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Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology the Center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the department of Electronic and information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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Active traveling wave vibration control of elastic supported conical shells with smart micro fiber composites based on the differential quadrature method

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Applied Mathematics and Mechanics(English Edition) 2025年第2期46卷 305-322页

作者： Yuxin HAO Lei SUN Wei ZHANG Han LI College of Mechanical Engineering Beijing Information Science and Technology University department of Mechanics Guangxi University

This paper investigates the active traveling wave vibration control of an elastic supported rotating porous aluminium conical shell (CS) under impact *** smart materials in the form of micro fiber composites (MFCs) are used as actuators and sensors. To this end, a metal pore truncated CS with MFCs attached to its surface is considered. Adding artificial virtual springs at two edges of the truncated CS achieves various elastic supported boundaries by changing the spring stiffness. Based on the first-order shear deformation theory (FSDT), minimum energy principle, and artificial virtual spring technology, the theoretical formulations considering the electromechanical coupling are derived. The comparison of the natural frequency of the present results with the natural frequencies reported in previous literature evaluates the accuracy of the present approach. To study the vibration control, the integral quadrature method in conjunction with the differential quadrature approximation in the length direction is used to discretize the partial differential dynamical system to form a set of ordinary differential equations. With the aid of the velocity negative feedback method, both the time history and the input control voltage on the actuator are demonstrated to present the effects of velocity feedback gain, pore distribution type, semi-vertex angle, impact loading, and rotational angular velocity on the traveling wave vibration control.

关键词： rotating conical shell (CS) porous metal material active vibration control elastic support differential quadrature method TB123

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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Computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Multi-party quantum private comparison of size relationship based on one-direction quantum walks on a circle

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Science China(information Sciences) 2025年第2期68卷 399-400页

作者： Jintao WANG Jiangyuan LIAN Tianyu YE College of information & Electronic Engineering Zhejiang Gongshang University

Quantum walks(QW) are the quantum counterpart of classical random walks, and portray a natural stochastic process when the walker randomly wanders around. In a discretetime QW system on a circle, when the walker particle generally steps towards two directions, i.e., clockwise and anticlockwise, this kind of QW is called two-direction quantum walks on a circle(TDQWC); and the walker particle always steps towards one direction or stays stationary, this kind of QW is called as one-direction quantum walks on a circle(ODQWC). ODQWC exhibits some different properties,and may induce potential applications in the field of quantum secure multi-party computation.

关键词： Quantum entanglement

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea department of Computer Science and information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia department of Computer Engineering and information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

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Buried interface bridging for inverted cesium-formamidinium triiodide perovskite solar cells with long operational stability

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Science China Chemistry 2025年第1期68卷 163-173页

作者： Chenhui Zhang Chunjun Liang Fulin Sun Ting Zhu Xinghai Huang Yuzhu Guo Xinyu Guo Kunyang Ge Dan Li Fangtian You Zhiqun He Key Laboratory of Luminescence and Optical information Ministry of EducationInstitute of Optoelectronic TechnologyBeijing Jiaotong UniversityBeijing 100044China department of Physics School of Physical Science and EngineeringBeijing Jiaotong UniversityBeijing 100044China

In the field of perovskite solar cells(PSCs),the research on defects in the buried interface has been relatively limited due to its non-exposure;however,this interface significantly impacts the performance enhancement of inverted *** study employs phenylethylammonium chloride(PEACl)molecules as a buffer layer to modify the buried interface of p-i-n structured PSCs,aiming to enhance the uniformity of self-assembled monolayers(SAMs)and facilitate the uniform nucleation and growth of perovskite films on the ***,the introduction of the PEACl buffer layer effectively passivates defects at the bottom of the perovskite layer and notably enhances the crystal quality of the perovskite film by mitigating residual stress,thereby reducing nonradiative recombination *** these optimizations,the MA-free PSCs treated with PEACl achieve a power conversion efficiency(PCE)of 24.11%,with significant improvements in storage,thermal stability,and operational *** noteworthy is the device's performance in an unencapsulated state,whereas after 1,500 hours of continuous light operation stability testing,it retains 97%of its original *** study not only enriches the systematic understanding of the characteristics of the buried interface in PSCs but also contributes significantly to advancing the commercial production of perovskite photovoltaic technology.

关键词： defect-induced recombination buried interfaces inverted perovskite solar cells crystallization lattice mismatch

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Distributed stochastic model predictive control for energy dispatch with distributionally robust optimization

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Applied Mathematics and Mechanics(English Edition) 2025年第2期46卷 323-340页

作者： Mengting LIN Bin LI C.CECATI School of Aeronautics and Astronautics Sichuan University department of information Engineering Computer Science and MathematicsUniversity of L'Aquila

A chance-constrained energy dispatch model based on the distributed stochastic model predictive control (DSMPC) approach for an islanded multi-microgrid system is proposed. An ambiguity set considering the inherent uncertainties of renewable energy sources (RESs) is constructed without requiring the full distribution knowledge of the uncertainties. The power balance chance constraint is reformulated within the framework of the distributionally robust optimization (DRO) approach. With the exchange of information and energy flow, each microgrid can achieve its local supply-demand ***, the closed-loop stability and recursive feasibility of the proposed algorithm are proved. The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.

关键词： distributed stochastic model predictive control (DSMPC) distributionally robust optimization (DRO) islanded multi-microgrid energy dispatch strategy TM73

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Dynamic Process Monitoring Based on Dot Product Feature Analysis for Thermal Power Plants

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 563-574页

作者： Xin Ma Tao Chen Youqing Wang the College of information Science and Technology Beijing University of Chemical Technology the department of Chemical and Process Engineering University of Surrey IEEE

Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems, such as thermal power plants being studied in this work. Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms. Mainstream dynamic algorithms rely on concatenating current measurement with past data. This work proposes a new, alternative dynamic process monitoring algorithm, using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples, thus naturally capturing the process dynamics through temporal correlation. At the same time, DPFA's online computational complexity is lower than not just existing dynamic algorithms, but also classical static algorithms(e.g., principal component analysis and slow feature analysis). The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias, process fault and gain change fault. Through experiments with a numerical example and real data from a thermal power plant, the DPFA algorithm is shown to be superior to the state-of-the-art methods, in terms of better monitoring performance(fault detection rate and false alarm rate) and lower computational complexity.

关键词： Computational complexity dot product feature analysis (DPFA) dynamic process multivariate statistics process monitoring

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Strong Structural Controllability of Multi-Agent Systems with Uncertain Connection Topologies

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Journal of Systems Science & Complexity 2025年

作者： CHEN Qitian LI Shaoyuan department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and information Processing Ministry of Education of China

This paper investigates the strong structural controllability(SSC) of multi-agent systems(MASs) defined over Laplacian dynamics on directed graphs. The agents that are divided into leaders and followers are connected based on the consensus law and only leaders are manipulated by the external control input directly. In contrast to existing work, the topology of MAS contains uncertain interconnection edges between agents. The interconnection graph has a zero/nonzero/arbitrary structure, to handle the parameters uncertainty problem in MASs. Under this framework, the authors propose a color-changing rule based on the zero-forcing set(ZFS). A graph-theoretic sufficient condition of SSC is proved. Next, the authors investigate the leader selection problem to ensure the SSC of MASs. A greedy algorithm based on ZFS is introduced. In addition, the authors figure out that the redundant property of edges in MASs can help us decide the leader selection problem. A new heuristic algorithm of polynomial complexity is developed to select minimum leaders of the multi-agent ***, the authors support the proposed analysis with numerical results on various simulations.

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