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Gravity well-inspired double friction pendulum system for bridges under pulse-like near-fault earthquakes

Resilient Cities and Structures 2025年第1期4卷 83-100页

作者： Cao, Sasa Ozbulut, Osman E. School of Civil Engineering Guangzhou University Guangzhou510006 China University of Virginia Department of Engineering Systems and Environment VA22904 United States

When a coin is tossed to a gravity well, it will spiral instead of falling directly to the center. Inspired by this phenomenon, a gravity well-inspired double friction pendulum system (GW-DFPS) is developed to extend the length of sliding trajectories of bridge superstructures during pulse-like near-fault earthquakes. As a result, a greater amount of energy will be dissipated due to the frictional sliding of the isolators. The GW-DFPS consists of a spherical surface and an outer surface described by a 1/x or logarithmic function to build gravity well. Full-scale isolators were fabricated and their response was characterized considering various parameters such as the friction material of slider, surface roughness of sliding surfaces, and applied vertical loads. Additionally, a finite element model of the isolator was created using the experimental test data. Numerical simulations were performed on a case-study bridge structure isolated using both a conventional DFPS system and the proposed GW-DFPS systems. The experimental results reveal that the proposed isolators exhibit stable response under vertical loads varying from 200 kN to 1000 kN with a negative stiffness response when the isolator slides at the outer sliding surface. The numerical simulations of the selected bridge structure demonstrate that the GW-DFPS significantly extends the sliding trajectory lengths of the superstructure during half of the earthquake pulses, resulting in increased energy dissipation during this interval. The kinetic energies of the bridge isolated by GW-DFPS are consistently lower than those of the bridge isolated by the other two kinds of isolators, resulting lower shear forces on the bridge. © 2025 The Author(s)

关键词： Earthquakes

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Smart Drying Technologies: A Case Study on Red Flesh Apple (Kissabel®) Using Computer Vision and Deep Learning

Smart Drying Technologies: A Case Study on Red Flesh Apple (...

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International Mid-Term Conference of the Italian Association of Agricultural engineering, MID-TERM AIIA 2024

作者： Benelli, Alessandro Pandolfi, Flavia Bandiera, Andrea Taormina, Eleonora Russo, Paola Adiletta, Giuseppina Massantini, Riccardo Moscetti, Roberto Department for Innovation in Biological Agro-Food and Forest Systems University of Tuscia Viterbo Italy Department of Chemical Engineering Materials Environment Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031842115

Food drying is a crucial process that helps in enhancing the shelf-life of food at ambient temperature, also lowering transportation costs. However, erroneous applications can degrade product quality and lead to a significant energy consumption as common dryers use fossil fuels. The present study aims to develop a "smart" prototype hot-air dryer augmented with Computer Vision (CV) and Deep Learning (DL) technologies to monitor quality and drying status of red-flesh sliced apples (Malus domestica B. - cultivar Kissabel®) during the process. Both CV and DL represent Process Analytical Technology (PAT) tools that, combined with a Quality by Design (QbD) approach, can lead to real-time data from the process, which are potentially vital for understanding and controlling the drying dynamics of products. The experimentation was conducted on apple slices (approx. 5-mm thick) using the following drying parameters: drying time, 24 h;temperature, 35 ℃;R.H., 35%;and airflow, 3 m s−1. The "smart" drying system consisted of (i) a CMOS digital camera;(ii) a LED illumination setup (4200 K);(iii) a load cell;(iv) an Arduino UNO microcontroller (master);(v) a Jumo Dicon Touch controller (slave);(vi) a temperature and R.H. sensor;and (vii) a Raspberry Pi 4 microcomputer. The open-source software Node-Red was used as an orchestrator for controlling devices, as well as acquiring and storing product features in real-time, such as weight, color, size, and shape of slices. All acquired images of apple slices were subjected to semantic segmentation using a deep learning model. With the slice shrinkage data obtained from semantic segmentation and the moisture data, time-independent moisture ratio prediction models were developed. The approach opens new perspectives for the use of CV and DL technologies in food drying. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer vision deep learning moisture red flesh apples shrinkage smart drying

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Effect of preprocessing on performances of machine learning-based mineral composition analysis on gas hydrate sediments, Ulleung Basin, East Sea

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Petroleum Science 2025年第1期22卷 151-162页

作者： Hongkeun Jin Ju Young Park Sun Young Park Byeong-Kook Son Baehyun Min Kyungbook Lee Department of Geoenvironmental Sciences Kongju National University Tunnel & Underground Facility Division ESCO Consultant & Engineers Company Yellow Sea Institute of Geoenvironmental Sciences Petroleum and Marine Research Division Korea Institute of Geoscience and Mineral Resources Department of Climate and Energy Systems Engineering Ewha Womans University Center for Climate/Environment Change Prediction Research Ewha Womans University

Gas hydrate(GH) is an unconventional resource estimated at 1000-120,000 trillion *** on GH is ongoing to determine its geological and flow characteristics for commercial production. After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin, the mineral composition of 488 sediment samples was analyzed using X-ray diffraction(XRD). Because the analysis is costly and dependent on experts, a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data. However, the model's performance was limited because of improper preprocessing of the intensity profile. Because preprocessing was applied to each feature, the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition. In this study, the profile was preprocessed for each sample using minmax scaling because relative intensity is critical for mineral analysis. For 49 test data among the 488data, the convolutional neural network(CNN) model improved the average absolute error and coefficient of determination by 41% and 46%, respectively, than those of CNN model with feature-based preprocessing. This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data. The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau. In addition, the overall procedure can be applied to any XRD data of sediments worldwide.

关键词： Sample-based preprocessing X-ray diffraction (XRD) Machine learning Mineral composition Gas hydrate (GH) Ulleung basin

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An Improved Power Factor Correction Converter for Switched Reluctance Motor Drive Performance Analysis

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IEEE Access 2025年 13卷 71187-71196页

作者： Mohanraj, Deepak Samithas, Devakirubakaran Balachandran, Praveen Kumar Mohd Zainuri, Muhammad Ammirrul Atiqi KIT-Kalaignarkarunanidhi Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641402 India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Automobile Engineering Tamil Nadu Kattankulathur603203 India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Telangana Hyderabad501218 India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600069 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Selangor Bangi43600 Malaysia

Power Factor Correction (PFC) is crucial for the efficient operation of Switched Reluctance Motor (SRM) converters, as it enhances energy utilization and minimizes system losses. Conventional PFC techniques often rely on passive components or simple active methods, which may not fully exploit the dynamic characteristics of SRM operation. Low power factors in SRM converters lead to increased reactive power consumption and potential grid disturbances, necessitating effective PFC strategies to optimize energy efficiency, enhance system performance, and ensure compliance with standards. Therefore, this paper proposes a buck-boost converter to efficiently regulate the DC voltage supplied to the load, enabling voltage levels higher or lower than the input voltage while improving PFC in SRM converters. The objective is to address current challenges and push the boundaries of efficiency and performance. The proposed control strategies leverage the unique characteristics of SRMs to achieve superior PFC performance by dynamically adjusting the converter’s operation in real time based on motor load conditions. This approach ensures optimal PFC across a wide range of operating conditions. The proposed model is developed and validated using MATLAB/Simulink. Simulation and experimental results assess the motor’s performance under both static and dynamic load conditions. Additionally, the effectiveness of the converter is evaluated by analyzing the total harmonic distortion of the input current. © 2013 IEEE.

关键词： Reluctance motors

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Global trade of green iron

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Steel Times International 2025年第33期2025卷 26-33页

作者： Bilici, Süheyb Holtz, Georg Zelt, Ole Jülich, Alexander Tonjes, Annika König, Robin Kronshage, Stefan Meurer, Andreas Li, Zhenxi Trollip, Hilton McCall, Bryce Vishwanathan, Saritha Sudharmma Lechtenböhmer, Stefan Wuppertal Institute for Climate Environment & Energy Wuppertal Germany Institute of Networked Energy Systems Stuttgart Germany Environmental and Energy Systems Studies at Faculty of Engineering Lund University Lund Sweden Energy Systems Research Group Chemical Engineering Department University of Cape Town Cape Town South Africa National Institute for Environmental Studies Tsukuba Ibaraki Japan Indian Institute of Management-Ahmedabad Vastrapur Gujarat Ahmedabad India University of Kassel Kassel Germany

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PID Controller Design for a Mechatronic System using Lion Optimization Algorithm 5

PID Controller Design for a Mechatronic System using Lion Op...

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5th National and the 1st International Conference on Applied Research in Electrical engineering, AREE 2025

作者： Safdari, Amirhossein Mansouri, Amirhosein Fazli, Mohammad Javad Zarafshan, Payam Alipour, Khalil Tarvirdizadeh, Bahram College of Interdisciplinary Science and Technology University of Tehran School of Intelligent Systems Department of Mechatronics Engineering Tehran Iran Shahid Beheshti University Faculty of Mechanical and Energy Engineering Department of Renewable Energy Engineering Tehran Iran University of Tehran School of Mechanical Engineering College of Engineering Tehran Iran Western Sydney University School of Engineering Design and Built Environment Sydney Australia

ISBN: (纸本)9798350377170

Controllers act as a bridge between the operator and the system, allowing the operator to communicate with the system as if it were a human. In other words, the controller is like the thinking brain of an industrial process. A controller communicates all the commands that an expert wants to apply to the system to follow its standard flow process and ultimately achieve the desired response. When industrial processes are left alone and without a controller in the control loop, they typically do not give satisfactory responses in terms of transient or steady-state characteristics. Therefore, the selection and programming of an appropriate controller is one of the most important steps in an industrial process. One of the most widely used and well-known controllers is the PID controller, whose gains are often determined experimentally or through trial and error. However, finding the optimal gains is always a challenge. In this study, for the first time, the optimal gains of the PID controller are determined using Lion Optimization Algorithm (LOA). This research has introduced a systematic and efficient approach to achieving optimal control of mechatronic systems. © 2025 IEEE.

关键词： Three term control systems

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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Computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Exhaustive Feature Selection Using Wrapper method for Artery-Vein Classification in Retinal Fundus Image

Exhaustive Feature Selection Using Wrapper method for Artery...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Assyareefah Hudaibah Saad Nur Asyiqin Amir Hamzah Wan Mimi Diyana Wan Zaki Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Malaysia

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

Retinal imaging plays a crucial role in diagnosing ocular and systemic disorders by analyzing abnormalities in retinal blood vessels. Numerous diagnostic parameters, such as vascular caliber, centerline pixels, vessel bifurcation, arteriolar constriction, and color reflections, have been explored using machine learning and deep learning approaches for artery-vein classification. While these methods have demonstrated high-performance outcomes, limited research has investigated alternative approaches to identify optimal features for artery-vein classification. This study addresses this gap by evaluating nine color features using a wrapper method across three benchmark datasets: DRIVE, HRF, and VICA VR. The DRIVE dataset achieved the best classification performance, with an Area Under the Curve of 91.49%, accuracy of 83.10%, sensitivity of 84.68%, and specificity of 81.47%. Among the features studied, the Chrominance-Blue (Cb) and Chrominance-Red (Cr) components emerged as the most influential. These findings underscore the critical role of color features in enhancing artery-vein classification. They provide a foundation for further research into feature optimization, potentially improving diagnostic tools for retinal and systematic diseases.

关键词： Accuracy Systematics Sensitivity Image color analysis Veins Redundancy Retina Feature extraction Reflection Optimization

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Performance Evaluation of Multi-ONU Customers Access Network using Spectrum Slicing and Duplication Techniques 5

Performance Evaluation of Multi-ONU Customers Access Network...

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5th International Conference on Emerging Electrical Energy, Electronics and Computing Technologies, ICE4CT 2024

作者： Rahman, Juwairiyyah Abdul Mugunthan, Sivaguru A.L. Hassan, Hasliza Abu Muslim, Norliana Ab-Rahman, Mohammad Syuhaimi Department of Electrical Engineering Faculty of Engineering and Life Sciences Universiti Selangor Selangor Bestari Jaya45600 Malaysia Department of Computer and Communication Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Perak Kampar31900 Malaysia Department of Electrical Electronics and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Electrical and Computer Engineering Program Faculty of Engineering Sohar University PC:311 Sohar Oman

This study aims to increase the number of access users by limiting the sample size to 30 users while ensuring that every Optical Network Unit (ONU) receives data from the Optical Line Terminal (OLT). The proposed solution allows for the determination of power requirements at each ONU based on bus-star topologies, facilitating an increase in user capacity. The power received by each ONU is a critical parameter that system designers must consider when planning the implementation of Fiber to the Home (FTTH), as it impacts the output data in terms of Bit Error Rate (BER) and Signal Quality Factor (QF) for each user. Various parameters were evaluated to ensure that the enhanced network improves scalability while adhering to standard specifications. These parameters included the Q-factor, BER, eye diagram, user count, and received power. The study employed slicing and duplication techniques, utilizing Arrayed Waveguide Grating (AWG) for signal duplication and Wavelength Division Multiplexing for signal slicing. The simulations were conducted using a transmitter operating at a frequency of 1491 nm with a power output of 0 dBm, utilizing the latest version of OptiSystem 20 software. The results highlighted the system's capability to support a total of 73,728 users, meeting stringent performance criteria. Specifically, the system achieved a Q-factor greater than 6, indicating high signal quality, and a bit error rate (BER) lower than 1 × 10-9, ensuring reliable data transmission. For the sampled 30 users, the average Q factor was 8.1527, and the average BER was 5.3648 × 10^-11, with an output power level of -54.278 dBm. Despite an increase in users, the quality of the data supplied to clients remained unchanged. These outcomes underscore the robustness and efficiency of the design in delivering high-capacity, error-resilient optical communication. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Q factor measurement

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Resonance broadening effects of weak turbulence on Earth's radiation belt electrons

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Physical Review E 2025年第3期111卷 L033201-L033201页

作者： Xiongdong Yu Zhigang Yuan Dedong Wang Oliver Allanson Samuel Hunter School of Earth and Space Science and Technology Wuhan University Wuhan 430072 China GFZ German Research Center for Geosciences Potsdam 14473 Germany Space Environment and Radio Engineering Electronic Electrical and Systems Engineering School of Engineering University of Birmingham Birmingham B15 2TT United Kingdom Environmental Mathematics Department of Earth and Environmental Sciences University of Exeter Penryn TR10 9FE United Kingdom Department of Mathematics University of Exeter Exeter EX4 4QJ United Kingdom

The quasilinear diffusion theory is generalized to include the resonance broadening effect of weak turbulence on relativistic electrons in Earth's radiation belts. In contrast with quasilinear resonant diffusion, in which cases the resonant condition should be rigorously satisfied, the second-order theory demonstrates that the wave-driven scattering of relativistic electrons could be broadened into an area of velocity space adjacent to the resonant region. It is shown that a finite departure time, resulted from the second-order wave-particle interaction, is the key factor controlling the occurrence and effectiveness of such a resonance broadening. The formula to solve the departure time has been proposed. Adopting the solved departure time, the resultant model can correctly reproduce results of test particle simulations. Our result indicates the importance of resonance broadening effect in the radiation belt modeling, and in understanding the weak turbulence dynamics in a magnetized plasma system.

关键词： Radiation effects

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