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23rd European Signal Processing Conference (EUSIPCO)

作者： Tamura, Satoshi Uno, Takuya Takehara, Masanori Hayamizu, Satoru Kurata, Takeshi Gifu Univ Dept Informat Sci Gifu Japan AIST Ctr Serv Res Tsukuba Ibaraki Japan

ISBN: (纸本)9780992862633

In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoustic information is one of useful and key modalities;particularly environmental or background sounds include effective cues. This paper focuses on two aspects: (1) extracting powerful and robust acoustic features by using stacked-denoising-autoencoder and hag-of-feature techniques, and (2) investigating a multi-modal SOE scheme by combining the audio features and the other sensor data as well as non-sensor information. We conducted evaluation experiments using multi-modal data recorded in a restaurant. We improved SOE performance in comparison to conventional acoustic features, and effectiveness of our multi modal SOE scheme is also clarified.

关键词： Service operation estimation multi-modal signal processing stacked denoising autoencoder bag of features environmental sounds

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Tensor-Train Based Deep Learning Approach for Compressive Sensing in Mobile Computing 15

Tensor-Train Based Deep Learning Approach for Compressive Se...

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15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Zou, Cong Yang, Fang Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China Tsinghua Univ Shenzhen Res Inst Key Lab Digital TV Syst Guangdong Prov & Shenzhen Shenzhen 518057 Peoples R China

ISBN: (纸本)9781538677476

Several recent studies have been conducted on solving the compressive sensing problem with deep learning framework, which enhance the signal recovery performance and greatly shorten the running time compared with traditional algorithms. However, as the size of signals increases, so does the neural network, which will impose large memory space and high computational complexity, making it hard to use this method on mobile devices. To address this issue, in this paper, the neural network is decomposed by Tensor-Train (TT) format, which reduces the number of parameters to a great extent. In particular, the neural network decomposed by TT format is a stacked denoising autoencoder (SDA) network, which called TT-SDA. The experiments demonstrate that, especially with low measurement rates, the proposed TT-SDA network can improve the reconstruction results, reduce the computational complexity and save the memory space.

关键词： Compressive sensing deep learning Tensor-Train stacked denoising autoencoder

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Machine Learning for Data Reduction in Quantum State Tomography 37

Machine Learning for Data Reduction in Quantum State Tomogra...

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37th Chinese Control Conference (CCC)

作者： Liu, Ximin Lu, Sicong Wu, Rebing Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9789881563958

The purpose of quantum state tomography (QST) is to obtain a complete quantum state by reconstructing a density matrix from experimental data and therefore gives researchers a powerful tool to analyze complex synthetic quantum systems. In the past decades, many methods have been developed to improve the efficiency of quantum state tomography (QST), such as old methods: compress sensing on almost pure state and low-rank systems, new method: using restricted Boltzmann machine on systems of quantum many-body. However, they are both limited to some certain systems. In this paper, we introduce an universal method using deep neural networks based on stacked denoising autoencoder, which probably can help finish QST on all types of systems with a much smaller number of measurements and therefore is very efficient.

关键词： quantum state tomography deep neural networks stacked denoising autoencoder

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A Novel Approach for Trajectory Feature Representation and Anomalous Trajectory Detection 18

A Novel Approach for Trajectory Feature Representation and A...

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18th International Conference on Information Fusion (Fusion)

作者： Feng, Wenhui Han, Chongzhao Xi An Jiao Tong Univ Sch Elect & Informat Engn MOE KLINNS Lab Inst Integrated Automat Xian 710049 Peoples R China

ISBN: (纸本)9780982443866

Trajectories obtained from low level tracking algorithm provide an opportunity for us to analyze meaningful behaviors and monitor adverse or malicious events. How to abstract meaningful features from the raw data of trajectories is a challenge due to the high dimensionality and noise. In this paper, a novel approach, stacked denoising autoencoder(SDA) is applied to address this problem. This method can reduce the dimensionality of the trajectories significantly, so that they can be handled easily. More importantly, the denoising process of the SDA can capture the structure of the raw data, so the features they producing generalize well for detecting anomalous trajectories. The results of the numerical experiments prove the validity of the proposed approach.

关键词： anomalous trajectory detection feature representation stacked denoising autoencoder

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PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions

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BMC BIOINFORMATICS 2021年第SUPPL 3期22卷 415-415页

作者： Zhou, Haoran Wekesa, Jael Sanyanda Luan, Yushi Meng, Jun Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Bioengn Dalian 116024 Liaoning Peoples R China

Background Plant long non-coding RNAs (lncRNAs) play vital roles in many biological processes mainly through interactions with RNA-binding protein (RBP). To understand the function of lncRNAs, a fundamental method is to identify which types of proteins interact with the lncRNAs. However, the models or rules of interactions are a major challenge when calculating and estimating the types of RBP. Results In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence and structural information, named PRPI-SC. PRPI-SC predicts interactions between lncRNAs and proteins based on the k-mer features of RNAs and proteins. Experiments proved good results on Arabidopsis thaliana and Zea mays datasets (ATH948 and ZEA22133). The accuracy rates of ATH948 and ZEA22133 datasets were 88.9% and 82.6%, respectively. PRPI-SC also performed well on some public RNA protein interaction datasets. Conclusions PRPI-SC accurately predicts the interaction between plant lncRNA and protein, which plays a guiding role in studying the function and expression of plant lncRNA. At the same time, PRPI-SC has a strong generalization ability and good prediction effect for non-plant data.

关键词： lncRNA-protein k-Mer stacked denoising autoencoder Convolutional neural network

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A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging

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FOOD CHEMISTRY 2023年第1期409卷 135251页

作者： Zhou, Xin Zhao, Chunjiang Sun, Jun Cao, Yan Yao, Kunshan Xu, Min Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China Natl Engn Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Natl Engn Lab Agriprod Qual Traceabil Beijing 100097 Peoples R China

The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing al-gorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decom-position layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.

关键词： stacked denoising autoencoder Wavelet transform Heavy metal lead Oilseed rape Fluorescence hyperspectral imaging Nondestructive testing

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Inter-subject cognitive workload estimation based on a cascade ensemble of multilayer autoencoders

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 211卷

作者： Zheng, Zhanpeng Yin, Zhong Wang, Yongxiong Zhang, Jianhua Univ Shanghai Sci & Technol Engn Res Ctr Opt Instrument & Syst Shanghai Key Lab Modern Opt Syst Minist Educ Jungong Rd 516 Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Jungong Rd 516 Shanghai 200093 Peoples R China Oslo Metropolitan Univ Dept Comp Sci OsloMet Artificial Intelligence Lab N-0130 Oslo Norway

Machine learning approaches can build a computational model to predict cognitive workload levels by using electroencephalogram (EEG) feature inputs at the same time instant. However, when the EEG signals are recorded on different people, the accuracy of such a model could be impaired due to its incapability of fitting varied statistical distributions across different individuals. To this end, we propose an individual-independent workload estimator, a cascade ensemble of multilayer autoencoders to tackle the individual difference within the EEG features. It could assess the workload levels of an unseen subject by adapting the EEG data recorded from non-overlapped existing subjects. We first construct a deep stacked denoising autoencoder to abstract EEG features from a specific individual. Its shallow weights are optimized with individual-specific geometrical information of the features. Then, to find generalizable feature properties, we introduce Q-statistics to measure the independence between base learners. Finally, a regularized extreme learning machine is used as a cascade metaclassifier to fuse and filter high-level EEG abstractions and determine workload levels. We employ databases from two different experiments to validate our approach. The proposed framework can lead to acceptable accuracy and computational complexity compared to several existing workload classifiers.

关键词： Cognitive workload stacked denoising autoencoder Extreme learning machine Electroencephalogram Abstraction fusion

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FEATURE EXTRACTION OF MOTOR IMAGERY EEG SIGNALS BASED ON MULTI-SCALERECURRENCEPLOT AND SDA

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2021年第1期36卷

作者： Wang, Wenbo Sun, Lin Chen, Guici Wuhan Univ Sci & Technol Sch Sci Wuhan Peoples R China Wuchang Univ Technol Coll Gen Educ Wuhan Peoples R China Wuhan Univ Technol Hubei Key Lab Transportat Internet Things Wuhan Peoples R China Natl Engn Res Ctr Water Transport Safety Wuhan Peoples R China

When recognizing multi-class motor imagery electoencephalogram (EEG) signals directly using stacked denoising autoencoders (SDA), it is difficult to fully train the weights due to the small sample size, which results in poor classification effect. To overcome this problem, the multi-scale recurrence plot and SDA method are combined to extract features of multi-class motor imagery EEG signals for recognition. Firstly, multi-class motor imagery EEG signals are decomposed into a series of intrinsic mode functions (IMFs) with different scale by synchrosqueezed wavelet transform, and the recurrence plot of each IMF is constructed to form one-level feature data as input samples of SDA. Then, high-level abstract features which can better express category attributes are extracted from multi-scale recurrence plot by SDA. Finally, EEG signals are classified by using Softmax classifier. Four types of motor imagery EEG data of Datasets 2a in BCI Competition IV are used to verify the proposed method. The average classification accuracy is 0.89, which shows that the proposed method has good effectiveness and robustness.

关键词： Motor imagery EEG feature extraction synchrosqueezed wavelet transform recurrence plot stacked denoising autoencoder

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An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion

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FUEL 2022年 308卷 121975-121975页

作者： Han, Zhezhe Li, Jian Hossain, Md Moinul Qi, Qi Zhang, Biao Xu, Chuanlong Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China Univ Kent Sch Engn Canterbury CT2 7NT Kent England

Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization control and environmental protection. This study proposes a novel ensemble deep learning model for exhaust emissions (NOx and CO2) prediction. In this ensemble learning model, the stacked denoising autoencoder is established to extract the deep features of flame images. Four forecasting engines include artificial neural network, extreme learning machine, support vector machine and least squares support vector machine are employed for preliminary prediction of NOx and CO2 emissions based on the extracted image deep features. After that, these preliminary predictions are combined by Gaussian process regression in a nonlinear manner to achieve a final prediction of the emissions. The effectiveness of the proposed ensemble deep learning model is evaluated through 4.2 MW heavy oil-fired boiler flame images. Experimental results suggest that the predictions are achieved from the four forecasting engines are inconsistent, however, an accurate prediction accuracy has been achieved through the proposed model. The proposed ensemble deep learning model not only provides accurate point prediction but also generates satisfactory confidence interval.

关键词： Emission prediction Flame image Ensemble method stacked denoising autoencoder Gaussian process regression

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Fast probability estimation of HVDC successive commutation failure caused by AC grid cascading failures

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 135卷 107618-107618页

作者： Zhu, Yuanzhen Liu, Tianhao Li, Changgang Liu, Yutian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control 17923 Jingshi Rd Jinan 250061 Peoples R China

For line-commutated converter (LCC) based HVDC system, successive commutation failure (SCF) can result in continual cessations of power transmission or even forced blocking of the DC system. AC grid cascading failure near inverters is an important reason for the SCF. The SCF probability is utilized to represent the impact of AC grid cascading failures on DC systems. This paper proposes a fast probability estimation method of SCF based on sequential importance sampling (SIS) and stacked denoising autoencoder (SDAE). Firstly, the probability estimation of SCF caused by cascading failures is transformed into a statistical analysis problem. Secondly, considering AC line failures have different impacts on DC system, a cascading failure sampling method based on SIS is presented to efficiently generate cascading failure samples and evaluate the SCF probability. Thirdly, SDAE based assessment network is established to quickly assess the impact of AC failures on SCF. Topology features related to AC grid structure and short-circuit fault locations are selected as inputs of the assessment network. Finally, real-life hybrid AC/DC grids are adopted to demonstrate the validity of the proposed method.

关键词： Cascading failure HVDC Sequential importance sampling stacked denoising autoencoder Successive commutation failure

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