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Boosted Stacking Ensemble Machine Learning Method for Wafer Map pattern classification

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Computers, Materials & Continua 2023年第2期74卷 2945-2966页

作者： Jeonghoon Choi Dongjun Suh Marc-Oliver Otto Department of Convergence and Fusion System Engineering Kyungpook National UniversitySangju37224Korea Department of Mathematics Natural and Economic SciencesUlm University of Applied SciencesUlm89075Germany

Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.

关键词： Wafer map pattern classification machine learning boosted stacking ensemble semiconductor manufacturing processing

Bridging the Traditional Chinese Medicine pattern classification and Biomedical Disease Diagnosis with Systems Biology

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Chinese Journal of Integrative Medicine 2012年第12期18卷 883-890页

作者：吕爱平卞兆祥陈可冀 School of Chinese Medicine Hong Kong Baptist University China Academy of Chinese Medical Sciences

Being the unique core of traditional Chinese medicine （TCM）, pattern classification exerts a direct effect on the efficacy and safety of herbal interventions. In this article, the authors integrated the pattern classification and disease diagnosis with many approaches from systems biology, integration of pattern classification with biomedical diagnosis by systems biology is not only a new direction of personalized medicine development, but also provides a new drug development model. In the further study, the pattern classifications of major diseases will be the focus of research.

关键词： traditional Chinese medicine pattern classification systems biology biomedical diagnosis

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Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience

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Journal of Central South University 2015年第5期22卷 1801-1808页

作者： SHIM Hyeon-min LEE Sangmin Institute for Information and Electronic Research Inha University 100 Inharo Incheon 402-751 Korea Department of Electronic Engineering Inha University 100 Inharo Incheon 402-751 Korea

An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.

关键词： electromyography(EMG) pattern classification feature extraction deep learning deep belief network(DBN)

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pattern classification in Chinese Medicine

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Pattern Classification in Chinese Medicine

Binary Image Steganalysis Based on Symmetrical Local Residual patterns

第十八届中国科协年会——分16针灸大科学研究学术高...

作者： Aiping LU School of Chinese Medicine Hong Kong Baptist University

pattern classification, the core theory in Chinese medicine clinical practice, will be a complementary method to classify individual?s status and contribute to personalized medicine. The author provided a logical argument focuses on the innovation that may result from integrated pattern classification and biomedical diagnosis, and introduced some novel research design and methods of pattern classification researches using systemic biological approaches and evidence-based medicine design, as well as developed novel approaches on the methodology of clinical effectiveness evaluation on Chinese medicine and new drug research and development.

关键词： pattern classification Chinese medicine system biology evidence-based medicine personalized medicine

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Chinese Journal of Electronics 2022年第4期31卷 752-763页

作者： LUO Junwei YU Mujian YIN Xiaolin LU Wei School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University

Residual computation is an effective method for gray-scale image steganalysis. For binary images, the residual computation calculated by the XOR operation is also employed in the local residual patterns(LRP) model for steganalysis. A binary image steganalytic scheme based on symmetrical local residual patterns(SLRP) is proposed. The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set. Multi-scale windows are utilized to construct three SLRP submodels which are further merged to construct the final features set instead of a single model.SLRPs with higher probability to be modified after embedding are emphasized and selected to construct the feature sets for training the support vector machine classifier. The experimental results show that the proposed steganalytic scheme is effective for detecting binary image steganography.

关键词： feature extraction symmetrical local residual patterns image coding probability Data security steganography residual computation Other topics in statistics matrix algebra Computer vision and image processing techniques pattern classification gray-scale image steganalysis local residual patterns model Image and video coding binary image steganalytic scheme support vector machines symmetrical relationships binary images binary image steganalysis Cryptography Optical, image and video signal processing binary image steganography

A Fine-Grained Flash-Memory Fragment Recognition Approach for Low-Level Data Recovery

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Chinese Journal of Electronics 2022年第4期31卷 732-740页

作者： ZHANG Li HAO Shengang ZHANG Quanxin Department of Media Engineering Zhejiang University of Media and Communication School of Computer Science Beijing Institute of Technology

Data recovery from flash memory in the mobile device can effectively reduce the loss caused by data corruption. Type recognition of data fragment is an essential prerequisite to the low-level data recovery. Previous works in this field classify data fragment based on its file type. Still, the classification efficiency is low, especially when the data fragment is a part of a composite file.We propose a fine-grained approach to classifying data fragment from the low-level flash memory to improve the classification accuracy and efficiency. The proposed method redefines flash-memory-page data recognition problem based on the encoding format of the data segment, and applies a hybrid machine learning algorithm to detect the data type of the flash page. The hybrid algorithm can significantly decompose the given data space and reduce the cost of training. The experimental results show that our method achieves better classification accuracy and higher time performance than the existing methods.

关键词： data handling flash memories File organisation type recognition flash page fine-grained flash-memory fragment recognition approach fine-grained approach Semiconductor storage Other topics in statistics classifying data fragment Computer vision and image processing techniques low-level data recovery data corruption pattern classification given data space low-level flash memory classification accuracy learning (artificial intelligence) storage management data segment Memory circuits Codes Data handling techniques method redefines flash-memory-page data recognition problem error correction codes

Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity:A Survey

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 377-391页

作者： Jun Zhang Lei Pan Qing-Long Han Chao Chen Sheng Wen Yang Xiang IEEE the School of Science Computing and Engineering TechnologiesSwinburne University of Technology the School of Information Technology Deakin University the College of Science and Engineering James CookUniversity

With the booming of cyber attacks and cyber criminals against cyber-physical systems(CPSs),detecting these attacks remains challenging.It might be the worst of times,but it might be the best of times because of opportunities brought by machine learning(ML),in particular deep learning(DL).In general,DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data.DL models are adopted quickly to cyber attacks against CPS systems.In this survey,a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context.A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems.The methodology includes CPS scenario analysis,cyber attack identification,ML problem formulation,DL model customization,data acquisition for training,and performance evaluation.The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules.Moreover,excellent performance is achieved partly because of several highquality datasets that are readily available for public use.Furthermore,challenges,opportunities,and research trends are pointed out for future research.

关键词： Cyber-physical system cybersecurity deep learning intrusion detection pattern classification

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A structural developmental neural network with information saturation for continual unsupervised learning

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 780-795页

作者： Zhiyong Ding Haibin Xie Peng Li Xin Xu College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina

In this paper,we propose a structural developmental neural network to address the plasticity‐stability dilemma,computational inefficiency,and lack of prior knowledge in continual unsupervised learning.This model uses competitive learning rules and dynamic neurons with information saturation to achieve parameter adjustment and adaptive structure development.Dynamic neurons adjust the information saturation after winning the competition and use this parameter to modulate the neuron parameter adjustment and the division timing.By dividing to generate new neurons,the network not only keeps sensitive to novel features but also can subdivide classes learnt repeatedly.The dynamic neurons with information saturation and division mechanism can simulate the long short‐term memory of the human brain,which enables the network to continually learn new samples while maintaining the previous learning results.The parent‐child relationship between neurons arising from neuronal division enables the network to simulate the human cognitive process that gradually refines the perception of objects.By setting the clustering layer parameter,users can choose the desired degree of class subdivision.Experimental results on artificial and real‐world datasets demonstrate that the proposed model is feasible for unsupervised learning tasks in instance increment and class incre-ment scenarios and outperforms prior structural developmental neural networks.

关键词： neural network pattern classification unsupervised learning

Self‐training maximum classifier discrepancy for EEG emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1480-1491页

作者： Xu Zhang Dengbing Huang Hanyu Li Youjia Zhang Ying Xia Jinzhuo Liu School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismChongqingChina School of Electrical and Computer Engineering Inha UniversityIncheonSouth Korea School of Software Yunnan UniversityYunnanChina

Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling.Recent study proposed to solve the limited label problem via domain adaptation methods.However,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this study.The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'outputs.Besides,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed.Finally,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is constructed.Extensive experiments on SEED and SEED‐IV are conducted.The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

关键词： artificial intelligence bioinformatics domain adaptation EEG neural network pattern classification