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EFFECTS OF REFRACTIVE INDEX AND ASPECT RATIO ON THE PARTICLE IMAGES IN A LEVITATED DROPLET

Multiphase science and technology 2024年第1期36卷 13-26页

作者： Gatete, Eugene Kaneko, Akiko Shen, Biao University of Tsukuba Systems and Information Engineering Graduate School of Science and Technology Degree Program of Engineering Mechanics and Energy 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan University of Tsukuba Faculty of Engineering of Information Engineering and Systems 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan

A technique using droplets suspended by ultrasound has attracted attention as one of the containerless processing methods. While this can avoid contamination from the container, it is known that ultrasonic levitation creates flow fields inside and outside the droplet. For more precise droplet control, it is desirable to elucidate the internal flow of the droplet, and measurements of the internal flow have been performed using the particle image velocimetry (PIV). The aim of this study is to elucidate the internal flow field behavior by solving optical problems and improving the accuracy of velocity field measurements in levitated droplets. The fluid that is to be investigated is scattered with small tracer particles and illuminated by a laser to capture the flow on a created laser sheet. The curvature distortion is successfully visualized using the PIV approach toward distortion correction using calibration methods. The curvature distortion illustration was performed based on the refractive index and aspect ratio of the simulated droplet in acrylic materials. The fluid flow, affected by droplet curvature and refractive index, has been visualized for both levitated and simulated droplets. The experimental results showed that the droplet curvature can be distorted in two types such as radial and tangential distortions and increase as the refractive index and aspect ratio increases. © 2024 by Begell House, Inc.

关键词： Refractive index

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Modeling and Solving of Platform Resource Scheduling Problem Based on Capability as a Service 7

Modeling and Solving of Platform Resource Scheduling Problem...

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7th International Conference on Computational Intelligence and Applications, ICCIA 2022

作者： Sun, Yu Mao, Shaojie Huang, Songhua Wang, Shuangling 1.Science and Technology on Information Systems Engineering Laboratory 2.Unit Nanjing94860 China Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781665495844

Command information system can realize flexible matching between platform resources and tasks on the basis of platform combat capability as a service. In order to satisfy the intelligent decision-making need of command information system, the platform resource scheduling problem under the condition of capability as a service is described mathematically. Then, a scheduling model aiming to maximize the task processing quality is constructed and a model solving algorithm with heuristic strategy is designed. Finally, the method is simulated and verified with detailed examples. The experiment results show that the method proposed is effective and efficient, which can support intelligent decision-making of command information system. © 2022 IEEE.

关键词： Scheduling

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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Computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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On a New Rough Clustering Based on Optimization 13

On a New Rough Clustering Based on Optimization

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Furuta, Sakuya Endo, Yasunori Master's Program in Risk and Resilience Engineering Degree Programs in Systems and Information Engineering Graduate School of Science Annd Technology University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8573 Japan

ISBN: (纸本)9798350373332

The purpose of this study is to develop and investigate a new rough clustering based on optimization. We propose a restructured algorithm using only one variable, instead of the algorithm described using two variables in existing methods. We also perform numerical examples using artificial data and the Iris and Wine datasets. This research has the potential to be used as a method for determining false negatives and false positives in cancer diagnosis. © 2024 IEEE.

关键词： Clustering algorithms

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Phase-bias-blind computational ghost imaging with optical phased array

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Optics Express 2025年第10期33卷 21132-21141页

作者： Hirata, Sota Matsuda, Naoki Komatsu, Kento Tanemura, Takuo Horisaki, Ryoichi Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

We propose a method for computational ghost imaging (CGI) with an optical phased array (OPA), wherein phase biases on the phase shifters are intrinsically unknown. In our method, an object is illuminated with a number of light fields from the OPA using random phase shifts, and the light from the object is captured by a single-pixel detector. The object and the phase biases are simultaneously estimated from the measured intensity signals. We numerically demonstrate the proposed method. Furthermore, we present complex amplitude field acquisition in OPA-based CGI with intensity measurements performed without reference light by simply adding an iris to the detector. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computational Imaging

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Constructions of a Visual Cryptography Scheme Using an Affine Resolvable BIBD 18

Constructions of a Visual Cryptography Scheme Using an Affin...

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18th International Symposium on information Theory and Its Applications, ISITA 2024

作者： Mitarai, Naoki Koga, Hiroki Graduate School of Science and Technology University of Tsukuba Tennoudai 1-1-1 Tsukuba305-8573 Japan Institute of Systems and Information Engineering University of Tsukuba Tennoudai 1-1-1 Tsukuba305-8573 Japan

ISBN: (纸本)9784885523519

A visual cryptography scheme (VCS) is one of the secret sharing schemes for digital images. In the VCS, n images called shares are generated from a secret image and are distributed to n respective participants. In this paper, we first give a new construction of basis matrices of the VCS realizing the (2, n)-threshold access structure. We use a combinatorial design known as an affine resolvable BIBD. In the construction, the relative difference is almost equal to the optimal value, while the pixel expansion is almost half compared with an existing construction attaining the optimal value if we use a (4l, 2l, 2l-1) affine resolvable BIBD. We also give a construction of basis matrices using a (4l, 2l, 2l-1) affine resolvable BIBD for a certain access structure. In this access structure, participants are partitioned into disjoint groups of size two and the two participants belonging to any group can reproduce a secret image. In addition, any three participants from different groups can obtain no information on a secret image. © 2024 The Institute of Electronics, information and Communication Engineers.

关键词： Cryptography

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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