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Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

IEEE ACCESS 2023年 11卷 129709-129721页

作者： Ning, Shangfeng Wang, Wenhong Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China

Hyperspectral band selection plays a key role for overcoming the curse of dimensionality in the classification of hyperspectral remote sensing images (HSIs). Recently, clustering-based band selection methods have demonstrated great potential to select informative and representative bands for hyperspectral classification tasks. However, most clustering-based methods perform clustering directly on the original high-dimensional data, which reduces their performance. To address this problem, a novel band selection method called global-local consistency constrained deep embedded clustering (GLC-DEC) is proposed in this paper. In GLC-DEC, to simultaneously learn the low-dimensional embedded representation and cluster assignments of all bands in an HSI, the stacked autoencoder is integrated with the K-means method. In addition, to reduce the adverse impact of a limited number of training samples available in HSIs, local and global consistency constraints are imposed on the embedded representation so that discriminatively consistent representation of all bands is learned. Specifically, local graph regularization and global graph regularization are introduced into the GLC-DEC model, by which the strong correlation between neighboring bands and the manifold structure of all bands are fully exploited. Based on the clustering results provided by GLC-DEC, a group of representative bands are selected by using the minimum noise method. Experimental results on two real datasets demonstrate that the proposed GLC-DEC outperformed several state-of-the-art methods.

关键词： Hyperspectral band selection deep embedded clustering stacked autoencoder representation learning graph regularization

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Efficient adaptive deep gradient RBF network for multi-output nonlinear and nonstationary industrial processes

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JOURNAL OF PROCESS CONTROL 2023年第1期126卷 1-11页

作者： Liu, Tong Chen, Sheng Yang, Po Zhu, Yunpeng Harris, Chris J. Imperial Coll London Dept Chem Engn London SW7 2AZ England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England Ocean Univ China Fac Informat Sci & Engn Qingdao Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP England Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England

Due to the complexity of process operation, industrial process data are often nonlinear and non -stationary, high dimensional, and multivariate with complex interactions between multiple outputs. To address all these issues, this paper proposes a novel industrial predictive model that integrates deep feature extraction and fast online adaptation, and can effectively deal with multiple process outputs. Specifically, a multi-output gradient radial basis function network (MGRBF) with excellent predictive capacity of nonstationary data is first used to provide preliminary prediction of target outputs. This prior quality information is combined with the original process input for deep feature learning and dimensional reduction. Through layer-wise feature extraction by the stacked autoencoder (SAE), deep quality-enhanced features can be obtained, which is further fed into a MGRBF tracker for online prediction. In order to timely capture the fast-changing process characteristics, the first two modules, namely, preliminary MGRBF predictor and SAE feature extractor are frozen after training, while the structure and parameters of the MGRBF tracker are updated online in an efficient manner. Two industrial case studies demonstrate that the proposed adaptive deep MGRBF network outperforms existing state-of-the-art online modeling approaches as well as deep learning models, in terms of both multi-output modeling accuracy and online computational complexity.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Multivariate nonlinear and nonstationary industrial process Multi-output gradient radial basis function network stacked autoencoder Quality-enhanced feature extraction Online adaptive tracking

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Neuron-Compressed Deep Neural Network and Its Application in Industrial Anomaly Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第7期19卷 7914-7924页

作者： Wang, Kai Yan, Caoyin Mo, Yanfang Yuan, Xiaofeng Wang, Yalin Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China City Univ Hong Kong Hong Kong Inst Data Sci Kowloon Hong Kong 999077 Peoples R China

Data modeling and online monitoring are two critical stages for data-driven anomaly detection. Regarding data modeling, deep neural networks (DNNs) can learn good decision boundaries to separate the anomaly and normal regions, due to their flexible model structures and excellent fitting ability. However, DNNs, using nonlinear activations with specific boundaries, may indirectly cause a limited anomaly detection margin, especially when there are samples far from centroids. Moreover, an anomaly detection model with a narrow detection margin is deemed insensitive to general faults. An anomaly detection model with a tight detection margin will suffer a severe performance degradation. To mitigate the intrinsic drawbacks of DNNs, we develop a new regularizer based on the maximum likelihood of complete data (i.e., observations and latent variables). The regularizer is neuronwise and mathematically acts as compressing neurons, dragging the marginal points into the centroids. Combining the regularizer with the encoding-decoding structure networks, we perform an industrial case study to verify the superiority of the proposed method.

关键词： Anomaly detection deep neural network (DNN) regularization stacked autoencoder

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Reducing smearing effect in contribution plots and improving fault detection via polynomial approximated isomap embeddings

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第5期101卷 2768-2789页

作者： Alakent, Burak Bogazici Univ Dept Chem Engn TR-34342 Istanbul Turkey

Dimension reduction is an essential method used in multivariate statistical process monitoring for fault detection and diagnosis. Principal component analysis (PCA) and independent component analysis (ICA) are the most frequently used linear dimensional reduction tools, and the contribution plot is the most popular fault isolation method in the absence of any prior information on the faults. These methods, however, come with their shortcomings. The fault detection capability of linear methods may not be sufficient for non-linear processes, and smearing effect is known to deteriorate the diagnostics obtained from contribution plots. While the fault detection rate may be increased by kernelized methods or deep artificial neural network models, tuning data-dependent hyperparameter(s) and network structure with limited historical data is not an easy task. Furthermore, the resulting non-linear models often do not directly possess fault isolation capability. In the current study, we aim to devise a novel method named ICA(pIso)-PCA, which offers non-linear fault detection and isolation in a rather straightforward manner. The rationale of ICA(pIso)-PCA mainly involves building a non-linear scores matrix, composed of principal component scores and high-order polynomial approximated isomap embeddings, followed by implementation of the ICA-PCA algorithm on this matrix. Applications on a toy dataset and the Tennessee Eastman plant show that the I-2 index from ICA(pIso)-PCA yields a high fault detection rate and offers accurate contribution plots with diminished smearing effects compared to those from traditional monitoring methods. Easy implementation and the potential for future research are further advantages of the proposed method.

关键词： fault isolation independent component analysis manifold learning stacked autoencoder

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Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull's Nest

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IEEE ACCESS 2023年 11卷 37194-37202页

作者： Nhidi, Wiem Ben Aoun, Najib Ejbali, Ridha Univ Gabes Natl Engn Sch Gabes ENIG Res Team Intelligent Machines RTIM Gabes 6029 Tunisia Al Baha Univ Coll Comp Sci & Informat Technol Al Baha 657797738 Saudi Arabia Univ Sfax Natl Sch Engineers Sfax ENIS REGIM Lab Res Grp Intelligent Machines Sfax 3038 Tunisia

Intraspecific nest parasitism is a phenomenon that attracts the attention of biologists since it helps in saving the endangered species such as Slender Billed Gull. The problem comes from the fact that a parasite female lays its eggs in the nest of another female (host) of the same species which causes the abandon of the nest by the host. This behavior causes a significant reduction in future birds number and leads to the expansion of this specie. Thus, there has been an urgent necessity to clean the nest from parasitic eggs. So, our aim is to build an automatic parasitic egg identification system based on egg visual features information. Our system uses deep learning models which have proven their success for image classification. Indeed, our system conduct an egg image's pre-processing phase followed by Fast Beta Wavelet Network (FBWN) to extract the most efficient descriptors (shape, texture, and color). Then, these features will be inputted to the stacked autoencoder for egg classification. Our proposed system, has been evaluated on 91-egg dataset collected from 31 clutches of eggs in Sfax region, Tunisia. Our model has given a parasitic egg identification accuracy of 89.9% which has outperformed the state-of-the-art method and shows the efficiency and the robustness of our system.

关键词： Feature extraction Image color analysis Visualization Birds Deep learning Support vector machines Intraspecific nest parasitism slender-billed gull parasitic egg identification fast beta wavelet network stacked autoencoder deep learning

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CapsNet-LDA: predicting IncRNA-disease associations using attention mechanism and capsule network based on multi -view data

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BRIEFINGS IN BIOINFORMATICS 2023年第1期24卷 bbac531-bbac531页

作者： Zhang, Zequn Xu, Junlin Wu, Yanan Liu, Niannian Wang, Yinglong Liang, Ying Jiangxi Agr Univ Coll Comp & Informat Engn Nanchang Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha Peoples R China

Cumulative studies have shown that many long non -coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential IncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low -dimensional representations of the IncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi -view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

关键词： multi-view stacked autoencoder attention mechanism capsule network lncRNA-disease association prediction

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CSO-CNN: circulatory system optimization-based cascade region CNN for fault estimation and driver behavior detection

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第6期17卷 3063-3071页

作者： Priyadharshini, G. Ukrit, M. Ferni SRM Inst Sci & Technol Sch Comp Dept Computat Intelligence Kattankulathur India

The evolution of new trends in the automobile industry is creating more comfortable and convenient means of transportation. But still there exist manifold challenges in detecting legal and illegal drivers;therefore, evaluating the behaviors of drivers needs to be addressed. Taking these into consideration his paper proposes a novel cascade region convolutional neural network-based circulatory system optimization algorithm (CRCNN++ based CSO) to attain optimal multitask framework which includes behavior evaluation, identity authentication of drivers, vehicle diagnosis as well as estimating the fault of the vehicle. In this paper, two diverse naturalistic driving behavior public datasets namely HCRL and UAH drive datasets are collected and pre-processed via normalization as well as scaling process. The preprocessed feature is then extracted and the dimensions are minimized using the stacked autoencoder technique. The CRCNN++-based CSO is employed in determining to multitask which includes identity authentication, behavioral evaluation, vehicle diagnosis, and faults estimation is performed. Finally, the efficiency of the proposed CRCNN++-based CSO method is analyzed by evaluating various metrics namely receiver operating characteristic curve, accuracy, false positive rate, precision, Cohen Kappa score, true positive rate, and F1-Score. The comparative analysis is carried out for various existing techniques and the proposed approach. From the evaluation results, it is revealed that the proposed CRCNN++-based CSO approach delivers better performance in driver identification through driving style behavior.

关键词： Cascade region Convolutional neural network stacked autoencoder Datasets Driver Authentication

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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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Micro-crack detection method of steel beam surface using stacked autoencoders on massive full-scale sensing strains

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2020年第4期19卷 1175-1187页

作者： Song, Qingsong Chen, Yu Oskoui, Elias Abdoli Fang, Zheng Taylor, Todd Tang, Guangwu Zhao, Xiangmo Ansari, Farhad Changan Univ Sch Informat Engn Xian Peoples R China Univ Illinois Dept Civil & Mat Engn 842 W Taylor St Chicago IL 60607 USA China Merchants Chongqing Commun Technol Res & De State Key Lab Bridge Engn Struct Dynam Chongqing Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China

Accurate micro-crack detections on the whole surface of civil structures have great significance. Distributed optical fiber sensor based on Brillouin optical time-domain analysis technology exhibits great facility to measure strain distributions along the whole surface of structures with a high spatial resolution, thus providing a potential and competitive solution to the detection problem. However, mainly due to low signal-to-noise ratio in measurements, such sensor system is still limited in crack detection-based structural health monitoring applications. How to extract high-quality micro-crack feature representations from the low signal-to-noise ratio-distributed strain measurements is crucial to solve the problem. It has been demonstrated in field of pattern recognition that deep learning can automatically extract high-quality noise-robust feature representations from mass chaos data. Therefore, a micro-crack detection method is proposed herein based on deep learning to analyze the full-scale strain measurements. Each measurement is normalized and segmented into a set of equal-length subsequences. autoencoders, a typical kind of building block of deep neural network, are stacked layer-wise into a deep network and then exploited to automatically extract feature representations from the subsequences. Each extracted feature representation is labeled as one of the two categories by a Softmax regression. One category originates in the subsequences acquired from structure sections with crack defects and another from sections without any cracks. The micro-crack detections are achieved by solving such a crack/non-crack binary classification problem. A 15-m-long steel I-beam with artifact crack defects is built up in laboratory to verify the proposed method. Experimental results demonstrate that the minimum size of detectable crack opening width reaches to 23 mu m, and besides, the proposed method is significantly better than traditional Fisher linear discriminant analys

关键词： Structural health monitoring crack detection pattern recognition deep learning feature representation stacked autoencoder Brillouin optical time-domain analyzer

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UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

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Journal of Systems Engineering and Electronics 2023年第5期34卷 1235-1251页

作者： XIE Lei TANG Shangqin WEI Zhenglei XUAN Yongbo WANG Xiaofei Institute of Aeronautics Engineering Air Force Engineering UniversityXi’an 710038China China Aerodynamics Research&Development Center Mianyang 621000China Blue Sky Innovation Center for Frontier Science Beijing 100000China

The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of *** overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is *** clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid *** evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is *** convolutional pooling layer is used to compress the size of data and preserve the distribution *** LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization *** experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient *** with the five kinds of assess-ments models,the SAE-LVQ model has the highest ***,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation *** assessment results are in line with the actual situation.

关键词： unmanned combat aerial vehicle(UCAV) situation assessment clustering K-means stacked autoencoder learn-ing vector quantization

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