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denoising autoencoder based topology identification in distribution systems with missing measurements

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2023年第1期154卷

作者： Raghuvamsi, Y. Teeparthi, Kiran Kosana, Vishalteja Natl Inst Technol Andhra Pradesh Dept Elect Engn Tadepalligudem India

In distribution systems, the loss of existing real measurements and the stochastic penetration levels of renewable energy sources (RES) are the major issues while identifying the correct topology. The frequent switching and control actions of RES can lead to many topology changes. The loss of existing measurements from the physical meters due to failures in meters or communication aggravates the topology identification process and also it brings the convergence issues in state estimation algorithm. In this work, a deep learning approach is utilized to address the issue of incomplete measurement data and to provide accurate topology identification. A temporal convolutional denoising autoencoder (TCDAE) is developed for dimensionality reduction, reconstruction of incomplete data, and accurate topology identification. The model performance is compared with other traditional deep learning approaches such as simple denoising autoencoder (DAE), convolutional DAE, and the LSTM-based DAE. The power injection measurements and the line current sensor measurements are considered as the input measurement data for topology identification. The simulations have been carried out on IEEE 13-node and IEEE 37-node distribution test systems. The corresponding results indicate that the model can fill the incomplete data with more accurate values and its classification results prove that the model performance is effective in identifying the correct topology.

关键词： Topology identification denoising autoencoder Temporal convolutional network (TCN) Huber-loss function Missing data Line current sensors

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Advanced Phishing Filter Using autoencoder and denoising autoencoder 2017

Advanced Phishing Filter Using Autoencoder and Denoising Aut...

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International Conference on Big Data and Internet of Things (BDIOT)

作者： Douzi, Samira Amar, Meryem El Ouahidi, Bouabid Univ Mohammed 5 Fac Sci LRI BO 1014 Rabat Morocco

ISBN: (纸本)9781450354301

Phishing is referred as an attempt to obtain sensitive information, such as usernames, passwords, and credit card details (and, indirectly, money), for malicious reasons, by disguising as a trustworthy entity in an electronic communication [1]. Hackers and malicious users, often use Emails as phishing tools to obtain the personal data of legitimate users, by sending Emails with authentic identities, legitimate content, but also with malicious URL, which help them to steal consumer's data. The high dimensional data in phishing context contains large number of redundant features that significantly elevate the classification error. Additionally, the time required to perform classification increases with the number of features. So extracting complex Features from phishing Emails requires us to determine which Features are relevant and fundamental in phishing detection. The dominant approaches in phishing are based on machine learning techniques;these rely on manual feature engineering, which is time consuming. On the other hand, deep learning is a promising alternative to traditional methods. The main idea of deep learning techniques is to learn complex features extracted from data with minimum external contribution [2]. In this paper, we propose new phishing detection and prevention approach, based first on our previous spam filter [3] to classify textual content of Email. Secondly it's based on autoencoder and on denoising autoencoder (DAE), to extract relevant and robust features set of URL (to which the website is actually directed), therefore the features space could be reduced considerably, and thus decreasing the phishing detection time.

关键词： autoencoder denoising autoencoder phishing Spam-filter

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Fake Twitter followers detection by denoising autoencoder 17

Fake Twitter followers detection by Denoising Autoencoder

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Castellini, Jacopo Poggioni, Valentina Sorbi, Giulia Univ Perugia Dept Math & Comp Sci Perugia Italy

ISBN: (纸本)9781450349512

Gaining followers on the Twitter platform has become a rapid way to increase one's credibility on this social network, that in the last few years has become a launch pad for new trends and to influence people opinions. So, many people have begun to buy fake followers on underground markets appositely created to sold them. Therefore, identifying fake followers profiles is useful to maintain the balance between real influential people on the network and people who simply exploited this mechanism. This work presents a model based on artificial neural networks able to detect fake Twitter profiles. In particular, a denoising autoencoder has been implemented as anomaly detector trained with a semi-supervised learning approach. The model has been tested on a benchmark already used in literature and results are presented.

关键词： fake Twitter followers denoising autoencoder autoencoder artificial neural networks anomaly detection one class mining semi-supervised learning

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A Novel denoising autoencoder Assisted Segmentation Algorithm for Cotton Field

A Novel Denoising Autoencoder Assisted Segmentation Algorith...

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Chinese Automation Congress (CAC)

作者： Li, Yanan Cao, Zhiguo Xiao, Yang Lu, Hao Zhu, Yanjun Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9781467371896

Crop segmentation from the images captured in the outdoor field is a complex task in agriculture automation, let alone detecting some specific crops with one method. Cotton, as one of the four major economic crops, is of great significance to the development of the national economy. In this paper, a novel strategy based on the deep learning is utilized to establish the crop classifier in the RGB vector color space in order to realize the specific crop image segmentation. To the best of our knowledge, little research has been done to crop segmentation in the wild cotton field with digital cameras. To verify the performance of the proposed method, two specific crops (cotton plants and raw cotton) grown in the cotton field are demonstrated in this paper. Experiment results show that our method outperforms other state-of-art algorithms on cotton plants (raw cotton) segmentation in yielding the highest performance with the lowest mean square deviation. Moreover, the impact of different color spaces to the proposed method is compared. The proposed crop segmentation method can not only be used to green crop segmentation in the field, but segment specific crop like cotton.

关键词： specific crop segmentation denoising autoencoder cotton

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Rogue Emitter Detection Using Hybrid Network of denoising autoencoder and Deep Metric Learning

Rogue Emitter Detection Using Hybrid Network of Denoising Au...

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IEEE International Conference on Communications (IEEE ICC)

作者： Yang, Zeyang Fu, Xue Gui, Guan Lin, Yun Gacanin, Haris Sari, Hikmet Adachi, Fumiyuki NJUPT Coll Telecommun & Informat Engn Nanjing Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Rhein Westfal TH Aachen Inst Commun Technol & Embedded Syst Aachen Germany Tohoku Univ Int Res Inst Disaster Sci IRIDeS Sendai Miyagi Japan

ISBN: (纸本)9781538674628

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under friendly environments. However, these methods perform unstably under low signal-to-noise ratio (SNR) scenarios. To address this problem, we propose a robust RED method, which is a hybrid network of denoising autoencoder and deep metric learning (DML). Specifically, denoising autoencoder is adopted to mitigate noise interference and then improve its robustness under low SNR while DML plays an important role to improve the feature discrimination. Several typical experiments are conducted to evaluate the proposed RED method on an automatic dependent surveillance-Broadcast dataset and an IEEE 802.11 dataset and also to compare it with existing RED methods. Simulation results show that the proposed method achieves better RED performance and higher noise robustness with more discriminative semantic vectors than existing methods.

关键词： Deep learning deep metric learning denoising autoencoder feature discrimination rogue emitter detection

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Text Window denoising autoencoder: Building Deep Architecture for Chinese Word Segmentation

Text Window Denoising Autoencoder: Building Deep Architectur...

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2nd Annual Conference of China-Computer-Federation Technical-Committee-of-Chinese-Information on Natural Language Processing and Chinese Computing (CCF TCCI NLPCC)

作者： Wu, Ke Gao, Zhiqiang Peng, Cheng Wen, Xiao Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9783642416439;9783642416446

Deep learning is the new frontier of machine learning research, which has led to many recent breakthroughs in English natural language processing. However, there are inherent differences between Chinese and English, and little work has been done to apply deep learning techniques to Chinese natural language processing. In this paper, we propose a deep neural network model: text window denoising autoencoder, as well as a complete pre-training solution as a new way to solve classical Chinese natural language processing problems. This method does not require any linguistic knowledge or manual feature design, and can be applied to various Chinese natural language processing tasks, such as Chinese word segmentation. On the PKU dataset of Chinese word segmentation bakeoff 2005, applying this method decreases the F1 error rate by 11.9% for deep neural network based models. We are the first to apply deep learning methods to Chinese word segmentation to our best knowledge.

关键词： Deep Learning Word Segmentation denoising autoencoder Chinese Natural Language Processing

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A Degradation Indicator Construction Method for Aeroengine Lifetime Estimation based on denoising autoencoder 3

A Degradation Indicator Construction Method for Aeroengine L...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Niu Yifan Shao Jingfeng Xian Polytech Univ Sch Management Xian Shaanxi Peoples R China

ISBN: (纸本)9781538662434

Degradation indicator construction is essential for the lifetime estimation process, since it provides useful indicator for lifetime estimation effectively. However, the degradation indicator is hard to constructed because of the complex relationships between various parameters. This paper proposes a degradation indicator construction method for aeroengine based on denoising autoencoder (DAE) algorithm. To make the degradation data can indicate the equipment operation state easily, the spearman correlation was adopted for the process of equipment operating state characterizing. Thus, the data smoothing method was used to smooth the degradation data, in order to make the prediction results of the equipment lifetime more accurate. Moreover, the degradation construction method was verified to predict the aeroengine lifetime by adopting the aeroengine degradation data provide by NASA datasets. The expected results of this construction method for degradation indicator based on parameter fusion has a lower mean-square error.

关键词： denoising autoencoder Lifetime Estimation Aeroengine Degradation Indicator

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Research On Text Classification of denoising autoencoder Based on Additional Momentum and Adaptive Learning Rate 11

Research On Text Classification of Denoising Autoencoder Bas...

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11th International Symposium on Computational Intelligence and Design (ISCID)

作者： Yang, Zhenyu Pang, Xue Qilu Univ Technol Sch Informat Jinan Shandong Peoples R China

ISBN: (纸本)9781538685273

Aiming at the problem of slow convergence speed and long training time of traditional denoising autoencoder (DAE) deep learning model in the process of feature expression, a deep learning model of DAE additional with momentum term and adaptive learning rate is proposed in this paper, which is used for text feature learning. And at the last layer of the model, softmax is used for classification. Finally, the text categorization experiments are carried out by using KNN classifier, denoising autoencoder model and improved model of denoising autoencoder respectively. The experimental results show that the reconstruction error curve of the improved DAE model is obviously lower than the traditional DAE model after 30 iterations, which greatly improves the convergence speed of the model. The value of the reconstruction error is also reduced. Moreover, the comprehensive correct rate of the improved DAE model is 95%, which is higher than that of KNN algorithm (88%) and traditional DAE model (92%). Experiments show that the method is feasible and practical.

关键词： classification deep learning denoising autoencoder adagrad algorithm momentum term

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denoising autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs

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SENSORS 2021年第15期21卷 5019-5019页

作者： Chen, Yeou-Jiunn Chen, Pei-Chung Chen, Shih-Chung Wu, Chung-Min Southern Taiwan Univ Sci & Technol Dept Elect Engn Tainan 71005 Taiwan Southern Taiwan Univ Sci & Technol Dept Mech Engn Tainan 71005 Taiwan Kun Shan Univ Dept Intelligent Robot Engn Tainan 710303 Taiwan

For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.

关键词： denoising autoencoder steady state visually evoked potential brain computer interface noise suppression deep neural network

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DUAL denoising autoencoder FEATURE LEARNING FOR CANCER DIAGNOSIS 18

DUAL DENOISING AUTOENCODER FEATURE LEARNING FOR CANCER DIAGN...

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18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Gao, Yuqing Ng, Wing W. Y. Wang, Ting Kwong, Sam South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Sch Comp Sci & Engn Guangzhou Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781728114194

Microarray data analysis has emerged as a strong tool for cancer diagnosis. Nevertheless, researches on it are significantly challenging as the microarray datasets are imbalanced and high-dimensional with relatively small sample size. In this paper, we utilized Dual denoising autoencoder Features (DDAF), which integrates two denoising Auto-Encoders (DAE) with different activation function to map the features for both minority and majority classes into a better classification representation. The experimental results on four typical microarray datasets show that the DDAF outperforms the Dual autoencoder Features (DAF) and the Cost-sensitive Oversampling Stacked denoising Auto-Encoder (CO-SDAE), rendering the robust ability for dimensionality reduction and imbalanced classification.

关键词： imbalanced classification denoising autoencoder feature learning

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