alexandria engineering journal最新文献_第7页

Diverse bimetallic MOF polymorphs for the degradation of pollutants/antibiotics and quantum dot integration for enhanced WLED performance 用于降解污染物/抗生素的多种双金属MOF多晶型和用于增强WLED性能的量子点集成

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-09 DOI: 10.1016/j.aej.2025.09.006

Kasimayan Uma , Cheng-Han Wu , Ting-Wei Shen , Robin Khosla , Xiu-Jia Guan , Wei-Kuan Hung , Zong-Liang Tseng

{"title":"Diverse bimetallic MOF polymorphs for the degradation of pollutants/antibiotics and quantum dot integration for enhanced WLED performance","authors":"Kasimayan Uma , Cheng-Han Wu , Ting-Wei Shen , Robin Khosla , Xiu-Jia Guan , Wei-Kuan Hung , Zong-Liang Tseng","doi":"10.1016/j.aej.2025.09.006","DOIUrl":"10.1016/j.aej.2025.09.006","url":null,"abstract":"<div><div>A bimetallic composite of cobalt (Co) and nickel (Ni) integrated into a zirconium-based metal-organic framework (ZrMOF) was successfully synthesized via a hydrothermal method for dual-functional applications. The Ni-Co/ZrMOF composite demonstrated good photocatalytic activity in degrading organic pollutants, particularly the antibiotic tetracycline (TC), outperforming Ni/ZrMOF, Co/ZrMOF, and pristine ZrMOF. This enhanced performance was attributed to the synergistic effect of the bimetallic system, which significantly increased the production of reactive oxygen species. Additionally, the integration of Ni-Co/ZrMOFs enabled the fabrication of a white light-emitting diode (WLED) by incorporating green formamidinium lead-bromide perovskite quantum dots (FAPQDs) and red-emitting phosphors powder onto a blue LED chip. The inclusion of FAPQDs into the Ni-Co/ZrMOF framework not only modified its structural properties but also enhanced its chemical stability through a straightforward solution-processing method. The resulting WLED exhibited superior performance, achieving a National Television System Committee (NTSC) color gamut coverage of 126.6 %. Notably, the NTSC coverage achieved in this work surpasses previously reported values, highlighting the excellent performance of the Ni-Co/ZrMOF@FAPQDs composite. This composite exhibits precise color coordinates and significant potential for next-generation WLED applications, further underscoring its versatility in environmental remediation and advanced optoelectronic devices.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"130 ","pages":"Pages 35-46"},"PeriodicalIF":6.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and analysis of biomimetic nested lattice structures based on additive manufacturing 基于增材制造的仿生嵌套晶格结构设计与分析

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-09 DOI: 10.1016/j.aej.2025.09.009

Dongming Li , Long Guo , Tongyuan Sun , Jiasen Si

{"title":"Design and analysis of biomimetic nested lattice structures based on additive manufacturing","authors":"Dongming Li , Long Guo , Tongyuan Sun , Jiasen Si","doi":"10.1016/j.aej.2025.09.009","DOIUrl":"10.1016/j.aej.2025.09.009","url":null,"abstract":"<div><div>There are various hierarchical structures in nature, such as the double-layer wings of a dragonfly, which possess enhanced mechanical properties while also being lightweight. Inspired by the double-layer wings of the dragonfly, this paper adopts bionics and novel hierarchical design concepts to design a single-layer hexagonal crystal lattice SFCC and two double-layer hexagonal crystal lattices DFCC and RFCC. Using the selective laser melting technology (SLM) in additive manufacturing (AM), the AlSi10Mg powder is melted and formed. The mechanical properties and failure mechanisms of these three lattice structures were systematically studied through numerical simulation and compression tests. The strains of them were observed using DIC deformation diagrams. Studies have shown that compared with SFCC, DFCC has a significantly better resistance to deformation in the compressed internal structure. Its <em>EA</em> and <em>SEA</em> have been significantly improved, and it has the optimal overall mechanical properties, verifying the effectiveness of the nested structure. Finally, the mechanical properties of five different diameter ratios of unit cells of DFCC were discussed. Among them, the unit cell with an outer-inner diameter ratio of 1:2 had the best <em>SEA</em> effect. In conclusion, this work provides new ideas for the design of bionic nested lattice structures.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"130 ","pages":"Pages 11-21"},"PeriodicalIF":6.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-scale oil well detection in remote sensing images based on MSA-Net: Background separation and dynamic optimization strategy 基于MSA-Net的遥感图像多尺度油井检测：背景分离与动态优化策略

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-09 DOI: 10.1016/j.aej.2025.08.043

Zixiang Zhao , Jiahui Li , Xianye Bu , Jinyu Wang , Yan Xu

{"title":"Multi-scale oil well detection in remote sensing images based on MSA-Net: Background separation and dynamic optimization strategy","authors":"Zixiang Zhao , Jiahui Li , Xianye Bu , Jinyu Wang , Yan Xu","doi":"10.1016/j.aej.2025.08.043","DOIUrl":"10.1016/j.aej.2025.08.043","url":null,"abstract":"<div><div>Oil well detection is vital in remote sensing resource monitoring, significantly improving oilfield resource management and environmental protection. However, oil well targets pose challenges due to their small size, complex backgrounds, and diverse viewpoints. This paper proposes an innovative oil well detection framework to enhance detection accuracy and robustness. The framework combines multi-scale feature extraction, background separation, and enhancement techniques. First, the background separation module distinguishes oil well targets from the background, while the Generative Adversarial Network (GAN) enhances target saliency, reducing background interference. Then, a Feature Pyramid Network (FPN) is used for multi-scale feature extraction to handle various oil well target sizes and shapes, combined with an attention mechanism to optimize feature fusion for improved detection of small and partially occluded targets. Finally, an adaptive detection module adjusts the strategy based on target features, using bounding box regression and Non-Maximum Suppression (NMS) to refine results and ensure precise localization. Experimental results show significant improvements in performance, effectively addressing different target scales and occlusion issues, providing reliable support for oilfield management.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"130 ","pages":"Pages 22-34"},"PeriodicalIF":6.8,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep segmentation of retail customers based on improved DEC and multimodal semantic representation 基于改进DEC和多模态语义表示的零售客户深度分割

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-08 DOI: 10.1016/j.aej.2025.09.012

Menglu Wang , Tong Meng , Xiaoyan Gu , Dandan Wang , Rong Wang , Rui Zhao

{"title":"Deep segmentation of retail customers based on improved DEC and multimodal semantic representation","authors":"Menglu Wang , Tong Meng , Xiaoyan Gu , Dandan Wang , Rong Wang , Rui Zhao","doi":"10.1016/j.aej.2025.09.012","DOIUrl":"10.1016/j.aej.2025.09.012","url":null,"abstract":"<div><div>With the advancement of digital transformation, the retail industry has accumulated a vast amount of customer data, particularly customer review data, which provides valuable insights into customer behavior and sentiment. Traditional customer segmentation methods mainly rely on market research and manual analysis. However, as the volume and complexity of data continue to grow, these traditional approaches struggle to meet the demands of precise segmentation and personalized marketing. As a result, machine learning-based customer segmentation methods have become a research focus. In particular, clustering algorithms are capable of identifying potential customer groups from large-scale datasets and providing a scientific basis for personalized marketing and product recommendations. With recent advances in natural language processing, especially the application of Bidirectional Encoder Representations from Transformers (BERT) models in text data processing, research on customer segmentation based on review data has gained increasing attention. Most current studies still focus on traditional clustering algorithms such as K-means and hierarchical clustering. However, these methods face limitations when dealing with high-dimensional sparse data and complex textual information. In addition, existing analyses of review texts often rely on traditional bag-of-words or Term Frequency–Inverse Document Frequency (TF-IDF) methods, which fail to fully capture the deep semantic information within the reviews. To address these challenges, this paper proposes an improved Deep Embedded Clustering (DEC) algorithm, incorporating BERT and Latent Dirichlet Allocation (LDA) models for vectorized representation and clustering analysis of review texts. This approach effectively overcomes the limitations of existing methods and enhances the accuracy and practicality of customer segmentation.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"130 ","pages":"Pages 1-10"},"PeriodicalIF":6.8,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine edge-aware IoT framework for real-time health monitoring: Sensor fusion and AI-driven emergency response in decentralized networks 用于实时健康监测的机器边缘感知物联网框架：分散网络中的传感器融合和人工智能驱动的应急响应

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-06 DOI: 10.1016/j.aej.2025.08.030

Asma Alshuhail , Amnah Alshahrani , Hany Mahgoub , Mukhtar Ghaleb , Abdulbasit A. Darem , Nojood O. Aljehane , Modhawi Alotaibi , Fahad Alzahrani

{"title":"Machine edge-aware IoT framework for real-time health monitoring: Sensor fusion and AI-driven emergency response in decentralized networks","authors":"Asma Alshuhail , Amnah Alshahrani , Hany Mahgoub , Mukhtar Ghaleb , Abdulbasit A. Darem , Nojood O. Aljehane , Modhawi Alotaibi , Fahad Alzahrani","doi":"10.1016/j.aej.2025.08.030","DOIUrl":"10.1016/j.aej.2025.08.030","url":null,"abstract":"<div><div>Health monitoring systems require wider deployment to support medical institutions in their care of both chronic patients and elderly people while providing urgent emergency services. Systems based on traditional cloud infrastructure through central locations create multiple problems that include delays in performance along with failures in connectivity and restrictions in system usage, and privacy risks. The IoT framework resolves issues through real-time sensor fusion, where edge-based decision systems use lightweight AI anomaly detectors for urgent emergency choices. Several wearable biosensors analyze heart rate and blood oxygen saturation rates and body temperature information as they process fall metrics live. The edge nodes perform speedy AI-based analytics directly for essential health situations through their efficient processing capabilities to establish brief cloud system connections. In distributed networks, system-wide alerts are activated through the emergency alert protocol after distributed networks execute autonomous decision-making processes. The proposed design utilizes an adaptive mesh networking approach to ensure dependable transmission across diverse settings and support ongoing remote monitoring. A hybrid sensor fusion algorithm analyses different physiological parameters, such as ECG, SpO₂, and body temperature, to detect potentially dangerous signals and set off local emergency alarms. The figures show that the system achieves high detection accuracy (up to 95.4 %) within just 0.045 s and uses less power. The efficacy of the fall and cardiac event detection capabilities was shown in a simulation of real-life assisted living settings. The findings demonstrate that the system provides accurate, secure health data sharing in a scalable and rapid manner for dispersed IoT environments.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1349-1361"},"PeriodicalIF":6.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TinyML-enabled structural health monitoring for real-time anomaly detection in civil infrastructure 支持tinml的结构健康监测，用于民用基础设施的实时异常检测

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-05 DOI: 10.1016/j.aej.2025.08.046

Asma Alshuhail , Hanan Abdullah Mengash , Meshari H. Alanazi , Muhammad Kashif Saeed , Mukhtar Ghaleb , Mesfer Al Duhayyim , Nawaf Alhebaishi , Abdulrahman Alzahrani

{"title":"TinyML-enabled structural health monitoring for real-time anomaly detection in civil infrastructure","authors":"Asma Alshuhail , Hanan Abdullah Mengash , Meshari H. Alanazi , Muhammad Kashif Saeed , Mukhtar Ghaleb , Mesfer Al Duhayyim , Nawaf Alhebaishi , Abdulrahman Alzahrani","doi":"10.1016/j.aej.2025.08.046","DOIUrl":"10.1016/j.aej.2025.08.046","url":null,"abstract":"<div><div>SHM is an essential requirement to maintain civil infrastructure safety while extending its operational lifespan, including bridges, buildings, and dams. The conventional SHM systems need centralized data processing together with high-power sensors, even though they remain expensive, while needing significant amounts of energy and are not appropriate for areas lacking resources or infrastructure. The TinyML-based SHM (TSHM) system performs edge computing and machine learning-based computations which resulting in real-time structural integrity analysis with low power consumption. This work proposes a scalable TinyML-based SHM framework capable of real-time anomaly detection using low-power edge devices. The integrated system utilizes inexpensive accelerometers together with strain gauges and environmental sensors for the continuous acquisition of real-time data that includes vibration patterns, deformations, and environmental factors such as temperature and humidity. A resource-conserving anomaly detection model operates on edge devices to monitor and identify structural defects as well as damage in real-time. TSHMS achieves device-based critical decisions at minimal delay through the unification of structural dynamics principles with real-time sensor information and without needing cloud-based processing. The developed system performs structural anomaly detection with 92 % accuracy when compared to ordinary SHM systems while using 40 % less energy. The study illustrates how TinyML technology enables effective and sustainable structural health monitoring of civil infrastructure through AI-based decentralized operations with reduced energy needs.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1340-1348"},"PeriodicalIF":6.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

YOLO-ARM: An enhanced YOLOv7 framework with adaptive attention receptive module for high-precision robotic vision object detection YOLO-ARM：一种带有自适应注意接收模块的增强YOLOv7框架，用于高精度机器人视觉目标检测

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-05 DOI: 10.1016/j.aej.2025.09.001

Fuzhi Wang, Changlin Song

{"title":"YOLO-ARM: An enhanced YOLOv7 framework with adaptive attention receptive module for high-precision robotic vision object detection","authors":"Fuzhi Wang, Changlin Song","doi":"10.1016/j.aej.2025.09.001","DOIUrl":"10.1016/j.aej.2025.09.001","url":null,"abstract":"<div><div>This study addresses the difficulties of low detection precision, poor real-time performance, and poor model generalization in robotic vision systems under adverse circumstances through the proposition of an improved object recognition scheme based on a better convolutional neural network (CNN). To address these ends, YOLOv7-improved architecture is proposed, referred to as YOLO-ARM, which employs two new modules: the Adaptive Attention Receptive Module (ARM) and the Convolutional Block Attention Module (CBAM). ARM enhances feature extraction by adjusting the dynamic receptive field and multi-scale feature fusion, whereas CBAM improves feature maps by using channel and spatial attention procedures to improve the attention of the model towards critical features. The contributions of this paper involve the combination of ARM and CBAM in YOLOv7 to enhance the capacity of the model for handling scale changes, occlusions, and clutters. ARM module leverages group convolutions, squeeze-and-excitation blocks, and depth-wise convolutions for strengthening feature discrimination, while CBAM leverages channel and spatial attention in order to boost respective features. The proposed YOLO-ARM model outperforms other models on the MS COCO dataset, with an F1-score of 98.60 %, precision of 97.997 %, and accuracy of 99.727 %.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1326-1339"},"PeriodicalIF":6.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bifurcation and stability analysis of exact solution of couple stress fluid creeping flow in a Darcy porous slit 达西多孔缝中耦合应力流体蠕变精确解的分岔及稳定性分析

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-04 DOI: 10.1016/j.aej.2025.08.048

Mohamed R. Eid , Essam M. Elsaid , Awatif J. Alqarni , Azza M. Algatheem , Hany A. Hosham

{"title":"Bifurcation and stability analysis of exact solution of couple stress fluid creeping flow in a Darcy porous slit","authors":"Mohamed R. Eid , Essam M. Elsaid , Awatif J. Alqarni , Azza M. Algatheem , Hany A. Hosham","doi":"10.1016/j.aej.2025.08.048","DOIUrl":"10.1016/j.aej.2025.08.048","url":null,"abstract":"<div><div>In this paper, the investigation examines the problem of creeping flow of a non-Newtonian couple-stress fluid through a linear porous-walled slit within a Darcy porous material. A method uses similar shapes made by changing coordinates and making complex equations simpler to find clear formulas for the flow variables, leading to an exact solution to the nonlinear field equations. We use dynamical system theory and nonlinear stability analysis to establish a systematic framework for analyzing and controlling creeping flows. This framework allows for a more in-depth understanding of their stability and bifurcations, as well as a thorough exploration of the whole phase space while taking into account the interactions between various flow modes. As a novel result, it shows that the identification of homoclinic and heteroclinic orbits involving several distinct saddle stagnation points causes a qualitative change within the attraction basin, resulting in a trapping zone. The analytical results indicated the absence of bubbles or trapping in the wall boundaries and clarified the determination of the maximum and minimum retention limits. The results also showed that changes and improvements from earlier studies can be used in microfiltration devices, biological porous membranes, and energy transfer systems that deal with non-Newtonian fluids.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1299-1313"},"PeriodicalIF":6.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MSVDNet: A multi-scale vehicle detection network for target detection in autonomous driving MSVDNet：一种用于自动驾驶目标检测的多尺度车辆检测网络

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-04 DOI: 10.1016/j.aej.2025.08.025

Bingshuo Li , Xiuhao Hu , Lan Zhang , Qian Li , Jian Hu

{"title":"MSVDNet: A multi-scale vehicle detection network for target detection in autonomous driving","authors":"Bingshuo Li , Xiuhao Hu , Lan Zhang , Qian Li , Jian Hu","doi":"10.1016/j.aej.2025.08.025","DOIUrl":"10.1016/j.aej.2025.08.025","url":null,"abstract":"<div><div>With the development of new energy vehicle technology, the demand for target detection in autonomous driving scenarios has grown. Synthetic aperture radar image technology combined with deep learning can replace traditional remote sensing target recognition. However, detecting objects in SAR images for autonomous driving faces challenges like small vehicle targets and varying scales. To address these, this paper proposes MSVDNet, a method based on lightweight YOLOv5 for better multi-scale object detection in SAR images. It constructs two key modules: a cross-stage multi-scale receptive field feature extraction module with enhanced feature representation capability, and a feature adaptive fusion pyramid module with learnable fusion coefficients. Compared with existing methods, MSVDNet shows significant improvements. Experimental results on SSDD and Berkeley DeepDrive datasets demonstrate its superiority: it achieves 61.1 % AP, which is higher than OTA’s 59.1 % and outperforms YOLOv5s. With 24.5 GFLOPs, it reduces computational load by 29 % compared to the Res2Net baseline. Notably, it enhances small-target detection with 55.4 % APS, which is 3.3 % higher than YOLOv5s, while enabling real-time inference at 24.2 ms on embedded hardware.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1314-1325"},"PeriodicalIF":6.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of nonlinear dynamics in nonlinear electrical transmission line networks: Exact solutions, fractional analysis and new discoveries 非线性动力学在非线性输电线路网络中的应用：精确解、分数分析和新发现

IF 6.8 2区工程技术

alexandria engineering journal Pub Date : 2025-09-04 DOI: 10.1016/j.aej.2025.08.042

Xinchen Liang, Peng Guo, Jianming Qi

{"title":"Application of nonlinear dynamics in nonlinear electrical transmission line networks: Exact solutions, fractional analysis and new discoveries","authors":"Xinchen Liang, Peng Guo, Jianming Qi","doi":"10.1016/j.aej.2025.08.042","DOIUrl":"10.1016/j.aej.2025.08.042","url":null,"abstract":"<div><div>This study focuses on nonlinear electrical transmission line networks (NETNs), exploring their dynamics via high-order modeling and fractional-order analysis. It first uses the extended tanh method to solve the quartic nonlinear voltage equation of multi-coupled discrete networks, yielding exact solutions like multi-peak and parabolic solitons. Fractional-order operators significantly regulate signal distribution, amplitude, and propagation; dispersive element Cs decisively affects voltage amplitude and compensation (e.g., peak rising linearly as Cs increases from 20 to 60 pF). Differences between fractional-order operators (e.g., Conformable, Riemann–Liouville) in impacting system potential are negligible. Via 3D phase portraits, Lyapunov exponents (first quantifying chaos), and Runge–Kutta–Nyström method (errors <span><math><mrow><mo><</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>9</mn></mrow></msup></mrow></math></span>), it reveals signal distortion from modulation instability. Compared to prior second-order models, it supplements high-order stability analysis via quartic equations, offering a precision-efficiency adjustable theoretical toolchain for aerospace power anti-interference and high-voltage transmission parameter optimization.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1258-1278"},"PeriodicalIF":6.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0