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12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Bougoffa, Mouaad Benmoussa, Samir Djeziri, Mohand Contaret, Thierry Badji Mokhtar Annaba Univ LASA Lab Annaba Algeria Aix Marseille Univ Univ Toulon CNRS IM2NP Marseille France

This paper deals with the modeling of a photovoltaic system connected to a grid for the simulation of normal and faulty operations and the generation of a data-set for learning a fault detection algorithm based on a stacked autoencoder. To evaluate the effectiveness of the proposed approach, a Mean Squared Error is used. This method enables early fault detection, enhancing system relability and efficiency while addressing the need for proactive fault management in the system under normal conditions. Obtained results under different radiation and temperature conditions highlight the relevance of the proposed model and the effectiveness of the fault detection algorithm. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Photovoltaic fault detection Mean Squared Error stacked autoencoder

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Groundwater Pollution Source and Aquifer Parameter Estimation Based on a stacked autoencoder Substitute

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WATER 2024年第18期16卷 2564页

作者： Wang, Han Zhang, Jinping Li, Hang Li, Guanghua Guo, Jiayuan Lu, Wenxi Minist Water Resources Song Liao River Water Resources Commiss Changchun 130000 Peoples R China Song Liao River Water Resources Commiss River Basin Planning & Policy Res Ctr Changchun 130000 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130000 Peoples R China

A concurrent heuristic search iterative process (CHSIP) is used for estimating groundwater pollution sources and aquifer parameters in this work. Frequent calls to carry out a numerical simulation of groundwater pollution have generated a huge calculated load during the CHSIP. Therefore, a valid means to mitigate this is building a substitute to emulate the numerical simulation at a low calculated load. However, there is a complicated nonlinear correlativity between the import and export of the numerical simulation on account of the large quantity of variables. This leads to a poor approach accuracy of the substitute compared to the simulation when using shallow learning methods. Therefore, we first built a stacked autoencoder substitute, using the deep learning method, to boost the approach accuracy of the substitute compared to the numerical simulation. In total, 400 training samples and 100 testing samples for the substitute were collected by employing the Latin hypercube sampling method and running the numerical simulator. The CHSIP was then employed for estimating the groundwater pollution sources and aquifer parameters, and the estimated outcome was obtained when the CHSIP was terminated. The data analysis, including interval estimation and point estimation, was implemented on the MATLAB platform. A relevant hypothetical case is set to verify our approaches, which shows that the CHSIP is helpful for estimating the groundwater pollution source and aquifer parameters and that the stacked autoencoder method can effectively boost the approach precision of the substitute for the simulator.

关键词： groundwater pollution source estimation concurrent heuristic search iterative process deep learning method substitute stacked autoencoder

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A model-based collaborate filtering algorithm based on stacked autoencoder

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NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 2503-2511页

作者： Yu, Miao Quan, Tianqi Peng, Qinglong Yu, Xu Liu, Lei Qingdao Univ Coll Text & Clothing Qingdao 266071 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Qingdao Univ Coll Mat Sci & Engn Qingdao 266071 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Recently, recommender systems are widely used on various platforms in real world to provide personalized recommendations. However, sparsity is a tough problem in a Collaborate Filtering (CF) recommender system as it always leads to the over-fitting problem. This paper proposes a Model-based Collaborate Filtering Algorithm Based on stacked autoencoder (MCFSAE) to overcome the sparsity problem. In the MCFSAE model, we first convert the rating matrix into a high-dimensional classification dataset with a size equal to the number of ratings. As the number of ratings is usually large scale, the classification performance can be guaranteed. Since the obtained classification dataset is high dimensional, we then utilize stacked autoencoder, which is a good nonlinear feature reduction model, to obtain a high-level low-dimensional feature presentation. Finally, a softmax classification model is used to predict the unknown ratings based on the high-level features. Extensive experiments on EachMovie and MovieLens datasets are conducted to compare the proposed MCFSAE model with other SOTA CF models. Experimental results show that MCFSAE performs better than other CF models, especially when the rating matrix is sparse.

关键词： Recommender systems Collaborate Filtering The sparsity problem Classification problem stacked autoencoder Softmax classification

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Deep Spatio-Temporal Representation for Detection of Road Accidents Using stacked autoencoder

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第3期20卷 879-887页

作者： Singh, Dinesh Mohan, Chalavadi Krishna IIT Hyderabad Dept Comp Sci & Engn Visual Learning & Intelligence Grp Hyderabad 502205 India

Vision-based detection of road accidents using traffic surveillance video is a highly desirable but challenging task. In this paper, we propose a novel framework for automatic detection of road accidents in surveillance videos. The proposed framework automatically learns feature representation from the spatiotemporal volumes of raw pixel intensity instead of traditional hand-crafted features. We consider the accident of the vehicles as an unusual incident. The proposed framework extracts deep representation using denoising autoencoders trained over the normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class support vector machine. Also, the intersection points of the vehicle's trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. We evaluated out proposed approach on real accident videos collected from the CCTV surveillance network of Hyderabad City in India. The experiments on these real accident videos demonstrate the efficacy of the proposed approach.

关键词： Accident detection anomaly detection deep learning stacked autoencoder

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Deep Co-Saliency Detection via stacked autoencoder-Enabled Fusion and Self-Trained CNNs

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第4期22卷 1016-1031页

作者： Tsai, Chung-Chi Hsu, Kuang-Jui Lin, Yen-Yu Qian, Xiaoning Chuang, Yung-Yu Texas A&M Univ Dept Elect & Comp Engn Uvalde TX 77843 USA Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106 Taiwan Natl Chiaorung Univ Dept Comp Sci Hsinchu 300 Taiwan

Image co-saliency detection via fusion-based or learning-based methods faces cross-cutting issues. Fusion-based methods often combine saliency proposals using a majority voting rule. Their performance hence highly depends on the quality and coherence of individual proposals. Learning-based methods typically require ground-truth annotations for training, which are not available for co-saliency detection. In this work, we present a two-stage approach to address these issues jointly. At the first stage, an unsupervised deep learning model with stacked autoencoder (SAE) is proposed to evaluate the quality of saliency proposals. It employs latent representations for image foregrounds, and auto-encodes foreground consistency and foreground-background distinctiveness in a discriminative way. The resultant model, SAE-enabled fusion (SAEF), can combine multiple saliency proposals to yield a more reliable saliency map. At the second stage, motivated by the fact that fusion often leads to over-smoothed saliency maps, we develop self-trained convolutional neural networks (STCNN) to alleviate this negative effect. STCNN takes the saliency maps produced by SAEF as inputs. It propagates information from regions of high confidence to those of low confidence. During propagation, feature representations are distilled, resulting in sharper and better co-saliency maps. Our approach is comprehensively evaluated on three benchmarks, including MSRC, iCoseg, and Cosal2015, and performs favorably against the state-of-the-arts. In addition, we demonstrate that our method can be applied to object co-segmentation and object co-localization, achieving the state-of-the-art performance in both applications.

关键词： Proposals Saliency detection Image segmentation Image reconstruction Reliability Task analysis Fuses Co-saliency detection stacked autoencoder reconstruction residual adaptive fusion optimization self-paced learning CNNs

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Parallel stacked autoencoder with particle swarm optimization for community detection in complex networks

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APPLIED INTELLIGENCE 2022年第3期52卷 3366-3386页

作者： Al-Andoli, Mohammed Tan, Shing Chiang Cheah, Wooi Ping Multimedia Univ Fac Informat Sci & Technol Jalan Ayer Keroh Lama Bukit Bewang Melaka Malaysia Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Taikang East Rd Ningbo 315100 Peoples R China

Community detection is one of the long standing and challenging tasks in the field of Complex Networks (CNs). Recently, deep learning is one of the promising community detection methods, which can learn effectively low-dimensional representation of CNs. However, the existing methods have major drawbacks in terms of local minima and slow convergence, since they use Gradient Descent Backpropagation algorithm (GDBP). This reduces the performance of community detection in terms of effectiveness and efficiency. To overcome these drawbacks, this paper introduces a new parallel deep learning model based on Metaheuristic (MH) algorithm instead of the GDBP algorithm. To be specific, a new parallel stacked autoencoder (SAE) based on particle swarm optimization (PSO) is developed for feature learning and community detection in CNs. The PSO algorithm uses a multi-objective fitness function that includes the standard loss function (i.e., MSE) of the autoencoder and the modularity function to guide SAE optimization and improve community detection performance. In addition, an efficient distributed parallel implementation is proposed to improve the efficiency and scalability of the SAE-based PSO method. The parameter settings of PSO such as features-dimension and number of particles, are tuned and studied to observe their implications on community detection performance. We conducted an experiment comprising datasets of 10 real-world networks to evaluate the proposed method in different parameter settings. The results demonstrated that the SAE-based PSO method is promising and provides a competitive performance against state-of-art methods in community detection. Furthermore, the results showed that the parallel implementation of the proposed method could improve efficiency with three or greater orders of speed.

关键词： Complex networks Community detection stacked autoencoder Particle swarm optimization Parallel computation

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

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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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Enhancing Top-N Recommendation Using stacked autoencoder in Context-Aware Recommender System

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NEURAL PROCESSING LETTERS 2021年第3期53卷 1865-1888页

作者： Abinaya, S. Devi, M. K. Kavitha Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India

Context-aware recommender systems (CARS) are a vital module of many corporate, especially within the online commerce domain, where consumers are provided with recommendations about products potentially relevant for them. A traditional CARS, which utilizes deep learning models considers that user's preferences can be predicted by ratings, reviews, demographics, etc. However, the feedback given by the users is often conflicting when comparing the rating score and the sentiment behind the reviews. Therefore, a model that utilizes either ratings or reviews for predicting items for top-N recommendation may generate unsatisfactory recommendations in many cases. In order to address this problem, this paper proposes an effective context-specific sentiment based stacked autoencoder (CSSAE) to learn the concrete preference of the user by merging the rating and reviews for a context-specific item into a stacked autoencoder. Hence, the user's preferences are consistently predicted to enhance the Top-N recommendation quality, by adapting the recommended list to the exact context where an active user is operating. Experiments on four Amazon (5-core) datasets demonstrated that the proposed CSSAE model persistently outperforms state-of-the-art top-N recommendation methods on various effectiveness metrics.

关键词： Context-aware recommender systems Item splitting Sentiment analysis stacked autoencoder

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A Data-Driven Health Monitoring Method Using Multiobjective Optimization and stacked autoencoder Based Health Indicator

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第9期17卷 6379-6389页

作者： Chen, Zhiwen Guo, Rongjie Lin, Zhi Peng, Tao Peng, Xia Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China

This article proposes a new data-driven health monitoring method, which uses multiobjective optimization and stacked autoencoder based health indicator. Specifically, the proposed method proposes an improved nondominated sorting genetic algorithm-II (NSGA-II) to perform multiobjective optimization on a large number of candidate features extracted from the sensor measurements. Then, a stacked autoencoder model is used to construct health indicators from the selected features. In the improved NSGA-II algorithm, the optimization goals of feature selection are defined as the minimum gap of health indicators between different states and the number of features. Comparisons between the proposed method and the state-of-the-art methods on simulation experiments show that the proposed method can accurately identify the status of the equipment and effectively limit the complexity of the diagnostic model.

关键词： Optimization Feature extraction Indexes Monitoring Sorting Sociology Statistics Data driven health monitoring multiobjective optimization stacked autoencoder

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Quantum stacked autoencoder fault diagnosis model for bearing faults

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INSIGHT 2023年第11期65卷 631-638页

作者： Yu, Tianyi Li, Shunming Lu, Jiantao Nanjing Univ Aeronaut & Astronaut NUAA Coll Energy & Power Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing Peoples R China Nantong Inst Technol Sch Automot Engn Nantong 226002 Peoples R China

The use of neural network models to monitor bearing vibration signals can easily be affected by noise, which leads to a decrease in the model test accuracy. Therefore, the existence of noise problems increases the requirements for non-linear mapping capability and robustness of deep neural network (DNN) models. In order to deal with the noise problem, the concept of qubit neurons is introduced into a deep learning stacked autoencoder (SAE) model, and a quantum stacked autoencoder (QSAE) model based on qubits and quantum gates is proposed. The properties of SAE layer-by-layer coding and the arithmetic of qubit neurons are combined in the QSAE. The quantum state signal is taken as the input signal to the encoder and the coding activation function and coding weight matrix are redefined by quantum-controlled non-gates and quantum revolving gates, so that the quantum state signal can be coded layer by layer. Experimental results show that the QSAE can train and diagnose noisy experimental data and maintain high test accuracy in an anti-attack test. This shows that the QSAE has non-linear mapping capability and robustness.

关键词： stacked autoencoder quantum neural network non-linear mapping robustness bearing fault

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