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28th International Conference on Robotics in Alpe-Adria-Danube Region (RAAD)

作者： Loncarevic, Zvezdan Pahic, Rok Simonic, Mihael Ude, Ales Gams, Andrej Jozef Stefan Inst Jamova Cesta 39 Ljubljana 1000 Slovenia

ISBN: (纸本)9783030196486;9783030196479

Autonomous learning and adaptation of robotic trajectories by complex robots in unstructured environments, for example with the use of reinforcement learning, very quickly encounters problems where the dimensionality of the search space is beyond the range of practical use. Different methods of reducing the dimensionality have been proposed in the literature. In this paper we explore the use of deep autoencoders, where the dimensionality of autoencoder latent space is low. However, a database of actions is required to train a deep autoencoder network. The paper presents a study on the number of required database samples in order to achieve dimensionality reduction without much loss of information.

关键词： deep autoencoder Reinforcement learning Robotic throwing

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deep autoencoder with localized stochastic sensitivity for short-term load forecasting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 130卷 106954-106954页

作者： Wang, Ting Lai, Chun Sing Ng, Wing W. Y. Pan, Keda Zhang, Mingyang Vaccaro, Alfredo Lai, Loi Lei South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Sch Comp Sci & Engn Guangzhou 510630 Peoples R China Brunel Univ London Brunel Interdisciplinary Power Syst Res Ctr London UB8 3PH England Guangdong Univ Technol Dept Elect Engn Sch Automat Guangzhou 510006 Peoples R China Univ Sannio Engn Dept I-82100 Benevento Italy

This paper presents a short-term electric load forecasting model based on deep autoencoder with localized stochastic sensitivity (D-LiSSA). D-LiSSA can learn informative hidden representations from unseen samples by minimizing the perturbed error (including the training error and stochastic sensitivity) from historical load data. Specifically, this general deep autoencoder network as a deep learning model improves prediction accuracy and reliability. Moreover, a nonlinear fully connected feedforward neural network as a regression layer is applied to forecast the short-term load, with the generalization capability of the proposed model using hidden representations learned by D-LiSSA. The performance of D-LiSSA is evaluated using real-world public electric load markets of France (FR), Germany (GR), Romania (RO), and Spain (ES) from ENTSO-E. Extensive experimental results and comparisons with the classical and state-of-the-art models show that D-LiSSA yields accurate load forecasting results and achieves desired reliable capability. For instance, with the French case, D-LiSSA yields the lowest mean absolute error, mean absolute percentage error, root mean squared error;providing up to 61.89%, 63.20%, and 56.40% forecasting accuracy improvements as compared to the benchmark model for forecasting hourly horizon, respectively.

关键词： Short-term load forecasting deep autoencoder deep learning Stochastic sensitivity

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Abnormal Event Detection based on deep autoencoder fusing optical flow 36

Abnormal Event Detection based on Deep Autoencoder fusing op...

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第36届中国控制会议

作者： Meina Qiao Tian Wang Jiakun Li Ce Li Zhiwei Lin Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University College of Electrical and Information Engineering Lanzhou University of Technology School of Computing Ulster University Institute Charles Delaunay-LM2S-UMR STMR 6279 CNRS University of Technology of Troyes

ISBN: (纸本)9781538629185

As an important research topic in computer vision,abnormal detection has gained more and more *** order to detect abnormal events effectively,we propose a novel method using optical flow and deep *** our model,optical flow of the original video sequence is calculated and visualized as optical flow image,which is then fed into a deep *** the deep autoencoder extract features from the training samples which are compressed to low dimension ***,the normal and abnormal samples gather separately in the coordinate *** the evaluation,we show that our approach outperforms the existing methods in different scenes,in terms of accuracy.

关键词： Abnormal event detection deep autoencoder Optical flow

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Fault Detection of Elevator System using deep autoencoder Feature Extraction for Acceleration Signals 11

Fault Detection of Elevator System using Deep Autoencoder Fe...

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11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) / 11th International Conference on Knowledge Engineering and Ontology Development (KEOD)

作者： Mishra, Krishna Mohan Huhtala, Kalevi J. Tampere Univ Unit Automat Technol & Mech Engn Tampere Finland

ISBN: (纸本)9789897583827

In this research, we propose a generic deep autoencoder model for automatic calculation of highly informative deep features from the elevator time series data. Random forest algorithm is used for fault detection based on extracted deep features. Maintenance actions recorded are used to label the sensor data into healthy or faulty. Avoiding false positives are performed with the rest of the healthy data in terms of validation of the model to prove its efficacy. New extracted deep features provide 100% accuracy in fault detection along with avoiding false positives, which is better than existing features. Random forest was also used to detect faults based on existing features to compare results. New deep features extracted from the dataset with deep autoencoder random forest outperform the existing features. Good classification and robustness against overfitting are key characteristics of our model. This research will help to reduce unnecessary visits of service technicians to installation sites by detecting false alarms in various predictive maintenance systems.

关键词： Elevator System deep autoencoder Fault Detection Feature Extraction Random Forest

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Profile Extraction and deep autoencoder Feature Extraction for Elevator Fault Detection 16

Profile Extraction and Deep Autoencoder Feature Extraction f...

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16th International Joint Conference on E-Business and Telecommunications (ICETE)

作者： Mishra, Krishna Mohan Krogerus, Tomi R. Huhtala, Kalevi J. Tampere Univ Unit Automat Technol & Mech Engn Tampere Finland

ISBN: (纸本)9789897583780

In this paper, we propose a new algorithm for data extraction from time series signal data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction elevator start and stop events are extracted from sensor data, and a generic deep autoencoder model is also developed for automated feature extraction from the extracted profiles. After this, extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved 100% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperforms results using existing features. Existing features are also classified with random forest to compare results. Our developed algorithm provides better results due to the new deep features extracted from the dataset compared to existing features. This research will help various predictive maintenance systems to detect false alarms, which will in turn reduce unnecessary visits of service technicians to installation sites.

关键词： Elevator System deep autoencoder Fault Detection Feature Extraction Random Forest Profile Extraction

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SHAPE RETRIEVAL USING deep autoencoder LEARNING REPRESENTATION 13

SHAPE RETRIEVAL USING DEEP AUTOENCODER LEARNING REPRESENTATI...

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13th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Xu, Guoqing Fang, Weiwei Nanyang Inst Technol Sch Comp & Informat Engn Nanyang 473004 Peoples R China

ISBN: (纸本)9781509061266

deep learning has shown to be very effective in variety of applications including image classification and object recognition. In this paper we use deep autoencoder for compact shape representation learning and image retrieval. In this method the autoencoder is a 4-layer coding network, and the original shape images after scale normalization are used to pre-train the autoencoder in an unsupervised way. Then fine-tuning procedure is executed on the 4-layer coding network to refine the learned weight matrixes. Finally a learned 40-dimension vector for each shape image is used as its features, and the similarity between any two shapes is measured using standard cosine similarity. The image retrieval performance of the proposed method is evaluated on the Swedish leaf database using precision and recall measurement, and compared with the classical Fourier shape descriptor. The experimental results indicate that the proposed method reaches the higher precision at the same recall value among compared methods.

关键词： deep autoencoder representation learning image retrieval Swedish leaf shape database

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Training deep autoencoder via VLC-Genetic Algorithm 24th

Training Deep Autoencoder via VLC-Genetic Algorithm

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24th International Conference on Neural Information Processing (ICONIP)

作者： Khan, Qazi Sami Ullah Li, Jianwu Zhao, Shuyang Beijing Inst Technol Sch Comp Sci & Technol Beijing Key Lab Intelligent Informat Technol Beijing Peoples R China

ISBN: (纸本)9783319700960;9783319700953

Recently, both supervised and unsupervised deep learning techniques have accomplished notable results in various fields. However neural networks with back-propagation are liable to trapping at local minima. Genetic algorithms have been popular as a class of optimization techniques which are good at exploring a large and complex space in an intelligent way to find values close to the global optimum. In this paper, a variable length chromosome genetic algorithm assisted deep autoencoder is proposed. Firstly, the training of autoencoder is done with the help of variable length chromosome genetic algorithm. Secondly, a classifier is used for the classification of encoded data and compare the classification accuracy with other state-of-the-art methods. The experimental results show that the proposed method achieves competitive results and produce sparser networks.

关键词： Neural networks Genetic algorithm Variable length chromosome deep autoencoder

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ECG-based Biometrics using a deep autoencoder for Feature Learning An Empirical Study on Transferability 6

ECG-based Biometrics using a Deep Autoencoder for Feature Le...

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6th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Eduardo, Afonso Aidos, Helena Fred, Ana Univ Lisbon Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9789897582226

Biometric identification is the task of recognizing an individual using biological or behavioral traits and, recently, electrocardiogram has emerged as a prominent trait. In addition, deep learning is a fast-paced research field where several models, training schemes and applications are being actively investigated. In this paper, an ECG-based biometric system using a deep autoencoder to learn a lower dimensional representation of heartbeat templates is proposed. A superior identification performance is achieved, validating the expressiveness of such representation. A transfer learning setting is also explored and results show practically no loss of performance, suggesting that these deep learning methods can be deployed in systems with offline training.

关键词： Biometrics User Identification Electrocardiogram (ECG) deep Learning Feature Learning Transfer Learning deep autoencoder

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Fault Diagnosis Method for Mobile Energy Storage Cabin Based on Digital Twin Technology and deep autoencoder 3

Fault Diagnosis Method for Mobile Energy Storage Cabin Based...

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3rd Asian Conference on Frontiers of Power and Energy

作者： Liang, Hongfu Luo, Tian Zhang, Xuan Chen, Jun Ding, Bo State Grid Chongqing Elect Power Co Shinan Power Supply Branch Chongqing Peoples R China Chongqing Guanghui Power Supply Serv Co Ltd Shinan Branch Chongqing Peoples R China Beijing Zhongdian Puhua Informat Technol Co Ltd Chongqing Business Dept Xian Branch Chongqing Peoples R China

ISBN: (纸本)9798350378467;9798350367676

A fault diagnosis method for mobile energy storage cabin based on digital twin technology and deep autoencoder is proposed to address the problems of time-consuming, labor-intensive, and low accuracy in traditional fault diagnosis methods. Firstly, a fault diagnosis model based on digital twin technology is constructed based on the mechanism and data model of mobile energy storage cabin, and the reliability of diagnosis is improved through the combination of virtual and real. Then, a deep autoencoder model is designed, which adopts an adaptive data filtering mechanism based on an improved spectral clustering algorithm, and a fusion loss function is designed to optimize the model parameters. Finally, the deep autoencoder model is deployed in the digital twin model to achieve fault diagnosis by analyzing energy storage cabin data. Based on the selected mobile energy storage cabin data, experimental analysis is conducted on the proposed method, and the results shows that the average accuracy and time consumption of fault diagnosis are 96.38% and 3.73s, respectively, demonstrating significant advantages in state diagnosis.

关键词： mobile energy storage cabin digital twin technology deep autoencoder fault diagnosis improved spectral clustering algorithm fusion loss function

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A deep autoencoder and RNN Model for Indoor Localization with Variable Propagation Loss 17

A Deep Autoencoder and RNN Model for Indoor Localization wit...

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17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

作者： Espindola, Allan Viegas, Eduardo K. Traleski, Andre Pellenz, Marcelo E. Santin, Altair O. Pontificia Univ Catolica Parana Grad Program Comp Sci Curitiba Parana Brazil

ISBN: (纸本)9781665428545

Current machine learning techniques for indoor localization of wireless devices assume a single wireless propagation loss setting, making them unfeasible for reliable production deployment. This paper proposes a new indoor localization technique designed for variable propagation loss environments based on deep autoencoder and recurrent neural network (RNN), implemented threefold. This paper proposes a new indoor localization technique designed for variable loss propagation environments based on deep autoencoder and recurrent neural network (RNN), implemented in three stages. First, we extract statistical feature values from collected RSSI. Second, a deep autoencoder is used to remove wireless propagation noises introduced by variable fading settings. Third, an RNN performs the localization task taking into account previous sensor measurements. Experiments performed in 3 simulated testbeds with distinct propagation loss settings have shown that current approaches decrease localization accuracy by up to 30% when a different propagation loss is faced. In addition, our proposed model improved localization accuracy by up to 25.8% regardless of the current environment propagation loss.

关键词： WiFi-based indoor localization deep autoencoder Recurrent Neural Network

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