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Area Day-Ahead Photovoltaic Energy Generation Forecasting by auto-encoder Inputting Images of Multiple Meteorological Elements

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2024年第5期19卷 653-661页

作者： Mori, Yusuke Wakao, Shinji Ohtake, Hideaki Takamatsu, Takahiro Oozeki, Takashi Waseda Univ Dept Elect Engn & Biosci 3-4-1 Okubo Tokyo Tokyo 1698555 Japan Natl Inst Adv Sci & Technol 2-2-9 Machi Ikedai Koriyama Fukushima 9630298 Japan Meteorol Res Inst 1-1 Nagamine Tsukuba Ibaraki 3050052 Japan

Photovoltaics (PV), which is one of variable renewable energies, have been installed largely. In the environment of largely installed PV, the output fluctuates of PV influences on the operation of power gird network adversely. Therefore, PV energy generation forecasting is valid for power grid operation and management and needs the reduction of large forecast error as well as average error. Due to a day-ahead forecast of the total PV energy generation in the power grid area, we developed the forecasting method by using meteorological forecast data of many points in the target area. In order to forecast the regional PV energy generation, the proposed method uses auto-encoder and convolutional neural network (CNN) for extracting requirements and valid data, and forecast with neural network (NN) by means of extracted information. In this paper, we improved the forecasting error by using multiple meteorological elements as input data. As a result, we indicated that using both of global horizontal irradiance and low cloud cover is required for the reginal PV energy generation forecasting, and large forecasting error can be reduced by adding either dew point depression, or cumulative precipitation as input data. (c) 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

关键词： photovoltaics (PV) area day-ahead PV energy generation forecasting auto-encoder convolutional neural network (CNN) meteorological elements

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Multi-Type Diesel Engines Operating Condition Recognition Method Based on Stacked auto-encoder and Feature Transfer Learning

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IEEE ACCESS 2021年 9卷 31043-31052页

作者： Xiong, Guoqing Ma, Wensheng Zhao, Nanyang Zhang, Jinjie Jiang, Zhinong Mao, Zhiwei Beijing Univ Chem Technol Beijing Key Lab HighEnd Mech Equipment Hlth Beijing 100029 Peoples R China

It is of great significance to diagnose the fault of diesel engine, which is widely used in many important fields as key power equipment. The accuracy of fault diagnosis can be effectively improved by obtaining the complex and changeable operating conditions, which can result in the change of monitoring signals. This study proposes a variable operating conditions recognition method based on stacked auto-encoder (SAE) and feature transfer learning. In this method, the vibration in the firing angle domain collected from multi-sensor signals is reconstructed. Then a feature set sensitive to working conditions is extracted from the recombinant signals by a well-constructed stack auto-encoder. According to the dataset test, the softmax classifier can effectively get a high recognition accuracy. Considering that the fault may affect the condition identification, the misfire fault that has a great influence on firing angle domain signals is used to test the robustness of the proposed method. Besides, to enable a well-trained test rig with a large amount of data to be effectively applied to another unit that lacks data, the BDA transfer learning method is used to map the operating conditions of two different engines to the same feature space. The results of experiments conducted on two large power marine multi-cylinder diesel engines show that BDA is capable of transferring the sensitive features of operating conditions.

关键词： Diesel engines Feature extraction Fault diagnosis Transfer learning Vibrations Engines Neural networks Diesel engine operating condition recognition auto-encoder transfer learning

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Conditional Variational auto-encoder and Extreme Value Theory Aided Two-Stage Learning Approach for Intelligent Fine-Grained Known/Unknown Intrusion Detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 3538-3553页

作者： Yang, Jian Chen, Xiang Chen, Shuangwu Jiang, Xiaofeng Tan, Xiaobin Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China

Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on organizations and information infrastructure. This paper aims at developing an intelligent intrusion detection system capable of classifying known attacks as well as inferring unknown ones. To achieve this, we formulate the problem of fine-grained known/unknown intrusion detection as a two-stage minimization problem, where the first stage is to seek a score measure for minimizing the empirical risk of misclassifying the known attacks, while the second stage is to find another score measure for minimizing the identification risk of inferring unknown attacks. The hierarchical nature of problem formulation allows us to employ the class conditioned auto-encoders to construct a hierarchical intrusion detection framework. Since the reconstruction errors of unknown attacks are generally higher than that of the known attacks, we further employ extreme value theory in the second stage to model the distribution of reconstruction errors for differentiating known/unknown attack. To further reduce the false positive rate, we add a benign clustering module for learning the multimodal distribution of benign traffic. We conduct an experiment on two widely used datasets for assessing intrusion detection. The results show that the proposed method improves the detection rate of unknown attacks while keeping a low false positive rate.

关键词： Intrusion detection Feature extraction Machine learning Support vector machines Anomaly detection Training Deep learning Intrusion detection artificial intelligence auto-encoder extreme value theory

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Deep multi-kernel auto-encoder network for clustering brain functional connectivity data

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NEURAL NETWORKS 2021年 135卷 148-157页

作者： Lu, Hu Liu, Saixiong Wei, Hui Chen, Chao Geng, Xia Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Fudan Univ Sch Comp Sci Lab Cognit & Model Algorithm Shanghai 200433 Peoples R China

In this study, we propose a deep-learning network model called the deep multi-kernel auto-encoder clustering network (DMACN) for clustering functional connectivity data for brain diseases. This model is an end-to-end clustering algorithm that can learn potentially advanced features and cluster disease categories. Unlike other auto-encoders, DMACN has an added self-expression layer and standard back propagation is used to learn the features that are beneficial for clustering brain functional connectivity data. In the self-expression layer, the kernel matrix is constructed to extract effective features and a new loss function is proposed to constrain the clustering portion, which enables the training of a deep neural learning network that tends to cluster. To test the performance of the proposed algorithm, we applied the end-to-end deep unsupervised clustering algorithm to brain connectivity data. We then conducted experiments based on four public brain functional connectivity data sets and our own functional connectivity data set. The DMACN algorithm yielded good results in various evaluations compared with the existing clustering algorithm for brain functional connectivity data, the deep auto-encoder clustering algorithm, and several other relevant clustering algorithms. The deep learning-based clustering algorithm has great potential for use in the unsupervised recognition of brain diseases. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Deep neural network Brain functional connectivity auto-encoder Multi-kernel Disease diagnosis

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Robust image Translation and Completion Based on Dual auto-encoder With Bidirectional Latent Space Regression

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IEEE ACCESS 2019年 7卷 58695-58703页

作者： Lee, Sukhan Ul Islam, Naeem Sungkyunkwan Univ Intelligent Syst Res Inst Dept Elect & Comp Engn Suwon 440746 South Korea

automated image translation and completion is a subject of keen interest due to their impact on image representation, interpretation, and enhancement. To date, a conditional or a dual adversarial framework with a convolutional auto-encoder embedded as a generator is known to offer the best accuracy in image translation. However, although the frequency is excellent, the adversarial framework may suffer from a lack of generality, i.e., the accuracy drops when translating incomplete and corrupted data given as untrained noisy input data. This paper proposes an approach to robust image-to-image translation that offers a high level of generality while keeping accuracy high as well. The proposed approach is referred to here as a dual auto-encoder with bidirectional latent space regression or Bi-directionally Associative DualAE, for short. The proposed BA-DualAE is configured with two auto-encoders the individual latent spaces of which are tightly associated by a bidirectional regression network. Once the two auto-encoders are trained independently for their respective domains, and then, the bidirectional regression network is trained to learn the general association between data pairs. With its capability of robust and bidirectional image translation, BA-DualAE performed direct image completion with no iterative search. The experiments with photo-sketch datasets demonstrated that the proposed BA-DualAE is highly robust under incomplete or corrupted data conditions and is far superior to adversarial frameworks in terms of generality and robustness.

关键词： Convolutional neural network auto-encoder bidirectional latent space regression image-to-image translation generative adversarial network

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Deep Geo-Constrained auto-encoder for Non-Landmark GPS Estimation

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IEEE TRANSACTIONS ON BIG DATA 2019年第2期5卷 120-133页

作者： Jiang, Suhui Kong, Yu Fu, Yun Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Engn Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Comp & Informat Sci Boston MA 02115 USA

This paper addresses the problem of geotagging images, i.e., assigning GPS coordinates (i.e., latitude, longitude) to images using image contents. Due to the huge appearance variability of visual features across the world, the images' contents and their GPS coordinates may be inconsistent. This means images captured from geographically close areas may appear visually distinct;and images with visually similar contents may be taken from geographically distant areas. In this paper, we propose a deep Geo-constrained auto-encoder (DGAE) to solve these inconsistency problems. Using clustered GPS data and visual data, our approach identifies inconsistent data pairs (i.e., image, GPS). We then propose a novel deep learning framework that can learn similar feature representations for geographically close images and distinct feature representations for geographically distant images. We introduce two new constraints: the same-area constraint and the easy-confusing constraint to our feature learning networks. The former one penalizes images from the same area but with very distinct visual features, and the latter one penalizes images from distant areas but with very similar visual features. A deep architecture is developed to further improve learning discriminative features, which can disambiguate different geometric locations. Our approach is extensively evaluated on a newly-compiled large image geotagging dataset from large-scale community-contributed images with 664,720 images and outperforms comparison approaches.

关键词： GPS estimation deep learning auto-encoder

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Efficient Non-Compression auto-encoder for Driving Noise-Based Road Surface Anomaly Detection

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2022年第11期17卷 1648-1656页

作者： Park, YeongHyeon Jung, JongHee SK Planet Co Ltd Seongnam South Korea

Wet weather makes water film over the road and that film causes lower friction between tire and road surface. When a vehicle passes the low-friction road, the accident can occur up to 35% higher frequency than a normal condition road. In order to prevent accidents as above, identifying the road condition in real-time is essential. Thus, we propose a convolutional auto-encoder-based anomaly detection model for taking both less computational resources and achieving higher anomaly detection performance. The proposed model adopts a non-compression method rather than a conventional bottleneck structured auto-encoder. As a result, the computational cost of the neural network is reduced up to 1 over 25 compared with the conventional models, and the anomaly detection performance is improved by up to 7.72%. Thus, we conclude the proposed model as a cutting-edge algorithm for real-time anomaly detection. (c) 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： anomaly detection auto-encoder non-compression road safety vehicle noise

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Compressed Sensing Verses auto-encoder: On the Perspective of Signal Compression and Restoration

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IEEE ACCESS 2024年 12卷 41967-41979页

作者： Jeong, Jin-Young Ozger, Mustafa Lee, Woong-Hee Korea Univ Dept Control & Instrumentat Engn Sejong Si 30019 South Korea KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-11428 Stockholm Sweden Dongguk Univ Seoul Div Elect & Elect Engn Seoul 04620 South Korea

This paper presents a comparison between compressed sensing (CS) and auto-encoder (AE) for compression and restoration of signals. The study used K-sparse vectors and generated an under-determined system, which is a system of linear equations with fewer equations than unknowns. By using CS and AE under various specific conditions, the accuracy of the signal restoration is compared with mean squared error (MSE). The experimental methodology includes comparing and analyzing the signal recovery performance by altering the algorithm and various parameters. The result represents the performance and accuracy of signal compression and restoration obtained using both techniques. It also provides a comprehensive analysis of CS and AE methods. The importance of this research and the possibility of practical application in various fields are discussed. Overall, this study provides insights into the comparison of CS and AE techniques in the field of sparse signal compression and restoration.

关键词： Compressed sensing auto-encoder signal processing compression restoration

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QoS Prediction for Mobile Edge Service Recommendation With auto-encoder

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IEEE ACCESS 2019年 7卷 62312-62324页

作者： Yin, Yuyu Zhang, Weipeng Xu, Yueshen Zhang, He Mai, Zhida Yu, Lifeng Hangzhou Dianzi Univ Sch Comp Hangzhou 310018 Zhejiang Peoples R China Minist Educ Key Lab Complex Syst Modeling & Simulat Hangzhou 310018 Zhejiang Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Shaanxi Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Xanten Guangdong Dev Co Ltd Foshan 528200 Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou 310023 Zhejiang Peoples R China

In the mobile edge computing environment, there are a large number of mobile edge services which are the carriers of various mobile intelligent applications. So how to recommend the most suitable candidate from such a huge number of available services is an urgent task, especially the recommendation task based on quality-of-service (QoS). In traditional service recommendation, collaborative filtering (CF) has been studied in academia and industry. However, due to the mobility of users and services, there exist several defects that limit the application of the CF-based methods, especially in an edge computing environment. The most important problem is the cold-start. In this paper, we propose an ensemble model which combines the model-based CF and neighborhood-based CF. Our approach has two phases, i.e., global features learning and local features learning. In the first phase, to alleviate the cold-start problem, we propose an improved auto-encoder which deals with sparse inputs by pre-computing an estimate of the missing QoS values and can obtain the effective hidden features by capturing the complex structure of the QoS records. In the second phase, to further improve prediction accuracy, a novel computation method is proposed based on Euclidean distance that aims to address the overestimation problem. We introduce two new concepts, common invocation factor and invocation frequency factor, in similarity computation. Then we propose three prediction models, containing two individual models and one hybrid model. The two individual models are proposed to utilize user similar neighbors and service similar neighbors, and the hybrid model is to utilize all neighbors. The experiments conducted in a real-world dataset show that our models can produce superior prediction results and are not sensitive to parameter settings.

关键词： QoS prediction service recommendation auto-encoder features learning similarity computation

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Sparse auto-encoder with Smoothed Regularization

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NEURAL PROCESSING LETTERS 2018年第3期47卷 829-839页

作者： Zhang, Li Lu, Yaping Wang, Bangjun Li, Fanzhang Zhang, Zhao Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Soochow Univ Joint Int Res Lab Machine Learning & Neuromorph C Suzhou 215006 Jiangsu Peoples R China

Improving the performance on data representation of an auto-encoder could help to obtain a satisfying deep network. One of the strategies to enhance the performance is to incorporate sparsity into an auto-encoder. Fortunately, sparsity for the auto-encoder has been achieved by adding a Kullback-Leibler (KL) divergence term to the risk functional. In compressive sensing and machine learning, it is well known that the regularization is a widely used technique which can induce sparsity. Thus, this paper introduces a smoothed regularization instead of the mostly used KL divergence to enforce sparsity for auto-encoders. Experimental results show that the smoothed regularization works better than the KL divergence.

关键词： auto-encoder Sparsity KL divergence Smoothed l(1) regularization Data representation

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