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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhao, Dequn Li, Chunsheng Ma, Hongwei Zhang, Suming Faculty of Information Technology Beijing University of Technology Beijing China Beijing Institute of Aerospace Systems Engineering Beijing China

ISBN: (纸本)9781510680425

This paper propose a retinal image segmentation model according to hybrid pooling and multi-scale attention mechanism. Due to the crucial importance of retinal vessels for the segmentation of the retina and their intricate structure, which serves as vital diagnostic information for diseases, we focus on retinal images in medical imaging. We introduce a novel medical image segmentation model, MDA-Unet, built upon the Unet architecture. Extensive experiments are conducted on the DRIVE dataset. Addressing challenges such as limited data, varying image quality, and the presence of small structures in segmented target areas causing reduced segmentation accuracy, the proposed enhanced model MDA-Unet aims to overcome these issues. We investigate the impact of incorporating modules such as Exponential Moving Average (EMA), Multi-Path Module (MPM), and serpentine convolutional kernels on the model. The research results indicate that the addition of these modules enhances the ability of the model to be segmented. This improved model is tested with the DRIVE dataset, yielding segmentation results that surpass those of previously proposed segmentation models. Compared with the previous model segmentation results, the method proposed in this paper has achieved good results in fundus vascular segmentation. © 2024 SPIE.

关键词： Image segmentation

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Exploring Large Language Models for Relevance Judgments in Tetun 1

Exploring Large Language Models for Relevance Judgments in T...

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1st Workshop on Large Language Models for Evaluation in Information Retrieval, LLM4Eval 2024

作者： de Jesus, Gabriel Nunes, Sérgio INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal FEUP - Faculty of Engineering University of Porto Portugal

The Cranfield paradigm has served as a foundational approach for developing test collections, with relevance judgments typically conducted by human assessors. However, the emergence of large language models (LLMs) has introduced new possibilities for automating these tasks. This paper explores the feasibility of using LLMs to automate relevance assessments, particularly within the context of low-resource languages. In our study, LLMs are employed to automate relevance judgment tasks, by providing a series of query-document pairs in Tetun as the input text. The models are tasked with assigning relevance scores to each pair, where these scores are then compared to those from human annotators to evaluate the inter-annotator agreement levels. Our investigation reveals results that align closely with those reported in studies of high-resource languages. © 2024 CEUR-WS. All rights reserved.

关键词： Structured Query Language

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Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns

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Frontiers of Structural and Civil engineering 2024年第8期18卷 1169-1194页

作者： Tang QIONG Ishan JHA Alireza BAHRAMI Haytham F.ISLEEM Rakesh KUMAR Pijush SAMUI School of Applied Technologies Qujing Normal UniversityQujing 655011China Department of Civil Engineering Indian Institute of Technology-BHUVaranasi 221005India Department of Building Engineering Energy Systems and Sustainability ScienceFaculty of Engineering and Sustainable DevelopmentUniversity of GävleGävle 80176Sweden Department of Civil Engineering National Institute of Technology-PatnaPatna 800005India

This study employs a hybrid approach,integrating finite element method(FEM)simulations with machine learning(ML)techniques to investigate the structural performance of double-skin tubular columns(DSTCs)reinforced with glass fiber-reinforced polymer(GFRP).The investigation involves a comprehensive examination of critical parameters,including aspect ratio,concrete strength,number of GFRP confinement layers,and dimensions of steel tubes used in DSTCs,through comparative analyses and parametric *** ensure the credibility of the findings,the results are rigorously validated against experimental data,establishing the precision and trustworthiness of the *** present research work examines the use of the columns with elliptical cross-sections and contributes valuable insights into the application of FEM and ML in the design and evaluation of structural systems within the field of structural engineering.

关键词： elliptical column fiber-reinforced polymer machine learning finite element method ABAQUS

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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FPGA Realization of a Security System for Internet of Multimedia Things

FPGA Realization of a Security System for Internet of Multim...

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2024 International Conference on Microelectronics, ICM 2024

作者： Osama, Remas Mamdouh, Eyad Alexan, Wassim Abd El Ghany, Mohamed A. Faculty of Media Engineering and Technology German University in Cairo Egypt Faculty of Information Engineering and Technology German University in Cairo Egypt Integrated Electronics Systems Lab Tu Darmstadt Germany

ISBN: (纸本)9798350379396

Multimedia encryption is essential for securing sensitive data during transmission and storage. This research work introduces a new visual data encryption technique using three hyperchaotic systems: a memristor system, a hyperchaotic 7D system, and an Erbium-doped fiber laser system and its hardware implementation. The algorithm leverages the complex dynamics and random-like behavior of these systems to achieve robust encryption across three layers. Each layer uses a pseudorandom number generator to create an S-box and an encryption key, which are then applied to the image content. Experiments demonstrate the scheme's effectiveness in terms of encryption quality, resistance to common attacks, and computational efficiency. This technique is promising for applications requiring secure image transmission and storage, such as military communications, medical imaging, and confidential document sharing. The hardware design of the proposed algorithm is implemented on Nexys 4 DDR FPGA board. For a 256 x 256 image, the hardware achieves an encryption rate of 0.49 Gbps. © 2024 IEEE.

关键词： Medical imaging

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An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2873-2897页

作者： Yingui Qiu Shuai Huang Danial Jahed Armaghani Biswajeet Pradhan Annan Zhou Jian Zhou School of Resources and Safety Engineering Central South UniversityChangsha410083China School of Civil and Environmental Engineering University of Technology SydneyNew South WalesAustralia Centre for Advanced Modelling and Geospatial Information Systems School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew SouthWalesAustralia Civil and Infrastructure Engineering Discipline School of EngineeringRoyal Melbourne Institute of Technology(RMIT)MelbourneVictoria3001Australia

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and *** the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction *** performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine *** previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of ***,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF *** the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,*** examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM *** of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied *** newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed ***,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHA

关键词： Tunnel boring machine random forest GOGHS optimization PSO optimization GA optimization ABC optimization SHAP

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Comparison of different compressor technologies for refrigerated display cabinet: Experimental study

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Materials Today: Proceedings 2023年 81卷 74-80页

作者： Ocak, Cemil Koşan, Meltem Erten, Süleyman Nur Erdoğmuş, Fatma Öder, Melis Electrical Electronics Engineering Faculty of Technology Gazi University Ankara 06560 Turkey Energy Systems Engineering Faculty of Engineering Kahramanmaras Istiklal University Kahramanmaras 46300 Turkey Nurdil Refrigeration Inc. Ankara 06000 Turkey

Designing refrigerated display cabinet with a high coefficient of performance and low energy consumption have a crucial role to maximize energy savings. Compressors have a leading position in refrigeration systems which use mechanical vapor compression in terms of energy consumption. Proper selection and adaptation of compressors will increase the efficiency of the refrigeration system and reduce energy consumption to a large extent. In this study, an eco-friendly refrigerated display cabinet with variable speed compressor technology was designed to analyze the effects of fixed and variable speed compressor technologies on the coefficient of performance and total energy consumption. Two identical refrigerated display cabinets which had different compressor technologies were tested in Class 3 (25 °C, Rh 60 %) conditions in accordance with ISO 23953-2: 2015 standard. Environmentally friendly R290 (Propane) was used as a refrigerant in both systems. During the experiment, 11.2 kWh/24 h total electrical energy was consumed per day in the system with fixed speed compressor (System 1) and 8.3 kWh/24 h total electrical energy per day in the system with variable speed compressor (System 2). In order to contribute to the widespread use of environmentally friendly cabinets, it was observed that System 2 consumed 25.9 % less energy than System 1 with the help of a variable speed compressor. © 2023

关键词： Energy efficiency

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Power optimization of photovoltaic modules under varying environmental conditions based on current equalization collaborating with constant voltage control

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Clean Energy 2024年第4期8卷 169-182页

作者： DaiBin Tang Fei Lu Siaw Tzer Hwai Gilbert Thio School of Electrical Engineering Anhui Technical College of Mechanical and Electrical EngineeringWuhu 241002China Centre for Sustainability in Advanced Electrical and Electronics Systems(CSAEES) Faculty of EngineeringBuilt Environment and Information TechnologySEGi University47810 Petaling JayaSelangorMalaysia

The performance of photovoltaic(PV)modules is affected by environmental factors such as irradiance and temperature,which can lead to a decrease in output performance or even *** study proposes an improved formula for calculating the real maximum power of PV modules by analysing the influence of irradiance and temperature.A simulation model is developed using PLECS software to simulate the global maximum power of PV modules under different environmental conditions and the results are compared with the calculated real maximum power.A power optimization scheme for PV modules is then proposed based on current equalization and constant voltage *** scheme employs a single-switch multi-winding forward-flyback converter to equalize the mismatched currents between cell strings,thereby enhancing the output *** proportional-integral controllers are utilized to achieve constant voltage control and obtain the real maximum power of PV *** models are built in the PLECS simulation platform to evaluate the performance of a global maximum power point tracking scheme based on the traditional perturb-and-observe(TPO)algorithm with current equalization,a segment perturb-and-observe algorithm without current equalization,and the proposed power optimization *** simulation results demonstrate that the proposed constant voltage control has greater efficiency than the TPO *** proposed scheme achieves a significant improvement in efficiency,with a 27.87%increase compared with the segment perturb-and-observe algorithm without current equalization.

关键词： current equalization constant voltage control forward-flyback converter PV modules perturb-and-observe(P&O)algorithm

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

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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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Predicting Students Performance Using Data Mining Approach (Case Study: IT Del)

Predicting Students Performance Using Data Mining Approach (...

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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Bonar Sirait, Jaime Christ Togatorop, Parmonangan R. Tambunan, Paulus M.L.P Yanti Marpaung, Yemima Febe Situmeang, Samuel I.G Simanjuntak, Humasak T.A Faculty of Informatics and Electrical Engineering Institute of Del Technology Department of Information Systems Indonesia Faculty of Informatics and Electrical Engineering Institute of Del Technology Lecturer in Department of Information Systems Indonesia

ISBN: (纸本)9798350381382

Predicting student performance stands as a critical task to comprehensively assess their academic achievements, whether they classified as the category of good, sufficient, or bad. The outcome of a student's final exam can be influenced by a multitude of factors, among which the quality of the learning process and teaching methodology hold considerable significance. Recognizing the intricate interplay of these factors, it becomes evident that the integration of a satisfaction survey becomes imperative. In the realm of this research, we harness the potential embedded within both student satisfaction survey data and their ultimate academic grades. Overcoming existing challenges by an imbalanced dataset, we employ a holistic approach by utilizing the Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) to rectify the skewed distribution of data. To further enhance prediction capabilities, we introduced an innovative technique known as Deep Feature Synthesis. This advanced approach generating 36 novel features. The culmination of our research underscores the superiority of the random forest algorithm, especially when enriched by the harmonious merging of Deep Feature Synthesis and SVM-SMOTE through the strategic implementation of Random Search. Notably, our model achieves an impressive accuracy rate of 69%, complemented by 47% precision, 52% recall, and 47% F1-score. copy;2023 IEEE. © 2023 IEEE.

关键词： Support vector machines

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