier are implemented simultaneously with small number of samples by supervised learning. Comparative experiments are performed on three widely used hyperspectral remote sensing data. The extensive comparative experiments demonstrate that the proposed architecture can effectively extract deep features and maintain high classification accuracy with small number of training samples.

关键词： Hyperspectral image classification Deep learning stacked autoencoder Multi-dimensional convolutional neural networks

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Learning view-invariant features using stacked autoencoder for skeleton-based gait recognition

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IET COMPUTER VISION 2021年第7期15卷 527-545页

作者： Hasan, Md Mahedi Mustafa, Hossen Asiful Bangladesh Univ Engn & Technol BUET Inst Informat & Commun Technol Dhaka Bangladesh

Human gait recognition in a multicamera environment is a challenging task in biometrics because of the presence of the large pose and variations in illumination among different views. In this work, to address the problem of variations in view, we present a novel stacked autoencoder for learning discriminant view-invariant gait representations. Our autoencoder can efficiently and progressively translate skeleton joint coordinates from any arbitrary view to a common canonical view without requiring the prior estimation of the view angle or covariate type and without losing temporal information. Then, we construct a discriminative gait feature vector by fusing the encoded features with two other spatiotemporal gait features to feed into the main recurrent neural network. Experimental evaluations of the challenging CASIA A and CASIA B gait datasets demonstrate that the proposed approach outperformed other state-of-the-art methods on single-view gait recognition. In particular, the proposed method achieved 46.31% and 33.86% average correct class recognition on probe set ProbeBG and ProbeCL, respectively, of the CASIA B dataset while considering the view variation;this is 0.3% and 30.68% higher than previous best-performing methods. Furthermore, in cross-view recognition, our method shows better results over other state-of-the-art methods when the view-angle variation is large than 36 degrees.

关键词： multicamera environment discriminant view-invariant gait representations skeleton-based gait recognition learning (artificial intelligence) image motion analysis gait analysis recurrent neural nets human gait recognition feature extraction CASIA A image representation CASIA B gait datasets stacked autoencoder learning view-invariant features encoded features biometrics (access control) recurrent neural network image recognition spatiotemporal phenomena

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Transfer learning based on improved stacked autoencoder for bearing fault diagnosis

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KNOWLEDGE-BASED SYSTEMS 2022年 256卷

作者： Luo, Shuyang Huang, Xufeng Wang, Yanzhi Luo, Rongmin Zhou, Qi Huazhong Univ Sci & Technol Sch Aerosp Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Deep transfer learning algorithm is regarded as a promising method to address the issue of rolling bearing fault diagnosis with limited labeled data. stacked autoencoder (SAE) has been widely employed in deep transfer learning research since it is a semi-supervised algorithm. However, there are still some limitations for the transfer learning based on SAE, including the vanishing gradient problem caused by the sigmoid activation function in SAE, and low accuracy under the condition of cross-domain or limited labeled training data. In this work, an improved SAE based on convolutional shortcuts and domain fusion strategy (ISAE-CSDF) is proposed for fault diagnosis of rolling bearing. The sparse term Kullback-Leibler (KL) divergence in the original SAE is replaced with the convolutional shortcuts to prevent vanishing gradient problem and improve the feature extraction ability. The domain fusion strategy can transfer commonly shared feature information from various domains. The feasibility of ISAE-CSDF is validated on two publicly available bearing datasets and a custom-built experiment device. Results show that ISAE-CSDF outperforms the state-of-art methods in the context of different working conditions, cross-domain, and limited labeled data. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Transfer learning Fault diagnosis stacked autoencoder Convolutional shortcuts Domain fusion

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Mechanistic block-based attention mechanism stacked autoencoder for describing typical unit connection industrial processes and their monitoring

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2024年第1期102卷 291-306页

作者： Wang, Chenhao Tang, Xujia Yu, Jianbo Yang, Xiaofeng Yan, Xuefeng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China Fudan Univ Sch Microelect Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ POB 293MeiLong Rd 130 Shanghai 200237 Peoples R China

The overall information of a process can be obtained through global modelling, and the local information is easily ignored in the research of the industrial process monitoring of unit connection. Thus, finding the global type of faults is easy, but occurs at the expense of drowning out the local faults. The use of block modelling can highlight local information, thereby improving local fault detection capability. However, the connection information between blocks is usually ignored in block modelling, which makes finding fault classes that only affect the connection relationship between blocks difficult. A mechanistic block-based attention mechanism stacked autoencoder (MB-AMSAE) monitoring method is proposed in this paper. The industrial process is divided into several parts in accordance with its mechanistic relationships, and each part represents an independent block. Self-attention is used to focus on the information of each block itself. Cross-attention is adopted to focus on the information between blocks, and this information is fused to form new blocks. The new block is used as the feature of the original block, and the original block is reconstructed by using a stacked autoencoder. The corresponding control limit is obtained in accordance with the reconstruction ability of normal samples, and whether the working conditions are normal is judged according to the control limit. The proposed algorithm is used in numerical simulation, Tennessee-Eastman processes, and is compared with other advanced algorithms based on its fault detection capability. Results show the effectiveness of the MB-AMSAE algorithm in process monitoring.

关键词： attention mechanism block process monitoring stacked autoencoder

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Research on CTR prediction based on stacked autoencoder

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APPLIED INTELLIGENCE 2019年第8期49卷 2970-2981页

作者： Wang, Qianqian Liu, Fang'ai Xing, Shuning Zhao, Xiaohui Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China Shandong Normal Univ Sch Math Sci Jinan Shandong Peoples R China

Click-through rate prediction is critical in internet advertising and affects web publisher's profits and advertiser's payment. In the CTR prediction, mining the interaction between features and extracting user interest are key factors affecting the prediction rate. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features and user interest in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition, and uses the bidirectional gated recurrent unit (Bi-GRU) to extract user interest. We utilize stacked autoencoder to portray the nonlinear associated relationship of data. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in internet advertising.

关键词： Click through rate stacked autoencoder Bidirectional gated recurrent unit Tensor decomposition

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A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第6期18卷 8024-8044页

作者： Seraphim, B. Ida Poovammal, E. Ramana, Kadiyala Kryvinska, Natalia Penchalaiah, N. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Annamacharya Inst Technol & Sci Dept Artificial Intelligence & Data Sci Rajampet India Comenius Univ Fac Management Informat Syst Dept Odbojarov 10 Bratislava 82005 25 Slovakia Annamacharya Inst Technol & Sci Dept CSE Rajampet India

Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) plays a crucial role in identifying the data and user deviations in an organization. In this paper, stream data mining is incorporated with an IDS to do a specific task. The task is to distinguish the important, covered up information successfully in less amount of time. The experiment focuses on improving the effectiveness of an IDS using the proposed stacked autoencoder Hoeffding Tree approach (SAE-HT) using Darwinian Particle Swarm Optimization (DPSO) for feature selection. The experiment is performed in NSL_KDD dataset the important features are obtained using DPSO and the classification is performed using proposed SAE-HT technique. The proposed technique achieves a higher accuracy of 97.7% when compared with all the other state-of-art techniques. It is observed that the proposed technique increases the accuracy and detection rate thus reducing the false alarm rate.

关键词： intrusion detection system (IDS) stream data mining stacked autoencoder DPSO Hoeffding tree feature selection

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Regularized label relaxation-based stacked autoencoder for zero-shot learning

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APPLIED INTELLIGENCE 2023年第19期53卷 22348-22362页

作者： Song, Jianqiang Zhao, Heng Wei, Xing Zhang, Xiutai Yao, Haiyan Anyang Inst Technol Coll Elect Informat & Elect Engn Anyang 455000 Peoples R China Xidian Univ Sch Life Sci & Technol Xian 710071 Peoples R China Yangtze Univ Elect & Informat Sch Jingzhou 434023 Peoples R China

Recently, Zero-Shot Learning (ZSL) has gained great attention due to its significant classification performance for novel unobserved classes. As seen and unseen classes are completely disjoint, the current ZSL methods inevitably suffer from the domain shift problem when transferring the knowledge between the observed and unseen classes. Additionally, most ZSL methods especially those targeting the semantic space may cause the hubness problem due to their use of nearest-neighbor classifiers in high-dimensional space. To tackle these issues, we propose a novel pathway termed Regularized Label Relaxation-based stacked autoencoder (RLRSA) to diminish the domain difference between seen and unseen classes by exploiting an effective label space, which has some notable advantages. First, the proposed method establishes the tight relations among the visual representation, semantic information and label space using via the stacked autoencoder, which is beneficial for avoiding the projection domain shift. Second, by incorporating a slack variable matrix into the label space, our RLRSA method has more freedom to fit the test samples whether they come from the observed or unseen classes, resulting in a very robust and discriminative projection. Third, we construct a manifold regularization based on a class compactness graph to further reduce the domain gap between the seen and unseen classes. Finally, the learned projection is utilized to predict the class label of the target sample, thus the hubness issue can be prevented. Extensive experiments conducted on benchmark datasets clearly show that our RLRSA method produces new state-of-the-art results under two standard ZSL settings. For example, the RLRSA obtains the highest average accuracy of 67.82% on five benchmark datasets under the pure ZSL setting. For the generalized ZSL task, the proposed RLRSA is still highly effective, e.g., it achieves the best H result of 58.9% on the AwA2 dataset.

关键词： Label relaxation stacked autoencoder Zero-shot learning

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Hybrid Image Compression-Encryption Scheme Based on Multilayer stacked autoencoder and Logistic Map

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China Communications 2022年第1期19卷 238-252页

作者： Neetu Gupta Ritu Vijay Computer Science&Engineering Department Banasthali VidyapithBanasthaliIndia Department of Electronics Engineering Banasthali VidyapithBanasthaliIndia.

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic *** proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

关键词： compression-encryption stacked autoencoder chaotic system back propagation algorithm logistic map

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δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting

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NEUROCOMPUTING 2017年 247卷 31-38页

作者： Zhou, Teng Han, Guoqiang Xu, Xuemiao Lin, Zhizhe Han, Chu Huang, Yuchang Qin, Jing South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Affiliated Shantou Hosp Shantou 515000 Guangdong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong 999077 Hong Kong Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Hong Kong Polytech Univ Sch Nursing Ctr Smart Hlth Hong Kong 999077 Hong Kong Peoples R China

Accurate and timely traffic flow forecasting is critical for the successful deployment of intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Recently, the stacked autoencoder has been proven promising for traffic flow forecasting but still exists some drawbacks in certain conditions. In this paper, a training samples replication strategy is introduced to train a series of stacked autoencoders and an adaptive boosting scheme is proposed to ensemble the trained stacked autoencoders to improve the accuracy of traffic flow forecasting. Furthermore, sufficient experiments have been conducted to demonstrate the superior performance of the proposal. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Short-term traffic flow forecasting AdaBoost stacked autoencoder Time-series model Dynamic system

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