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Passenger flow prediction and management method of urban public transport based on SDAE model and improved Bi-LSTM neural network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 10563-10577页

作者： Xian, Luo Tian, Lan Leshan Vocat & Tech Coll Dept Elect & Informat Engn Leshan 610400 Sichuan Peoples R China Changchun Univ Technol Coll Art & Design Changchun Jilin Peoples R China

In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction model combining stacked denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field.

关键词： Intelligent transportation system passenger flow prediction stacked denoising autoencoder bidirectional long short-term memory network attention mechanism introduction

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Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

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COMPUTERS IN BIOLOGY AND MEDICINE 2019年 109卷 159-170页

作者： Yang, Shuo Yin, Zhong Wang, Yagang Zhang, Wei Wang, Yongxiong Zhang, Jianhua Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Shanghai Key Lab Modern Opt Syst Minist Educ Engn Res Ctr Opt Instrument & Syst Shanghai 200093 Peoples R China Oslo Metropolitan Univ Dept Comp Sci OsloMet Artificial Intelligence Lab N-0130 Oslo Norway

To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subject specific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

关键词： Mental workload Human-machine system Electroencephalogram stacked denoising autoencoder Deep learning

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LDICDL: LncRNA-Disease Association Identification Based on Collaborative Deep Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第3期19卷 1715-1723页

作者： Lan, Wei Lai, Dehuan Chen, Qingfeng Wu, Ximin Chen, Baoshan Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Guangxi Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techn Nanning 530004 Guangxi Peoples R China Guangxi Univ State Key Lab Conservat & Utilizat Subtrop Agrobi Nanning 530004 Guangxi Peoples R China Cent South Univ Sch Comp Sci & Engn Hunan Prov Key Lab Bioinformat Changsha 410083 Hunan Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia

It has been proved that long noncoding RNA (lncRNA) plays critical roles in many human diseases. Therefore, inferring associations between lncRNAs and diseases can contribute to disease diagnosis, prognosis and treatment. To overcome the limitation of traditional experimental methods such as expensive and time-consuming, several computational methods have been proposed to predict lncRNA-disease associations by fusing different biological data. However, the prediction performance of lncRNA-disease associations identification needs to be improved. In this study, we propose a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning. It uses an automatic encoder to denoise multiple lncRNA feature information and multiple disease feature information, respectively. Then, the matrix decomposition algorithm is employed to predict the potential lncRNA-disease associations. In addition, to overcome the limitation of matrix decomposition, the hybrid model is developed to predict associations between new lncRNA (or disease) and diseases (or lncRNA). The ten-fold cross validation and de novo test are applied to evaluate the performance of method. The experimental results show LDICDL outperforms than other state-of-the-art methods in prediction performance.

关键词： Diseases Matrix decomposition RNA Prediction algorithms Noise reduction Biological system modeling Computational modeling lncRNA-disease associations matrix factorization stacked denoising autoencoder

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A Sample-Efficient OPF Learning Method Based on Annealing Knowledge Distillation

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IEEE ACCESS 2022年 10卷 99724-99733页

作者： Dong, Ziheng Hou, Kai Liu, Zeyu Yu, Xiaodan Jia, Hongjie Zhang, Chi Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China State Grid Jiangsu Elect Power Co Ltd Nanjing 211102 Peoples R China Extra High Voltage Branch Co Nanjing 211102 Peoples R China

To quickly respond to variations in the state of network load demand, a solution using data-driven techniques to predict optimal power flow (OPF) has emerged in recent years. However, most of the existing methods are highly dependent on large data volumes. This limits their application on the newly established or expanded systems. In this regard, this work proposes a sample-efficient OPF learning method to maximize the utilization of limited samples. By decomposing the OPF task before knowledge distillation, deep learning complexity is reduced. Thereafter, knowledge distillation is used to integrate decoupled tasks and improve accuracy in low-data setups. Unsupervised pre-training is introduced to alleviate the demand for labeled data. Additionally, the focal loss function and teacher annealing strategy are adopted to achieve higher accuracy without extra samples. Numerical tests on different systems corroborate the advanced accuracy and training speed over other training methods, especially on fewer-sample occasions.

关键词： Encoding Knowledge engineering Load modeling Deep learning Data models Power generation Annealing Noise reduction Optimal control Annealing Optimal power flow sample efficiency annealing knowledge distillation focal loss function stacked denoising autoencoder deep learning

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EAF-SR: an enhanced autoencoder framework for social recommendation

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第10期82卷 14837-14858页

作者： Liu, Taiheng He, Zhaoshui Shenzhen Univ Coll Mechatron & Control Engn Guangdong Key Lab Electromagnet Control & Intelli Shenzhen 518060 Peoples R China Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen 518060 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Minist Educ Key Lab IoT Intelligent Informat Proc & Syst Inte Guangzhou 510006 Peoples R China Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Peoples R China Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Peoples R China

In the recommendation systems, the user feedback data (e.g., user-item rating and the social information) is usually sparse, discrete and full of noise. However, most of existing methods use it to make recommendations directly, which leads to the reduction of their recommendation accuracy and quality ultimately. To address this problem, this paper proposes an Enhanced autoencoder Framework with knowledge distillation (EAF-SR) for learning robust information from user feedback data, where it consists of three parts: Pre-training, knowledge distillation layers and Re-training. Specifically, Pre-training is proposed to generate the soft targets by using the stacked denoising autoencoder (SDAE) from user feedbacks and trust information, which aims to reduce data noise. Then, knowledge distillation layers are developed to learn the robust information from the generated soft targets. Finally, Re-training network combined with Pre-training is designed to make recommendations for each user. Extensive experiments done on three real-world datasets (e.g., Flixster, Epinions and Douban) show that the proposed EAF-SR is superior to the state-of-the-art approaches.

关键词： Deep learning Recommender system stacked denoising autoencoder Knowledge distillation Social recommendation

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Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2016年第Jun.期116卷 24-41页

作者： Zhang, Puzhao Gong, Maoguo Su, Linzhi Liu, Jia Li, Zhizhou Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Res Ctr Intelligent Percept & Computat Xian 710071 Shaanxi Provinc Peoples R China

Multi-spatial-resolution change detection is a newly proposed issue and it is of great significance in remote sensing, environmental and land use monitoring, etc. Though multi-spatial-resolution image pair are two kinds of representations of the same reality, they are often incommensurable superficially due to their different modalities and properties. In this paper, we present a novel multi-spatial resolution change detection framework, which incorporates deep-architecture-based unsupervised feature learning and mapping-based feature change analysis. Firstly, we transform multi-resolution image-pair into the same pixel-resolution through co-registration, followed by details recovery, which is designed to remedy the spatial details lost in the registration. Secondly, the denoising autoencoder is stacked to learn local and high-level representation/feature from the local neighborhood of the given pixel, in an unsupervised fashion. Thirdly, motivated by the fact that multi-resolution image-pair share the same reality in the unchanged regions, we try to explore the inner relationships between them by building a mapping neural network. And it can be used to learn a mapping function based on the most-unlikely-changed feature-pairs, which are selected from all the feature-pairs via a coarse initial change map generated in advance. The learned mapping function can bridge the different representations and highlight changes. Finally, we can build a robust and contractive change map through feature similarity analysis, and the change detection result is obtained through the segmentation of the final change map. Experiments are carried out on four real datasets, and the results confirmed the effectiveness and superiority of the proposed method. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Change detection Spatial-resolution denoising autoencoder stacked denoising autoencoder Deep neural networks Feature transformation

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Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第6期235卷 1106-1114页

作者： Che, Changchang Wang, Huawei Fu, Qiang Ni, Xiaomei Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Peoples R China

Accurate fault prediction of rolling bearing can predict the operation condition in advance, which is an important means to ensure the safety and reliability of rotating machinery. Aimed at the data processing of rolling bearing vibration signal with multi-fault and long time series, an intelligent fault prediction model based on gate recurrent unit and hybrid autoencoder is proposed in this paper. Firstly, vibration signals of multi-faults are brought into multi-layer gate recurrent unit model for multi-step and multi-variable time series prediction. Secondly, variational autoencoder is used for data augmentation of fault samples. Thirdly, the augmented fault samples are brought into stacked denoising autoencoder for noise reduction and fault prediction. Finally, fault prediction results of rolling bearing can be achieved on the basis of gate recurrent unit and hybrid autoencoder of variational autoencoder and stacked denoising autoencoder. The bearing datasets of Case Western Reserve University are used to verify the effectiveness of the proposed method. Comparative experiment results show that the proposed fault prediction model has more accurate time series prediction result and higher fault prediction accuracy than other deep learning models. With 98.68% accuracy of fault prediction, the proposed fault prediction model can be taken as an effective tool for intelligent predictive maintenance of rolling bearing.

关键词： Rolling bearing fault prediction gate recurrent unit variational autoencoder stacked denoising autoencoder

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A deep autoencoder feature learning method for process pattern recognition

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JOURNAL OF PROCESS CONTROL 2019年 79卷 1-15页

作者： Yu, Jianbo Zheng, Xiaoyun Wang, Shijin Tongji Univ Sch Mech Engn Shanghai 200082 Peoples R China Tongji Univ Sch Econ & Management Shanghai 200082 Peoples R China

Recognition of various defect patterns exhibited in discrete manufacturing processes can significantly reduce the diagnostic processes, and increase manufacturing process stability and quality. Thus the effective recognizers are in great demand to improve the performance of process pattern recognition (PPR). Deep learning has been widely applied in image and visual analysis with great successes. However, the application of deep learning in feature learning for process control is still few. This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for PPR in manufacturing processes. This paper will concentrate on developing an SDAE model to learn effective features from the process signals and then implementing an effective PPR through a deep network architecture. Feature visualization is also performed to explicitly present the feature representation of the proposed SDAE model. The effectiveness of the proposed PPR method is verified through a big simulation dataset and Tennessee Eastman process. The result shows that the proposed method obtains good feature learning and PPR performance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Manufacturing process Process pattern recognition Deep learning stacked denoising autoencoder Feature learning

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A Deep End-to-End Model for Transient Stability Assessment With PMU Data

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IEEE ACCESS 2018年 6卷 65474-65487页

作者： Zhu, Qiaomu Chen, Jinfu Zhu, Lin Shi, Dongyuan Bai, Xiang Duan, Xianzhong Liu, Yilu Huazhong Univ Sci & Technol Sch Elect & Elect Engn Elect Power Secur & High Efficiency Key Lab State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China

Accurate transient stability assessment (TSA) is a fundamental requirement for ensuring secure and stable operation of power systems. Tremendous efforts have been made to apply artificial intelligence approaches for TSA with phasor measurement unit data. However, many previous approaches may be failed to provide favorable accuracy due to the shallow architectures and error-prone hand-crafting features. This paper proposed a model for TSA, which is termed multi-branch stacked denoising autoencoder (MSDAE). This model is a unified framework integrating multiple stacked denoising autoencoders (SDAEs), one fusion layer, and one logistic regression (LR) layer. Initially, the SDAEs at the bottom of MSDAE extract features from multiple kinds of measurements respectively. Then, the extracted features are encoded into unified fusion features by the fusion layer. Finally, the LR layer performs TSA by using the fusion features. The depth of the architecture contributes to the remarkable ability for feature learning, while the width of the architecture (i.e., the multiple branches) enables MSDAE to deal with different kinds of measurements by a reasonable mechanism. In this way, MSDAE achieves feature extraction and classification intrinsically and simultaneously, namely, achieves TSA in an end-to-end manner. The results of experiments on IEEE 50-machine system demonstrate the superiority of the proposed model over the prior methods.

关键词： Deep learning feature extraction PMU data stacked denoising autoencoder transient stability assessment

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A Deep Learning Model for Robust Wafer Fault Monitoring With Sensor Measurement Noise

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2017年第1期30卷 23-31页

作者： Lee, Hoyeop Kim, Youngju Kim, Chang Ouk Yonsei Univ Dept Informat & Ind Engn Seoul 03722 South Korea

Standard fault detection and classification (FDC) models detect wafer faults by extracting features useful for fault detection from time-indexed measurements of the equipment recorded by in situ sensors (sensor signals) and feeding the extracted information into a classifier. However, the preprocessing-and-classification approach often results in the loss of information in the sensor signals that is important for detecting wafer faults. Furthermore, the sensor signals usually contain noise induced by mechanical and electrical disturbances. In this paper, we propose the use of a stacked denoising auto-encoder (SdA), which is a deep learning algorithm, to establish an FDC model for simultaneous feature extraction and classification. The SdA model can identify global and invariant features in the sensor signals for fault monitoring and is robust against measurement noise. Through experiments using wafer samples collected from a work-site photolithography tool, we confirmed that as the sensor measurement noise severity increased, the SdA's classification accuracy could be as much as 14% higher than those of the twelve models considered for comparison, each of which employed one of three feature extractors and one of four classifiers.

关键词： Semiconductor manufacturing fault detection and classification sensor measurement noise deep learning stacked denoising autoencoder

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