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Paper Defects Classification Based on VGG16 and Transfer Learning

Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2021年第2期53卷 5-14页

作者： Yun-Hui, Qu Tang, Wei Feng, Bo Computer Teaching and Research Section Xi’an Medical University Xi’an Shaanxi710021 China Department of Electric and Control Engineering Shaanxi University of Science & Technology Xi’an Shaanxi710021 China Department of Electric and Control Engineering Shaanxi University of Science & Technology Xi’an Shaanxi710021 China

There are some problems in traditional paper defects classification, such as the poor generalization performance, less types of recognition, and insufficient recognition accuracy. The deep learning method provides a new scheme for paper defects classification. However, due to the small sample size of paper defect images set, the over fitting phenomenon is easy to appear in the training process. Aiming this problem, a transfer learning method based on convolutional neural network model is ***, freezing the first seven construction layers of VGG16 network which has been trained by ImageNet, and fine tune the rest convolution layers with the paper defect images set to complete the feature extraction;Secondly, the full connection layers for classification are improved to meet the needs of paper defects classification;Finally, transfer learning strategy is adopted in the training process to improve the efficiency. The experimental results demonstrate that the paper defects classification proposed in our approach can improve the efficiency and accuracy of paper defects recognition. The approach will beneficial for the web inspection process. © 2021 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Efficiency

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SIFT-based Approach for Dorsal Hand Veins Images Recognition 26

SIFT-based Approach for Dorsal Hand Veins Images Recognition

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26th International Conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Egorov, Dobroslav P. Mizinov, Pavel V. Kravchenko, Oleg V. Konnova, Natalia S. Osipenko, Gavriil V. Kotel'nikov Institute of Radio Engineering and Electronics of Ras Moscow Russia Bauman Moscow State Technical University Department of Information Security Moscow Russia Computer Science and Control of Ras Federal Research Center Moscow Russia Moscow Aviation Institute Moscow Russia

ISBN: (纸本)9798350361728

In this paper we present a system for person identification using dorsal hand veins pattern images. A simple approach is developed based on scale-invariant feature transform (SIFT) method for image features extraction, fast approximate nearest neighbors algorithm (FLANN) for keypoints matching in a preselected region-of-interest (ROI), and subsequent utilization of dense layer neural network for accurate friend-or-foe predictions. The efficiency of the considered approach is evaluated using an open dataset, and confusion matrices are calculated. The obtained results confirm again that dorsal hand veins identification systems are particularly promising for high-security settings. The proposed approach is implemented in software for Orange pi single-board computers. © 2024 IEEE.

关键词： Nearest neighbor search

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Frequency Domain Auto-tuning of Structured LPV controllers for High-Precision Motion control

Frequency Domain Auto-tuning of Structured LPV Controllers f...

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IEEE Conference on Decision and control

作者： Yorick Broens Hans Butler Roland Tóth Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Systems and Control Laboratory HUNREN Institute for Computer Science and Control Hungary

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype.

关键词： Technological innovation Sensitivity Frequency-domain analysis Stability criteria Prototypes Throughput Frequency response Motion control Optimization MIMO communication

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Enhancing Transformer Tracking Using NF-ResNet and ResNeXT Backbones 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Verma, Sourabh Singla, Rajesh Verma, Om Prakash Sharma, Richa Gupta, Himanshu Sharma, Tarun Muthanna, Ammar Department of Instrumentation and Control Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Computer Science—Internet of Things ABES Institute of Technology Ghaziabad India University Meerut India Saint-Petersburg State University of Telecommunications Saint Petersburg Russia

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Residual networks play a foremost role in the domain of tracking, specifically in the extraction of features. The residual networks are using a simple technique of skipping connections to overcome the problem of vanishing gradient. Also, it is using batch normalization after each layer of convolution to accelerate the training by reducing the dependency over the parameters. Though, we have tried to solve these issues by introducing ResNeXt and NF-ResNet one by one into the TransT tracker as feature extractors in the place of ResNet. Finally, we present a ResNeXt-based TransT tracker (named as TransT_NeXt) and NF-Net-based TransT tracker (named as TransT_NFNet). We have evaluated proposed trackers on three large-scale benchmark datasets and four small-scale benchmark datasets but unfortunately, our trackers haven’t performed well. At last, we have discussed about the future work on the basis of this study. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Large datasets

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A New LMI-Based Non-Iterative Design Method for Static Output Feedback controllers for Linear Systems 25

A New LMI-Based Non-Iterative Design Method for Static Outpu...

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25th International Conference on Mechatronics Technology, ICMT 2022

作者： Oya, Hidetoshi Hayakawa, Satoshi Nagai, Shunya Nakano, Kazushi Tokyo City University Department of Computer Science Tokyo Japan Tokyo City University Graduate School of Integrative Science and Engineering Tokyo Japan Department of Electronics and Control Engineering Ehime Japan The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (数字)9781665461955

ISBN: (纸本)9781665461955

This paper presents a new non-iterative LMI-based design method for static output feedback controllers for linear systems. The proposed design method is derived from the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems. In this paper, we show the new non-iterative LMI-based design procedure for static output feedback controllers for linear systems, and the effectiveness of the proposed controller design approach is shown through a simple numerical example. © 2022 IEEE.

关键词： Linear systems

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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ENABLING HIGH SENSITIVITY AIRFLOW MEASUREMENT USING PHASE-CHANGE MATERIALS 26

ENABLING HIGH SENSITIVITY AIRFLOW MEASUREMENT USING PHASE-CH...

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26th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2022

作者： Zhou, Yushan Cao, Yunqi Sepúlveda, Nelson Zhang, Hongjian State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9781733419048

A lab-on-a-chip (LOC) thermal mass flow sensor based on microelectromechanical systems (MEMS) technology is designed, fabricated, and characterized. Vanadium dioxide (VO2), a nonlinear phase-change material with 3-order of change in resistance is placed in the down streaming direction of the heat source as the sensing element. The resistance of VO2 is monitored under gas flow rates ranging from 0 to 37.8 μL·s-1 with a maximum sensitivity of 368.56 ppm/(μL·min-1). The studies presented in this research may enable the application of utilizing nonlinear metamaterial in microfluidic LOC sensors with orders of magnitude improvement in sensitivity. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Phase change materials

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Automating the Generation of Functional Stress Inducing Stimuli for Burn-In Testing

Automating the Generation of Functional Stress Inducing Stim...

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IEEE European Test Symposium (ETS)

作者： Nikolaos I. Deligiannis Tobias Faller Zhou Chenghan Riccardo Cantoro Bernd Becker Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Department of Computer Science University of Freiburg Freiburg Germany

In the domain of high reliability applications, Burn-In testing (BI) is always present since it is one of the prime countermeasures against the infant mortality phenomenon. Traditional static BI testing proves to be inefficient for modern circuit designs. As the devices’ feature size scales down and their structural and architectural complexity increases, so does the complexity and cost of the BI test. Different BI methods are employed by the industry where stimuli are also applied to the devices under test (DUTs) in order to effectively stress and stimulate all nets of the design. One known industry practice resorts to Design for Testability (DfT) infrastructures (e.g., scan) and is based on the application of test vectors at low frequency to excite the DUT as much as possible with the goal of switching each net of the design at least once. In this paper we consider the case where the layout of the circuit is known and propose two novel methods able to automatically produce functional stimuli to switch pairs of neighboring nodes (i.e., nodes that are placed within a specified distance in the DUT) in short periods of time. This solution has been shown to be able to trigger some latent defects in a circuit better than other methods. As a case study, we target functional units within a RISC-V processor (RI5CY). We show that the functional stimuli generated by the exact method described in the paper are able to achieve optimal results (i.e., the maximum functional switching of neighboring pairs), thus maximizing the chance that their at-speed application can activate weak points in the circuit.

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On generalization bounds for projective clustering 23

On generalization bounds for projective clustering

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Maria Sofia Bucarelli Matilde Fjeldsø Larsen Chris Schwiegelshohn Mads Bech Toftrup Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Department of Computer Science Aarhus University Denmark

Given a set of points, clustering consists of finding a partition of a point set into k clusters such that the center to which a point is assigned is as close as possible. Most commonly, centers are points themselves, which leads to the famous k-median and k-means objectives. One may also choose centers to be j dimensional subspaces, which gives rise to subspace clustering. In this paper, we consider learning bounds for these problems. That is, given a set of n samples P drawn independently from some unknown, but fixed distribution Ɗ, how quickly does a solution computed on P converge to the optimal clustering of Ɗ? We give several near optimal results. In particular,1. For center-based objectives, we show a convergence rate of Õ(√k/n). This matches the known optimal bounds of [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] and [Bartlett, Linder, and Lugosi, IEEE Trans. Inf. Theory 1998] for k-means and extends it to other important objectives such as k-median.2. For subspace clustering with j -dimensional subspaces, we show a convergence rate of Õ(√kj2/n). These are the first provable bounds for most of these problems. For the specific case of projective clustering, which generalizes k-means, we show a convergence rate of Ω(√kj/n) is necessary, thereby proving that the bounds from [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] are essentially optimal.

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On Generalization Bounds for Projective Clustering

arXiv

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arXiv 2023年

作者： Bucarelli, Maria Sofia Larsen, Matilde Fjeldsø Schwiegelshohn, Chris Toftrup, Mads Bech Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Department of Computer Science Aarhus University Denmark

Given a set of points, clustering consists of finding a partition of a point set into k clusters such that the center to which a point is assigned is as close as possible. Most commonly, centers are points themselves, which leads to the famous k-median and k-means objectives. One may also choose centers to be j dimensional subspaces, which gives rise to subspace clustering. In this paper, we consider learning bounds for these problems. That is, given a set of n samples P drawn independently from some unknown, but fixed distribution D, how quickly does a solution computed on P converge to the optimal clustering of D? We give several near optimal results. In particular, 1. For center-based objectives, we show a convergence rate of Õ (√k/n ) . This matches the known optimal bounds of [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] and [Bartlett, Linder, and Lugosi, IEEE Trans. Inf. Theory 1998] for k-means and extends it to other important objectives such as k-median. 2. For subspace clustering with j-dimensional subspaces, we show a convergence rate of Õ (√kj2/n ) . These are the first provable bounds for most of these problems. For the specific case of projective clustering, which generalizes k-means, we show a convergence rate of Ω (√kj/n ) is necessary, thereby proving that the bounds from [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016] are essentially optimal. © 2023, CC BY.

关键词： K-means clustering

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