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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Ya Zou Mengfu Tu Xianliang Teng Rongzhang Cao Wei Xie Power Grid Dispatching&Control Technology Branch Company NARI TECHNOLOGY DEVELOPMENT CO.LTD Nanjing China NARI RESEARCH INSTITUTE NARI TECHNOLOGY DEVELOPMENT CO.LTD Nanjing China

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

Artificial neural network method is a common method for short-term electricity price forecasting. However, when the amount of input and output data is large, the training speed will be slow, and it is easy to fall into local extreme values or even the result is difficult to converge. In view of this, the paper proposes a deep learning model based on stacked autoencoder (SAE) to predict electricity price. This paper analyzes the factors affecting electricity price, proposes an algorithm based on stacked autoencoder model, and uses MATLAB tools to predict electricity price in PJM power market. The comparison between SAE algorithm and BP algorithm is carried out in the example. The results show that the prediction results based on SAE model are more accurate. The deep learning model has better ability to express the objective function than the shallow model, and can effectively solve the problem of traditional neural network training difficulties.

关键词： Electricity price factor Electricity price forecasting Deep learning stacked autoencoder Electricity pricing Electricity Prices algorithms Objective function

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A stacked autoencoder for operation mode classification of complicated industrial process

A stacked autoencoder for operation mode classification of c...

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Chinese Automation Congress (CAC)

作者： Zhou, Longfei Huang, Keke Yang, Chunhua Chen, Xiaofang Xie, Yongfang Yue, Weichao Cent South Univ Sch Software Changsha Hunan Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781728113128

In this paper, we propose a novel stacked autoencoder (SAE) based operation mode classification method for the complicated industrial process. In detail, we first add the sparse and regularization constraints into SAE to learn low-dimensional nonlinear representations of high-dimensional data, then the SAE is trained in two steps: unsupervised layer-by-layer pre-training is performed first, followed by supervised fine-tuning. In order to evaluate the efficiency of the proposed method, we conduct extensive experiments on the Tennessee Eastman (TE) process and aluminum electrolysis production process in comparison with several conventional methods, the experimental results validate that the proposed method is more effective than other methods in the mode classification task.

关键词： complicated industrial process stacked autoencoder sparse and regularization constraints

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Virtual Battery Parameter Identification using Transfer Learning based stacked autoencoder 17

Virtual Battery Parameter Identification using Transfer Lear...

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17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Chakraborty, Indrasis Nandanoori, Sai Pushpak Kundu, Soumya Pacific Northwest Natl Lab Optimizat & Control Grp Richland WA 99354 USA

ISBN: (纸本)9781538668054

Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble. In real-world applications, however, it is likely that the only available information are end-use measurements such as power consumption, room temperature, device on/off status, etc., while very little about the individual load models and parameters are known. We propose a transfer learning based deep network framework for calculating virtual battery state of a given ensemble of flexible thermostatic loads, from the available end-use measurements. This proposed framework extracts first order virtual battery model parameters for the given ensemble. We illustrate the effectiveness of this novel framework on different ensembles of ACs and WHs.

关键词： stacked autoencoder Convolution Neural Network LSTM Virtual Battery

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Deep Residual Learning-based Reconstruction of stacked autoencoder Representation 25

Deep Residual Learning-based Reconstruction of Stacked Autoe...

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25th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Li, Honggui Trocan, Maria Yangzhou Univ Phys Coll Sci & Technol Yangzhou Jiangsu Peoples R China Inst Super Elect Paris LISITE Res Lab Paris France

ISBN: (纸本)9781538695623

stacked autoencoder (SAE) can efficiently represent high dimensional data with low dimensional features via minimizing a reconstruction error. However, the decoder of SAE cannot achieve lossless recovery of original data. If the reestablishment performance of SAE decoder is improved, it can be employed for low-bitrate and high-quality data compression. This paper adopts deep residual learning of convolutional neural network (CNN) to promote the reconstruction capability of SAE decoder. It is demonstrated by experimental results that deep CNN is superior to SAE decoder in rebuilding the performance of image data.

关键词： stacked autoencoder deep residual learning convolutional neural network data recosntruction data compression

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Feature learning using stacked autoencoder for Multimodal Fusion, Shared and Cross Learning on Medical Images

Feature learning using Stacked Autoencoder for Multimodal Fu...

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IEEE Bombay Section Signature Conference (IBSSC)

作者： Zeeshan Ul Islam Vikas Singh Nishchal K. Verma Mechanical Engineering Visvesvarya National Institute of Technology Nagpur India Indian Institute of Technology Kanpur India

ISBN: (纸本)9781538674765

The analysis of medical images and to find meaningful patterns in it is a cumbersome task, even with the use of techniques of Computer Vision when the dataset is very large. In such a situation deep learning is a handy tool, because of its ability to learn and extract meaningful patterns and features from the images. The use of multiple modalities of training data to train system has been in practice for conventional machine learning algorithms. Here, in this paper, we are going to present a Deep Learning based architecture for extraction of features from large training set of medical images. The deep learning model is tested against conventional techniques by performing Multimodal fusion, Shared Learning and Cross Learning on it. It was found out that Deep Learning model performs superior than the conventional techniques in multimodal fusion and shared learning settings.

关键词： Deep Learning stacked autoencoder Multimodal Fusion Shared Learning Cross Modal Learning images Traditional technology Learning Fusion reactions Fusion, biological FUSION fusion (melting) Medical imaging Medical computer vision

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Highway tollgates traffic prediction using a stacked autoencoder neural network

Highway tollgates traffic prediction using a stacked autoenc...

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作者： OSKAR KARRMAN LINNEA OTTERLIND Chalmers University of Technology

学位级别：硕士

Traffic flow prediction is an important area of research with a great number of applications such as route planning and congestion avoidance. This thesis explored artificial neural network performance as travel time and traffic volume predictors. stacked autoencoder artificial neural networks were studied in particular due to recent promising performance in traffic flow prediction, and the result was compared to multilayer perceptron networks, a type of shallow artificial neural networks. The Taguchi design of experiments method was used to decide network parameters. stacked autoencoder networks generally did not perform better than shallow networks, but the results indicated that a bigger dataset could favor stacked autoencoder networks. Using the Taguchi method did help cut down on number of experiments to test, but choosing network settings based on the Taguchi test results did not yield lower error than what was found during the Taguchi tests.

关键词： stacked autoencoder multilayer perceptron neural network traffic prediction traffic flow taguchi

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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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Detection of Double Compressed AMR Audio Using stacked autoencoder

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2017年第2期12卷 432-444页

作者： Luo, Da Yang, Rui Li, Bin Huang, Jiwu Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China

The adaptive multi-rate (AMR) audio codec adopted by many portable recording devices is widely used in speech compression. The use of AMR speech recordings as evidence in court is growing. Nowadays, it is easy to tamper with digital speech recordings, which makes audio forensics increasingly important. The detection of double compressed audio is one of the key issues in audio forensics. In this paper, we propose a framework for detecting double compressed AMR audio based on the stacked autoencoder (SAE) network and the universal background model-Gaussian mixture model (UBM-GMM). Instead of hand-crafted features, we used the SAE to learn the optimal features automatically from the audio waveforms. Audio frames are used as network input and the last hidden layer's output constitutes the features of a single frame. For an audio clip with many frames, the features of all the frames are aggregated and classified by UBM-GMM. Experimental results show that our method is effective in distinguishing single/double compressed AMR audio and outperforms the existing methods by achieving a detection accuracy of 98% on the TIMIT database. Exhaustive experiments demonstrate the effectiveness and robustness of the proposed method.

关键词： Adaptive multi-mate double compressed AMR audio forensics stacked autoencoder

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Radar HRRP Target Recognition Based on stacked autoencoder and Extreme Learning Machine

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SENSORS 2018年第1期18卷 173-173页

作者： Zhao, Feixiang Liu, Yongxiang Huo, Kai Zhang, Shuanghui Zhang, Zhongshuai Natl Univ Def Technol Coll Elect Sci Changsha 410073 Hunan Peoples R China

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

关键词： radar target recognition high-resolution range profile deep learning extreme learning machine stacked autoencoder

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Plant classification based on stacked autoencoder 2

Plant classification based on Stacked Autoencoder

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2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Meng-Meng Yang Arifur Nayeem Ling-Ling Shen School of Computer Science and Technology Nanjing Normal University Saidpur Government Technical School And College School of Overseas Education Nanjing University Of Post And Telecommunications School of Business of Nanjing Normal University

ISBN: (纸本)9781509064151;9781509064144

With the development of rapid technology, the similarity between plants is increasing, which will enhance the classified workload of botanists. Therefore, it is urge to find a quick automatic classification method. In recent years, the performance of autoencoder has become more and more prominent. Consequently, in this paper, we employ stacked autoencoder to classify three plants, including 630 images in total. The result of this experience shows that the accuracy of classification is 93.3%.

关键词： stacked autoencoder plant classification deep learning

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