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第41届中国控制会议

作者： Jianjun Yang Xiaolu Xu Xiaohui Yin Wenxing Liu Yi Yuan Jian Peng School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Huaxin Cement Co. Ltd

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Soft sensing mainly studies the real-time prediction of some key performance indicators or quality variables in the actual production process,which has the role of guiding production in the actual production *** stacked autoencoder is a multi-layer autoencoder *** input variables will be encoded and decoded through each layer of autoencoders,and the obtained hidden features will be retained as the input of the next *** this way,high-level data features can be successfully learned from the input layer to the intermediate *** isomorphic autoencoder reconstruct an identical data input layer,and the reconstruction is performed by minimizing the error of the decoded data from the original input ***,for a soft measurement model,some data information may also reduce the accuracy or generalization of the *** order to overcome the above shortcomings,this paper proposes a gated stacked isomorphic autoencoder,which evaluates the contribution of each hidden layer feature through the gating unit,and then integrates the information of different hidden layers to complete the prediction and estimation of related main ***,the effectiveness and feasibility of the method are verified in practical industrial cases.

关键词： Deep learning soft sensor stacked autoencoder gating unit

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UNSUPERVISED stacked CAPSULE autoencoder FOR HYPERSPECTRAL IMAGE CLASSIFICATION

UNSUPERVISED STACKED CAPSULE AUTOENCODER FOR HYPERSPECTRAL I...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Pan, Erting Ma, Yong Mei, Xiaoguang Fan, Fan Ma, Jiayi Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ Inst Aerosp Sci & Technol Wuhan 430072 Peoples R China

ISBN: (纸本)9781728176055

Since CapsNet [1] shattered all previous records of algorithms for image recognition, the capsule's conception has attracted bright attention. It interprets an object by the geometrical arrangement of parts. We think it can be transferred to hyperspectral images. In a hyperspectral data cube, each pixel spectrum can be regarded as a continuous curve representing its inherent properties. In the spatial domain, there are various spatial distributions in different positionsand there is usually a specific structural relationship between adjacently distributed categories. Based on HSI data's aforementioned structural characteristics, combined with the stacked capsule autoencoder, we propose our model to achieve an unsupervised HSI classification. In our model, the ConvLSTM is employed to discover part capsules of HSI, and we utilize Set Transformer to encode relations among all parts and indicate object capsules. The decoders of both phases use Gaussian mixture models to reconstruct specific information. Experimental results of the Pavia Center dataset show the exceptional of our model.

关键词： hypersepctral classification unsupervised learning capsule network stacked autoencoder

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A novel soft sensor method based on stacked fusion autoencoder with feature enhancement for industrial application

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MEASUREMENT 2023年 221卷

作者： Wang, Wenhua Wang, Hengqian Chen, Lei Hao, Kuangrong Donghua Univ Engn Res Ctr Digitized Text & Apparel Technol Minist Educ Shanghai 201620 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

Soft sensor is pivotal in contemporary industrial processes. However, extracting effective feature representa-tions from intricate process data remains a challenging task. To this end, a feature enhancement stacked fusion autoencoder (FE-SFAE) is proposed to leverage spatiotemporal characteristics and linear residual fusion for modeling. Firstly, the feature enhancement (FE) module that incorporates multidirectional delayed transform (MDT) and bilateral smoothing constraint canonical polyadic (BS-CP) decomposition is developed to alleviate the issues of time-lag and spatial dependence. Based on the FE module, complementary data can be generated for the subsequent stage. Secondly, to address the problem of variables being overly non-linearized, we propose a linear residual fusion gate mechanism. On this basis, we design the model named FE-SFAE that not only captures spatiotemporal features in complementary data but also balances the degree of network nonlinearization. Finally, the superiority of FE-SFAE is demonstrated through an industrial case involving the esterification process of polyester polymerization.

关键词： Deep learning Soft sensor CP decomposition stacked autoencoder Polyester polymerization process

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Towards Control of EEG-based Robotic Arm using Deep Learning via stacked Sparse autoencoder

Towards Control of EEG-based Robotic Arm using Deep Learning...

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IEEE International Conference on Robotics and Biomimetics

作者： Oluwagbenga Paul Idowu Peng Fang Xiangxin Li Zeyang Xia Jing Xiong Guanglin Li Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (纸本)9781728103785;9781728103778

Recently, an EEG-based robotic arm control has offered a key solution to the problem of high level amputation or severe neuromuscular damage. Several attempt to use EMG-based pattern recognition (PR) failed due to insufficient myoelectric signals from the residual limb to perform control functions. The EEG activity recorded from human scalp is used to control the movement of a robotic arm. This can either be achieved when the arm is attached to or separated from the amputee stump by interfacing the brain directly to the robotic arm through the brain-machine interface (BMI). To build an intelligent robotic (or prosthetics) system that will manipulate object seamlessly with multiple degrees of freedom (DoF), it is required that a robust learning algorithm which is able to control the prosthetic arm while interacting with the environment should be implemented. However, the conventional machine learning approach of using handcrafted features to design a robot controller that can perform multiple task is not a feasible option. Hence, we propose a robust learning control which is based on unsupervised learning algorithm of deep autoencoder. We applied stacked autoencoder to generate our features, and softmax layer is then used to classify five different motor imagery tasks. The proposed method produced an overall accuracy of 98.9% across the four amputees recruited for the experiments. Our algorithm shows a better performance when compared with the state-of-the art classifiers. Thus, the proposed results demonstrates the possibility of providing better control performance for EEG-based prosthesis.

关键词： Human robot interaction Artificial intelligence in robotics Brain machine interface EEG-based robotic arm Deep learning stacked autoencoder

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Global dynamic features and information of adjacent hidden layer enhancement based on autoencoder for industrial process soft sensor application

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第5期103卷 2218-2233页

作者： Guo, Xiaoping Pan, Sulei Li, Yuan Shenyang Univ Chem Technol Coll Informat Engn Shenyang Liaoning Peoples R China

There is a lack of consideration of temporal and spatial correlation in the process variables and adjacent hidden layers correlation in the soft sensor model of stacked autoencoders. To address the issue, a novel global dynamic adjacent layer information enhancement auto encoder (GD-ALIEAE) method is proposed to improve the poor prediction performance. The gated recurrent unit (GRU) and uniform manifold approximation and projection (UMAP) are applied to the GD-ALIEAE model for obtaining global dynamic features of the temporal and spatial information of process variables by parallel computation. An adjacent layer information correlation algorithm is proposed to avoid the loss of hidden layers information during the stacking process. The algorithm enhances the features of the low layer through nonlinear mapping, combining the low layer and its adjacent layer as input. The input then is fed to the multi-head attention mechanism to obtain features that contain adjacent layer correlation. Finally, a prediction model is established through a fully connected layer. Through simulation experiments on two industrial cases of sulphur recovery unit and thermal power plant, and compared with models of stacked autoencoder (SAE), stacked isomorphic autoencoder (SIAE), and target-related stacked autoencoder (TSAE), the effectiveness of the proposed method was verified.

关键词： global dynamic feature soft sensor stacked autoencoder UMAP

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Analog Circuit Fault Diagnosis Model Based on WOA and Improved SDAE

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IEICE ELECTRONICS EXPRESS 2025年第1期22卷 20240633-20240633页

作者： Lu, Xinmiao Zou, Yixin Wu, Qiong Yang, Longyue Zhu, Yuna Harbin Univ Sci & Technol Sch Measurement Control Technol & Commun Engn Harbin 150000 Peoples R China Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150000 Peoples R China Heilongjiang Network Space Res Ctr Harbin 150000 Peoples R China

With the advancement of integrated circuit technology, the stable operation of electronic devices is crucial. Targeting the issue that traditional analog circuit fault diagnosis models cannot simultaneously satisfy noise resistance, stability, and accuracy in real circuit environments, this research represents an analog circuit fault diagnosis model relied on the whale optimization algorithm and an improved SDAE. The model transfor ms fault signals into 2D time-frequency representations using VMD and CWT to achieve initial denoising;Utilizing DSDAE for further denoising and dimensionality reduction of feature vectors;finally, RF is used for classification. The results of the simulation demonstrate that even in noisy conditions, the model can maintain excellent diagnostic accuracy and stability. making certain improvements in enhancing the operational reliability of electronic devices.

关键词： Variational Mode Decomposition Continuous Wavelet Trans for m stacked autoencoder Whale Optimization Algorithm

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Nonlinear dynamic process monitoring based on latent mapping embedding deep neural networks

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第4期103卷 1802-1812页

作者： Yu, Zhenhua Wang, Wenjing Wang, Xueting Jiang, Qingchao Wang, Guan East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol State Key Lab Bioreactor Engn Shanghai 200237 Peoples R China

In industrial processes, complex nonlinearity and dynamics generally exist, making it challenging to achieve good results using conventional process monitoring methods. In this paper, a latent mapping embedding neural network method (LMNN) is proposed for efficient monitoring of nonlinear dynamic processes. First, a deep neural network (DNN) is employed to acquire features of state variables from nonlinear process data and expand them along with the input to a new feature subspace. Second, a latent mapping (LM) method is used to map the high-dimensional feature subspace to a low-dimensional subspace that includes the most beneficial time series information. Then the entire neural network and regression parameters are obtained through an end-to-end learning manner, through which the nonlinearity and process dynamics are well characterized. Subsequently, prediction error-based residual is generated and the monitoring model is established. The performance of the proposed method is verified through a simulation of penicillin production process and an actual fermentation process of penicillin. Comparisons with state-of-the-art methods are carried out, and results validate the effectiveness and superiority of the proposed method.

关键词： date-driven process monitoring latent mapping nonlinear system stacked autoencoder

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Automatic Detection of COVID-19 Using a stacked Denoising Convolutional autoencoder

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Computers, Materials & Continua 2021年第12期69卷 3259-3274页

作者： Habib Dhahri Besma Rabhi Slaheddine Chelbi Omar Almutiry Awais Mahmood Adel M.Alimi College of Applied Computer Sciences(CACS) King Saud UniversityRiyadh11543Saudi Arabia Faculty of Sciences and Technology of Sidi Bouzid University of KairouanKairouanTunisia National School of Engineers(ENIS) University of SfaxTunisia Department of Computer Sciences College of Sciences and Arts in UnaizahQassim UniversityAl-QassimKingdom of Saudi Arabia

The exponential increase in new coronavirus disease 2019(COVID-19)cases and deaths has made COVID-19 the leading cause of death in many ***,in this study,we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images.A stacked denoising convolutional autoencoder(SDCA)model was proposed to classify X-ray images into three classes:normal,pneumonia,and *** SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy *** proposed model’s architecture mainly composed of eight autoencoders,which were fed to two dense layers and SoftMax *** proposed model was evaluated with 6356 images from the datasets from different *** experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting,*** metrics used for the SDCA model were the classification accuracy,precision,sensitivity,and specificity for both *** results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%.Therefore,this model can help physicians accelerate COVID-19 diagnosis.

关键词： stacked autoencoder augmentation multiclassification COVID-19 convolutional neural network

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Revolutionizing emergency vehicle response: a smart traffic management approach with adaptive CNN and hybrid deep learning

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1793-1804页

作者： Vikraman, Bindu Puthentharayil Mahadevan, Vanitha Jabbar, Rani Fathima University of Technology and Applied Sciences-Al Mussanah Al Mussanah Oman Bahrin Polytechnic Isha town Bahrain

This study explores the important problem of urban traffic congestion, focusing on the challenges that emergency vehicles encounter. Existing traffic management systems frequently fail to efficiently prioritize emergency vehicles, relying on costly sensors or manual intervention. To solve these challenges, this research presents a unique traffic control technique that detects and prioritizes emergency vehicles using images captured by traffic surveillance *** approach involves denoising input traffic images and recognizing vehicles using Adaptive Faster R-CNN (AfR-CNN), which has been optimized with the Enhanced walrus optimization (EWO) algorithm. In addition, a hybrid deep learning model is used to identify emergency vehicles, with stacked autoencoder (SAE) for feature extraction and modified DenseNet201 for classification. The evaluation metrics utilized for comparing with other deep learning techniques include accuracy, precision, recall, and the F-measure. Test results indicate that the proposed emergency vehicle detection model surpasses alternative deep learning algorithms in vehicle classification accuracy, achieving a remarkable accuracy. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Adaptive faster R-CNN Enhanced walrus optimization Modified denseNet201 stacked autoencoder Traffic surveillance

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stacked Denoising Extreme Learning Machine autoencoder Based on Graph Embedding for Feature Representation

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IEEE ACCESS 2019年 7卷 13433-13444页

作者： Ge, Hongwei Sun, Weiting Zhao, Mingde Yao, Yao Dalian Univ Technol Coll Comp Sci & Technol Dalian 116023 Peoples R China McGill Univ Sch Comp Sci Montreal PQ H3A 0E9 Canada

Extreme learning machine is characterized by less training parameters, fast training speed, and strong generalization ability. It has been applied to obtain feature representations from the complex data in the tasks of data clustering or classification. In this paper, a graph embedding-based denoising extreme learning machine autoencoder (GDELM-AE) is proposed for capturing the structure of the inputs. Specifically, in GDELM-AE, a graph embedding framework that contains an intrinsic graph and a penalty graph constructed by local Fisher discrimination analysis is integrated into the autoencoder. So, it can exploit both local structure and global structure information in extreme learning machine (ELM) spaces. Further, we propose a stacked graph embedded denoising (SGD)-ELM by stacking several GDELM-AEs. The experimental results on several benchmarks validate that GDELM-AE can obtain efficient and robust feature representation of original data;moreover, the stacked GDELM-AE can obtain high-level and noise-robust representations. The comparative results with the state-of-the-art algorithms indicate that the proposed algorithm can obtain better accuracy as well as faster training speed.

关键词： Extreme learning machine stacked autoencoder denoising graph embedding

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