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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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Stability of partially unstable discrete-time switched positive nonlinear systems with delays

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Applied Mathematics and Computation 2025年 507卷

作者： Ni, Jinyuan Sheng, Yin Xiao, Qiang Zeng, Zhigang Huang, Tingwen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

This paper addresses the stability issue of discrete-time switched positive nonlinear systems (SPNSs) characterized by partial unstable subsystems and constant delays. Firstly, we use a parameter to constrain the ratio of active durations of the stable and unstable modes, and derive the exponential stability conditions for SPNSs based on a key function. Then we analyze the relationship between convergence speed and the constant delay. Additionally, we obtain a stability criterion of SPNSs under a particular sequence of switching signals. Finally, we validate the results through a numerical example. © 2025 Elsevier Inc.

关键词： Stability criteria

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Memory Regressor Extended Echo State Networks for Nonlinear Dynamics Modeling

Memory Regressor Extended Echo State Networks for Nonlinear ...

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European control Conference (ECC)

作者： Kai Hu Qian Wang Tian Shi Kohei Nakajima Yongping Pan School of Artificial Intelligence Sun Yat-sen University Zhuhai China School of Advanced Manufacturing Sun Yat-sen University Shenzhen China School of Computer Science and Engineering Sun Yat -sen University Guangzhou China Graduate School of Information Science and Technology University of Tokyo Tokyo Japan

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Echo state network (ESN) implements an alternative paradigm called reservoir computing to train recurrent neural networks (RNNs), where internal weights are randomly generated and kept fixed, and only readout weights need to be trained, which greatly reduces the training complexity of RNNs. ESN not only facilitates the practical implementation of RNNs but also shows superior performance over fully trained RNNs across a range of applications. However, the conventional ESN suffers from the drawbacks of stringent conditions for weight convergence and slow convergence speed. This paper proposes a memory regressor extended learning method to update the readout weights of ESNs. By constructing and incorporating a generalized prediction error based on regressor extension and filtering, the capacity of ESN to utilize historical data can be greatly improved. In the discrete-time domain, it is proven that exponential convergence of readout weights is achieved under a condition termed interval excitation that is strictly weaker than the classical condition of persistent excitation. Simulation results on modeling a 10th-order nonlinear autoregressive moving-average (NARMA) system have revealed that the proposed approach accelerates weight convergence speed almost ten times higher compared to the conventional ESN.

关键词： Training Learning systems Recurrent neural networks Filtering Simulation Neurons Reservoir computing

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Teeth Segmentation from Bite-Wing X-Ray Images by Integrating Nested Dual UNet with Swin Transformers

Teeth Segmentation from Bite-Wing X-Ray Images by Integratin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yubo Li Yibin Tian Zhiyuan Zhang Xueyang Zhang Bingran Du Zhi Zeng School of Computer and Information Science Chongqing Normal University Chongqing CN College of Mechatronics and Control Engineering Shenzhen University Shenzhen CN Litemaze Technology Shenzhen CN School of Computing and Information Systems Singapore Management University Singapore SG Department of Stomatology Shunde Hospital Southern Medical University (The First People's Hospital of Shunde) Foshan CN

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In medical practice, the precision of image segmentation is crucial for diagnosis and treatment evaluations. Specifically, in dentistry, accurate teeth segmentation from bite-wing images is important for automatic and objective evaluations of root canal treatments. This study introduces $\text{Swin}-\mathrm{U}^{2} \text{Net}$ , a model merging the nested dual UNet with residual U-block and Swin Transformers. It combines the local feature extraction capability of the former and the global attention and context understanding of the latter. It has been evaluated for tooth root segmentation using 500 bite-wing dental x-ray images obtained from a root canal treatment clinic. It achieved the best segmentation outcome in terms of Intersection over Union (IOU) and the third best result in terms of Dice Similarity Coefficient (DSC) with the second least amount of network parameters among six UNet-like models, thus it is effective and efficient.

关键词： Image segmentation Irrigation Accuracy Merging Teeth Transformers Feature extraction Dentistry X-ray imaging Medical diagnostic imaging

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Autonomous In-Plane Normal Positioning in Robotic Ultrasound Scanning

Autonomous In-Plane Normal Positioning in Robotic Ultrasound...

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IEEE International Conference on Robotics and Biomimetics

作者： Yizhao Qian Yameng Zhang Max Q.-H. Meng Li Liu Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China Department of Electronic and Electrica Engineering Southern University of Science and Technology Shenzhen China Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Electrical and Computer Engineering University of Alberta Canada Great Bay University and the Dongguan Great Bay Institute for Advanced Study Dongguan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Ultrasound examination is pivotal in the preoperative assessment of Proximal Humerus Fractures (PHF). To construct a 3D representation of the entire upper arm, it is critical to minimize the relative error between the probe's axis and the skin's normal to secure high-fidelity images. In this research, we introduce an autonomous robotic ultrasound scanning system that facilitates both scanning and 3D reconstruction. To achieve precise control and diminish in-plane motion errors between the probe and skin, we introduce a method for in-plane normal vector search predicated on column similarity. This technique affords high-precision and high-frequency in-plane ultrasound scanning. Experimental findings confirm that our automated system consistently aligns the probe with the skin's normal direction on human arms, thighs, and abdomens. The findings reveal that, relative to traditional algorithms, our method realizes an 84% increase in speed, with an average error below 4.12°. To corroborate the algorithm's efficacy in real-time scanning, we reconstructed the 3D volume of volunteers' upper arms.

关键词： Ultrasonic imaging Three-dimensional displays Thigh Robot sensing systems Skin Vectors Robustness Real-time systems Probes Surface treatment

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TEMPORAL LOGIC control FOR NONLINEAR STOCHASTIC SYSTEMS UNDER UNKNOWN DISTURBANCES

arXiv

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arXiv 2024年

作者： Gracia, Ibon Laurenti, Luca Mazo, Manuel Abate, Alessandro Lahijanian, Morteza Smead Department of Aerospace Engineering Sciences CU Boulder BoulderCO United States Delft Center for Systems and Control Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Netherlands Department of Computer Science University of Oxford United Kingdom

In this paper, we present a novel framework to synthesize robust strategies for discrete-time nonlinear systems with random disturbances that are unknown, against temporal logic specifications. The proposed framework is data-driven and abstraction-based: leveraging observations of the system, our approach learns a high-confidence abstraction of the system in the form of an uncertain Markov decision process (UMDP). The uncertainty in the resulting UMDP is used to formally account for both the error in abstracting the system and for the uncertainty coming from the data. Critically, we show that for any given state-action pair in the resulting UMDP, the uncertainty in the transition probabilities can be represented as a convex polytope obtained by a two-layer state discretization and concentration inequalities. This allows us to obtain tighter uncertainty estimates compared to existing approaches, and guarantees efficiency, as we tailor a synthesis algorithm exploiting the structure of this UMDP. We empirically validate our approach on several case studies, showing substantially improved performance compared to the state-of-the-art. © 2024, CC BY.

关键词： Temporal logic

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单晶二维材料生长中的层数调控机制

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science Bulletin 2023年第23期68卷 2936-2944,M0005页

作者：张磊宁孔潇董际臣丁峰 Beijing Key Laboratory of Construction Tailorable Advanced Functional Materials and Green Applications MOE Key Laboratory of Cluster ScienceSchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China State Key Laboratory of Information Functional Materials 2020 X-LabShanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Beijing National Laboratory for Molecular Sciences Key Laboratory of Organic SolidsInstitute of ChemistryChinese Academy of SciencesBeijing 100190China School of Chemical Sciences University of Chinese Academy of SciencesBeijing 100049China Faculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

最近,通过将外延生长的多层二维晶畴进行无缝拼接,研究人员成功实现了晶圆级单晶二维多层材料的合成.不同于以往观察到的类似婚礼蛋糕或倒置婚礼蛋糕结构,这些多层二维晶畴中所有层都具有完全相同的尺寸和形状,即多层同步生长.基于此,... 详细信息

最近,通过将外延生长的多层二维晶畴进行无缝拼接,研究人员成功实现了晶圆级单晶二维多层材料的合成.不同于以往观察到的类似婚礼蛋糕或倒置婚礼蛋糕结构,这些多层二维晶畴中所有层都具有完全相同的尺寸和形状,即多层同步生长.基于此,本文通过理论计算探索了多层二维材料的不同生长模式,提出了多层同步生长的物理模型,揭示了基底上液体异质表面层的形成是保证同步生长的关键性因素.作者指出在生长过程中,多层二维材料嵌入在衬底的异质表面层中,同时底层与固态基底直接接触.异质层能有效钝化二维材料的边缘,从而防止这些活性边缘的融合,而异质层与基底之间的高界面则迫使不同层的二维材料边缘保持同步生长.通过对多层同步生长机理的深入了解,本文揭示了发生多层同步生长的条件,为合成层数可控的晶圆级单晶二维材料提供了新的思路.

关键词： 2D materials Epitaxial growth Thickness control Synchronic growth

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Flexible control of the Interline DC Power Flow controller Using Virtual Capacitor in VSC-HVDC Grids

Flexible Control of the Interline DC Power Flow Controller U...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mirhamed Pourmirasghariyan Mirsajed Pourmirasghariyan Seyed Saeid Heidari Yazdi Seyyed Yousef Mousazadeh Mousavi Kumars Rouzbehi G. B. Gharehpetian Departement of Electrical and Computer Engineering Amirkabir University of Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical & Computer Engineering School of Engineering and Digital Science Nazarbayev University Nur-Sultan Kazakhstan Faculty of Engineering and Technology University of Mazandaran Sari Iran Department of Systems and Automatic Control Engineering University of Seville Seville Spain

The performance of Interline DC Power Flow controllers (IDC-PFCs) in Voltage Source Converters (VSC)-based High Voltage Direct Current (HVDC) grids, can be affected due to different issues. The current limitation of HVDC lines, the voltage limitation of HVDC buses, and DC voltage of the IDC-PFC intermediary capacitor prevent effective and efficient operation of IDC-PFCs. In this paper, it is shown that this issue can be overcome by using a virtual capacitor in parallel with the IDC-PFC intermediary capacitor. Also, an energy control-based scheme is proposed for the operation of IDC-PFCs in VSC-HVDC grid. The benefits of using the virtual capacitor are: widening the operational area of the IDC-PFCs for the determined duty cycle and injecting more voltage in series to the interconnected HVDC line to control the related HVDC line current. The proposed solution is successfully evaluated on a CIGRE three-terminal VSC-HVDC grid which is modeled by linearized space-state equations.

关键词： Fluctuations HVDC transmission Capacitors High-voltage techniques Boosting Mathematical models Power conversion

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An Evolutionary Neuron Model with Dendritic Computation for Classification and Prediction 17th

An Evolutionary Neuron Model with Dendritic Computation for ...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Tang, Cheng Song, Zhenyu Tang, Yajiao Tang, Huimei Wang, Yuxi Ji, Junkai Faculty of Engineering University of Toyama Toyama930-8555 Japan College of Computer Science and Technology Taizhou University Taizhou225300 China College of Economics Central South University of Forestry and Technology Changsha410004 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Taizhou People’s Hospital Taizhou225300 China

ISBN: (纸本)9783030845216

Advances in the understanding of dendrites promote the development of dendritic computation. For decades, the researchers are committed to proposing an appropriate neural model, which may feedback the research on neurons. This paper aims to employ an effective metaheuristic optimization algorithm as the learning algorithms to train the dendritic neuron model (DNM). The powerful ability of the backpropagation (BP) algorithm to train artificial neural networks led us to employ it as a learning algorithm for a conventional DNM, but this also inevitably causes the DNM to suffer from the drawbacks of the algorithm. Therefore, a metaheuristic optimization algorithm, named the firefly algorithm (FA) is adopted to train the DNM (FADNM). Experiments on twelve datasets involving classification and prediction are performed to evaluate the performance. The experimental results and corresponding statistical analysis show that the learning algorithm plays a decisive role in the performance of the DNM. It is worth emphasizing that the FADNM incorporates an invaluable neural pruning scheme to eliminate superfluous synapses and dendrites, simplifying its structure and forming a unique morphology. This simplified morphology can be implemented in hardware through logic circuits, which approximately has no effect on the accuracy of the original model. The hardwareization enables the FADNM to efficiently process high-speed data streams for large-scale data, which leads us to believe that it might be a promising technology to deal with big data. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Magnetization Dynamics in Nanofiber Networks 11

Magnetization Dynamics in Nanofiber Networks

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11th IEEE International Conference "Nanomaterials: Applications and Properties", NAP 2021

作者： Blachowicz, Tomasz Steblinski, Pawel Grzybowski, Jacek Ehrmann, Andrea Institute of Physics Center for Science and Education Silesian University of Technology Gliwice Poland Faculty of Electronics and Informatics Koszalin University of Technology Koszalin Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences Bielefeld Germany

ISBN: (纸本)9781665439077

Magnetic nanofibers are of great interest for applications like data transport and storage as well as in basic research. Especially bent nanofibers, which can unambiguously be produced by electrospinning, show a broad spectrum of possible magnetization reversal processes, depending on bending radius, geometry, magnetic field orientation, etc. Besides these quasistatic processes, dynamic investigations are necessary for investigating data transport properties of magnetic nanofibers. We report on domain wall transport through coupled bent nanowires, forming networks with multiple data inputs and outputs. Our results show diverse phenomena which have to be taken into account during these dynamic processes, such as domain wall instabilities or interference between converging signals, and suggest possible architectures of nanowire-based logics. ©2021 IEEE

关键词： Nanofibers

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