alexandria engineering journal最新文献

{"title":"Enhancing frequency stability in power systems through DO-optimized FOPI-PIDA-controlled STATCOM for wind energy integration","authors":"Kareem M. AboRas ,&nbsp;Abdallah Fouad ,&nbsp;Hossam Kotb ,&nbsp;Hesham B. ElRefaie ,&nbsp;Ahmed H. Yakout","doi":"10.1016/j.aej.2025.06.002","DOIUrl":"10.1016/j.aej.2025.06.002","url":null,"abstract":"<div><div>This research endeavors to enhance the frequency stability of power systems connected to wind energy under various disturbances, such as load variations and generator outages due to grid faults, by dynamically regulating reactive power injection. The study introduces an advanced control strategy employing a Fractional Order Proportional-Integral with Proportional-Integral-Derivative-Acceleration (FOPI-PIDA) controller, integrated with a static synchronous compensator (STATCOM). The controller is meticulously optimized using the Dandelion Optimizer (DO), a cutting-edge metaheuristic algorithm selected for its exceptional convergence and robustness. The proposed DO-tuned FOPI-PIDA-controlled STATCOM effectively stabilizes system frequency during load shifts or generator failures, maintaining frequency deviations within acceptable limits. The controller’s efficacy is rigorously validated through time-domain simulations in MATLAB/SIMULINK across two benchmark systems: the Kundur two-area test system and the IEEE 39-bus test system, both coupled with wind energy integration. Stability metrics such as Maximum Overshoot (M.O.), Maximum Undershoot (M.U.), and Steady-State Frequency (S.S.) are assessed to quantify performance. Comparative analysis highlights the superior frequency regulation capabilities of the DO-optimized FOPI-PIDA-controlled STATCOM compared to the conventional PIDA-based STATCOM, which was previously tuned using the Marine Predator Algorithm (MPA). For example, in the IEEE 39-bus system with wind integration, during a 20 % load loss, the proposed controller limits the M.O. to 60.47 Hz and the S.S. to 60.34 Hz, outperforming the MPA-tuned controller’s 60.51 Hz and 60.36 Hz. Similarly, during the disconnection of Generator 2, the proposed controller sustains the M.U. at 59.62 Hz and the S.S. at 59.70 Hz, surpassing the PIDA controller's 59.57 Hz and 59.66 Hz. These results affirm the robustness and adaptability of the proposed control scheme for ensuring reliable frequency regulation in wind-integrated power systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 192-204"},"PeriodicalIF":6.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307537","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}

{"title":"Feature enhancement based aero-engine lubricant consumption prediction: A BiTCN-BiGRU-attention approach","authors":"Qifan Zhou ,&nbsp;Bosong Chai ,&nbsp;Yingqing Guo ,&nbsp;Hao Wu ,&nbsp;Kun Wang ,&nbsp;Yun Ye","doi":"10.1016/j.aej.2025.06.020","DOIUrl":"10.1016/j.aej.2025.06.020","url":null,"abstract":"<div><div>The aero-engine lubrication system is vital for lubricating, protecting, and cleaning mechanical components under diverse conditions. However, long-term lubricant consumption—due to factors like pipeline damage, bearing cavity leakage, and component fatigue—can degrade system and engine performance. Accurate prediction of lubricant consumption is thus essential for proactive maintenance and improved reliability. To overcome the limitations of existing methods that rely solely on historical data and single-level feature extraction, this paper proposes a multivariate regression algorithm: Bilateral Tree Convolutional Network–Bidirectional Gated Recurrent Unit–Attention (BiTCN-BiGRU-Attention), further optimized by random forest. BiTCN captures bidirectional temporal features to enrich semantics; BiGRU enhances temporal modeling by removing directional constraints; and Attention improves prediction by refining feature weighting. Experiments show the proposed method outperforms baselines, demonstrating strong potential for integration into aero-engine health management systems.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 137-167"},"PeriodicalIF":6.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297166","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}

{"title":"A CrossInformer model based on dual-layer decomposition and interpretability for short-term electricity load forecasting","authors":"Hongjie Li ,&nbsp;Yirui Tang ,&nbsp;Dayang Liu","doi":"10.1016/j.aej.2025.05.089","DOIUrl":"10.1016/j.aej.2025.05.089","url":null,"abstract":"<div><div>Short-term electricity load forecasting is a critical issue in power system management, and its accuracy is essential for the stable operation of the grid and energy dispatch. This paper proposes a CrossInformer model based on dual-layer decomposition and interpretability for predicting short-term electricity load. Firstly, the CrossInformer employs an intelligent optimization algorithm for feature selection, which enhances the model’s training efficiency and prediction accuracy. Secondly, an ICEEMDAN-RLMD dual-layer decomposition method is adopted to denoise the load data, thereby reducing the influence of noise on the prediction results. Subsequently, the CrossInformer architecture integrates multi-granularity patch inputs, ProbSparse Attention, self-attention distillation, and the HiLo attention mechanism to improve the model’s capability in capturing both long-term and short-term dependencies in time series data. Finally, to enhance the model’s interpretability, the SHAP method is used to quantify the contribution of each feature to the prediction outcomes. Experimental results demonstrate that the proposed method outperforms traditional approaches in electricity load forecasting, offering superior prediction accuracy and stability.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 117-127"},"PeriodicalIF":6.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291550","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}

{"title":"MusDiff: A multimodal-guided framework for music generation","authors":"Lili Liu ,&nbsp;Rui Gong ,&nbsp;Yubo Yang","doi":"10.1016/j.aej.2025.05.053","DOIUrl":"10.1016/j.aej.2025.05.053","url":null,"abstract":"<div><div>Music generation has become a key area in artificial intelligence, achieving significant progress in recent years. However, current research focuses primarily on general music tasks, with limited support for ethnic music. Moreover, the lack of multimodal guidance, such as text and image inputs, restricts generative models in understanding complex semantics and producing high-quality music. To address these limitations, we propose MusDiff, a multimodal music generation framework that combines text and image inputs to enhance music quality and cross-modal consistency. MusDiff is based on a diffusion model architecture, integrating IP-Adapter and KAN (Kolmogorov–Arnold Network) optimizations to improve feature fusion and modality alignment. Additionally, we introduce a new multimodal dataset, MusiTextImg, which includes diverse music categories, such as ethnic and modern styles, with annotations for text, image, and music modalities. We also extend the MusicCaps dataset by adding matched image pairs to text descriptions, further supporting multimodal research. Experimental results demonstrate that MusDiff outperforms existing methods on benchmark datasets (MusiTextImg and MusicCaps), excelling in realism, detail fidelity, and multimodal alignment. MusDiff not only sets a new performance standard for multimodal music generation but also opens new research directions in the field of multimodal generation.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 128-136"},"PeriodicalIF":6.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291515","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}

{"title":"Leveraging ensemble learning with metaheuristic optimization algorithms for an intelligent cyberattack defense framework in an IoT environment","authors":"Hend Khalid Alkahtani ,&nbsp;Mashael M. Asiri ,&nbsp;Rakan Alanazi ,&nbsp;Mohammed Mujib Alshahrani ,&nbsp;Fahad Alzahrani ,&nbsp;Shaymaa Sorour ,&nbsp;Mesfer Al Duhayyim","doi":"10.1016/j.aej.2025.06.015","DOIUrl":"10.1016/j.aej.2025.06.015","url":null,"abstract":"<div><div>Cybersecurity continues to be a significant problem for some industries on the Internet, as the number of security breaches is increasing over time. It is identified that many zero-day threats are continuously developing due to the addition of several protocols, mainly from the Internet of Things (IoT). The majority of these attacks are smaller versions of formerly known cyberattacks. IoT cybersecurity aims to decrease cybersecurity risk for companies and users by securing privacy and assets. The expansion of automatic devices for cyber threat classification and detection utilizing artificial intelligence (AI) and deep learning (DL) devices has become necessary for accomplishing security in IoT environments. Due to their notable performance, DL-based methods are essential to successfully reducing security problems associated with IoT gadgets. This article presents a Leveraging Ensemble Learning and Metaheuristic Optimization Algorithms for Intelligent Cyber Attack and Defense Framework (LELMOA-ICADF) model in IoT networks. The main intention of the LELMOA-ICADF model is to deliver an efficient method using advanced ensemble models for enhancing IoT cybersecurity. Initially, the min-max normalization is employed in the data pre-processing stage for transforming input data into a structured format. The cat swarm optimization (CSO) technique is utilized for the feature selection process to choose the most relevant and significant features from the dataset. Furthermore, ensemble models such as the bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) are employed for the attack classification process. Finally, the artificial bee colony (ABC) method is used for parameter tuning to improve the classification performance of ensemble classifiers. The experimental assessment of the LELMOA-ICADF approach is performed under Edge-IIoT and ToN-IoT datasets. The performance validation of the LELMOA-ICADF approach portrayed a superior accuracy value of 99.40 % and 99.50 % under the dual datasets.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 103-116"},"PeriodicalIF":6.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280018","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}

{"title":"A novel approach for enhancing personalized motion training and performance evaluation with Attn-MVAGCN","authors":"Wensuo Lian ,&nbsp;Yuxin He ,&nbsp;Jindong Xu","doi":"10.1016/j.aej.2025.05.065","DOIUrl":"10.1016/j.aej.2025.05.065","url":null,"abstract":"<div><div>With the continuous development of intelligent sports training, high-precision posture estimation technology plays a vital role in personalized guidance and sports effect evaluation. However, existing posture estimation methods usually have difficulty in balancing high precision and computational efficiency in complex sports scenes. To this end, this paper proposes a new posture estimation model Attn-MVAGCN, which combines the MobileViT module for image feature extraction, the Adaptive-GCN module for joint relationship modeling, and introduces the attention mechanism to enhance the capture of key posture information. Through experiments on multiple standard datasets (COCO-WholeBody, MPII Human Pose, and NTU RGB+D), the results show that Attn-MVAGCN reduces MPJPE (joint position error) by 10.4% (35.2 mm) and improves AP (average precision) by 6.7% (72.5%), while the inference speed reaches 70.5 FPS, which is significantly better than the existing baseline model. Personalized training experiments further verify the effectiveness of the model in dynamically optimizing athlete posture estimation and improving motion standardization. The advantages of the Attn-MVAGCN model in computational efficiency and real-time performance enable it to run efficiently on mobile devices and smart wearable devices. It has the potential for application on resource-constrained devices and has broad application value in fields such as sports rehabilitation and smart fitness.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 53-66"},"PeriodicalIF":6.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270752","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}

{"title":"Context-aware multi-label classification: Synergistic effects of masked attention and graph neural networks","authors":"Xiaoyan Liu ,&nbsp;Ling Yang ,&nbsp;Yong Yang","doi":"10.1016/j.aej.2025.05.084","DOIUrl":"10.1016/j.aej.2025.05.084","url":null,"abstract":"<div><div>We propose the MLC-GCN for remote sensing image classification. This model utilizes attention mechanisms to enhance the classification of multi-label remote sensing images by effectively capturing complex spatial and contextual features. The network integrates several key modules: feature extraction using ResNet-50, context processing, category feature extraction and fusion, label semantic mining, and a dual graph network. Experiments on three datasets — AID, UCM, and MLRSNet — show that MLC-GCN outperforms existing models. On the AID dataset, it achieves a 5.42% improvement in mAP and a 1.43% increase in accuracy. On the UCM dataset, MLC-GCN improves mAP by 0.62% and Precision by 6.45%. On the MLRSNet dataset, the network ranks in the top three for several performance metrics, including mAP, Micro-Precision, and Weight-Precision. The model’s ability to process complex remote sensing images is validated through extensive experiments and ablation studies, highlighting its robustness and efficiency.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 77-89"},"PeriodicalIF":6.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280014","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}

{"title":"Collision of solitons in neuronal microtubules with dipole–dipole interaction","authors":"Yaseen M. Lone,&nbsp;Parasuraman E.","doi":"10.1016/j.aej.2025.05.021","DOIUrl":"10.1016/j.aej.2025.05.021","url":null,"abstract":"<div><div>In this paper, we investigate the collision dynamics of solitons in neuronal microtubules. We have used a continuum equation of the type nonlinear Schrödinger equation with the presence of dipole–dipole interaction and electric field. The continuous nonlinear Schrödinger equation serves as an effective framework for describing soliton solutions and we have developed one and two soliton solutions using the Hirota bilinear method. We examine the behaviour of solitons in neuronal microtubules and extend our analysis to the collision dynamics of two solitons under varying dipole–dipole interactions. Our results show an intricate interplay between the collision dynamics of solitons and the effect of dipole–dipole interaction in neuronal microtubules. Further, the stability of the soliton solution is discussed with the help of the Fourier collocation method. This work is important for understanding the complex interaction between solitons and also provides crucial insights into microtubule properties. The effect of dipole–dipole interactions on these collisions may play a significant role in the propagation of coherent quantum states in the brain.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 90-102"},"PeriodicalIF":6.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280015","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}

{"title":"Efficient 3D human pose estimation for IoT-based motion capture using Spatiotemporal Attention","authors":"Chen Zhang ,&nbsp;Luyan Li ,&nbsp;Zhihao Zhang ,&nbsp;Yan Zhou","doi":"10.1016/j.aej.2025.05.067","DOIUrl":"10.1016/j.aej.2025.05.067","url":null,"abstract":"<div><div>With the growing demand for efficient and accurate 3D human pose estimation in fields such as virtual reality, human–computer interaction, sports analysis, and IoT-based monitoring, current Transformer-based solutions face challenges due to their quadratic computational cost as the number of joints and frames increases. To address this, we propose a 3D pose estimation network that combines Spatio-Temporal Criss-Cross Attention (STC) and a central point attention mechanism. The STC module splits the input features into spatial and temporal parts, applying self-attention to capture joint relationships within spatial frames and track dependencies across temporal frames. The central point attention mechanism uses a voxel network to refine pose regression within the central point range. By stacking multiple STC modules and introducing structure-enhanced positional embedding (SPE), our method captures spatiotemporal features and local structures. Experiments on the Human3.6M and MPI-INF-3DHP datasets show our approach achieves state-of-the-art accuracy with low computational cost, making it ideal for IoT-based monitoring and real-world applications requiring efficient pose estimation.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 67-76"},"PeriodicalIF":6.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280012","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}

{"title":"Progress in mechanoluminescent sensors for human motion monitoring: Materials and applications","authors":"Feng Li ,&nbsp;Yu Ding ,&nbsp;Bo Zhao","doi":"10.1016/j.aej.2025.06.006","DOIUrl":"10.1016/j.aej.2025.06.006","url":null,"abstract":"<div><div>The ability to monitor and analyze human motion has become increasingly critical across various fields, from healthcare to sports science. Among emerging sensing technologies, mechanoluminescent (ML) sensors have demonstrated unique advantages through their ability to convert mechanical energy directly into visible light emission without external power sources. This review comprehensively examines recent advances in ML sensors for human motion monitoring, focusing on material development, device architectures, and practical applications. We analyze the fundamental mechanisms of mechanoluminescence, including elastic deformation, plastic deformation, and friction-induced luminescence, which form the theoretical foundation for sensor design. The review details significant progress in developing high-performance ML materials, ranging from traditional inorganic compounds like ZnS:Cu and SrAl₂O₄:Eu²⁺,Dy³ ⁺ to novel organic systems and composite materials. We explore innovative device architectures and fabrication strategies that have enabled the creation of flexible, wearable sensors capable of detecting both subtle physiological movements and larger-scale motions. The integration of ML sensors with various substrate materials and their implementation in practical applications, such as healthcare monitoring and human-machine interfaces, are discussed in detail. While highlighting the remarkable progress in this field, we also address current challenges, including sensitivity optimization, signal processing, and long-term stability, providing insights into future research directions for advancing ML sensor technology in human motion monitoring applications.</div></div>","PeriodicalId":7484,"journal":{"name":"alexandria engineering journal","volume":"129 ","pages":"Pages 1-15"},"PeriodicalIF":6.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253733","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}