Engineering Science and Technology-An International Journal-Jestech最新文献

{"title":"Modeling and performance evaluation of filtering components for switch-mode power supplies based on piezoelectric effect","authors":"Tao Zhang,&nbsp;Wei Yan,&nbsp;Mengxia Zhou","doi":"10.1016/j.jestch.2025.102060","DOIUrl":"10.1016/j.jestch.2025.102060","url":null,"abstract":"<div><div>Traditional passive electromagnetic interference (EMI) filters typically need to use common-mode chokes with high inductance values to ensure that switch-mode power supplies (SMPSs) meet relevant electromagnetic compatibility (EMC) standards. This approach not only increases the weight and size of SMPSs but also reduces their power density. To address these issues, this article proposes a method for modelling and performance evaluation of filtering components for SMPSs based on piezoelectric effect. Piezoelectric filtering components (PFCs) possess low impedance properties at their resonant frequency, while outside of resonance, their impedance is similar to that of a capacitor. As a result, PFCs can not only serve as replacements for traditional interference suppression capacitors (e.g., Y-capacitors) used in passive EMI filters but also effectively suppress interference peaks at specific frequencies. Based on the impedance properties of PFCs, this article proposes a resonant frequency design model. This model can precisely tune the multiple resonant frequencies of PFCs by adjusting their shapes and sizes, thereby leveraging the resonant properties of PFCs to selectively suppress interference peaks at multiple specific frequencies in SMPSs. This article applies the PFCs to the flyback converter and investigates their conducted EMI suppression effectiveness. According to the measurement results, compared to the Y-capacitors, the PFCs reduce the weight and volume of the passive EMI filter by 88.72% and 90.71%, respectively. The experimental results indicate that, based on the model developed in this article, using PFCs as a filtering component can lead to a lighter and more compact filter design.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102060"},"PeriodicalIF":5.1,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833594","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":"Rapid control prototyping of sensorless control system for PMSM based on multi-agent reinforcement learning and fractional order sliding mode control","authors":"Marcel Nicola ,&nbsp;Claudiu-Ionel Nicola ,&nbsp;Dan Selișteanu ,&nbsp;Dorin Șendrescu","doi":"10.1016/j.jestch.2025.102054","DOIUrl":"10.1016/j.jestch.2025.102054","url":null,"abstract":"<div><div>Based on the overall Field Oriented Control (FOC) control strategy of a Permanent Magnet Synchronous Motor (PMSM), a flexible and efficient control system architecture is developed in this work to achieve superior control performance. Sliding Mode Control (SMC) laws are utilized for both the inner and outer loop, but the typical cascade control characteristic of the system is maintained. Thus, the inner loop (IL) control laws are designed to provide increased flexibility by using fractional order (FO) computation and a response speed that is an order of magnitude higher than that of the outer loop (OL). The optimization of the tuning parameters of these controllers is performed by a computational intelligence (CI) algorithm, more specifically the Improved Grey Wolf Optimizer-Cuckoo Search Optimization (IGWO-CSO). The minimization of the computation time in the implementation of control algorithms is achieved by using a neural network (NN) that estimates the derivative value of the sliding surface in the structure of the SMC type speed controller. A term is added to the control law to cancel global perturbations of the system model, estimated with a Disturbance Observer (DO). Mitigation of the numerical stability problems of the derivative computation is achieved by using a Levant observer tracking differentiator. The use of Multi-Agent Reinforcement Learning (MARL) based on three properly trained Twin-Delayed Deep Deterministic (TD3) RL agents, which provide correction signals overlapping the control signals, contributes to the superior performance of the sensorless control system of the PMSM (SCS-PMSM). These include both parametric robustness to parameter and load torque variations, but also the use of an adaptation law to estimate the stator resistance, which can vary significantly. The superiority of the proposed SCS-PMSM over a benchmark control system based on Proportional Integrator (PI) controllers is demonstrated by following both the Software-in-the-Loop (SIL) and Hardware-in-the-Loop Simulated-Rapid Control Prototyping (HILS-RCP) phases. The realization of an RCP for the proposed RCP SCS-PMSM at different sampling periods corresponding to the implementation in both high performance and low/medium performance Digital Signal Processors (DSPs) is achieved using a SpeedGoat Performance Real-Time Target Machine platform.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102054"},"PeriodicalIF":5.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817428","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":"Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues)","authors":"","doi":"10.1016/S2215-0986(25)00121-1","DOIUrl":"10.1016/S2215-0986(25)00121-1","url":null,"abstract":"","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"65 ","pages":"Article 102066"},"PeriodicalIF":5.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820772","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":"An active learning driven deep spatio-textural acoustic feature ensemble assisted learning environment for violence detection in surveillance videos","authors":"Duba Sriveni ,&nbsp;Dr.Loganathan R","doi":"10.1016/j.jestch.2025.102050","DOIUrl":"10.1016/j.jestch.2025.102050","url":null,"abstract":"<div><div>In this paper, a novel and robust deep spatio-textural acoustic feature ensemble-assisted learning environment is proposed for violence detection in surveillance videos (DestaVNet). As the name indicates, the proposed DestaVNet model exploits visual and acoustic features to perform violence detection. Additionally, to ensure the scalability of the solution, it employs an active learning concept that retains optimally sufficient frames for further computation and thus reduces computational costs decisively. More specifically, the DestaVNet model initially splits input surveillance footage into acoustic and video frames, followed by multi-constraints active learning based on the most representative frame selection. It applied the least confidence (LC), entropy margin (EM), and margin sampling (MS) criteria to retain the optimal frames for further feature extraction. The DestaVNet model executes pre-processing and feature extraction separately over the frames and corresponding acoustic signals. It performs intensity equalization, histogram equalization, resizing and z-score normalization as pre-processing task, which is followed by deep spatio-textural feature extraction by using gray level co-occurrence matrix (GLCM), ResNet101 and SqueezeNet deep networks. On the other hand, the different acoustic features, including mel-frequency cepstral coefficient (MFCC), gammatone cepstral coefficient (GTCC), <span><math><mrow><mi>GTCC</mi><mo>-</mo><mi>Δ</mi></mrow></math></span>, harmonic to noise ratio (HNR), spectral features and pitch were obtained. These acoustic and spatio-textural features were fused to yield a composite audio-visual feature set, which was later processed for principal component analysis (PCA) to minimize redundancy, and k-NN as part of an ensemble classifier to enhance prediction accuracy, achieving superior performance. The z-score normalization was performed to alleviate the over-fitting problem. Finally, the retained feature sets were processed for two-class classification by using a heterogeneous ensemble learning model, embodying SVM, DT, k-NN, NB, and RF classifiers. Simulation results confirmed that the proposed DestaVNet model outperforms other existing violence prediction methods, where its superiority was affirmed in terms of the (99.92%), precision (99.67%), recall (99.29%) and F-Measure (0.992).</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102050"},"PeriodicalIF":5.1,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817426","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":"Binary classification of Low-Rate DoS attacks using Long Short-Term Memory Feed-Forward (LSTM-FF) Intrusion Detection System (IDS)","authors":"Suhaila ZeinElabideen Omer ,&nbsp;Fazirulhisyam Hashim ,&nbsp;Aduwati Sali ,&nbsp;Faisul Arif Ahmad","doi":"10.1016/j.jestch.2025.102049","DOIUrl":"10.1016/j.jestch.2025.102049","url":null,"abstract":"<div><div>The data and size of networks have grown substantially due to the rapid development of the Internet and other communication techniques. This has led to the development of numerous new types of attacks, making it harder for network security to detect intrusions accurately. The goal of a Denial of Service (DoS) attack is to overwhelm a target with malicious traffic, exhausting its processing power and network bandwidth. Traditional DoS attacks rely on brute force techniques, making them easier to detect, whereas low-rate and slow attacks pose a greater threat due to their stealthy nature. These attacks target application or server resources with a prolonged trickle of traffic, requiring minimal bandwidth yet making mitigation challenging. Their low resource footprint allows them to degrade or deny service to legitimate users while remaining undetected for extended periods. This research introduces an advanced Intrusion Detection System (IDS) that utilizes a hybrid Long Short-Term Memory Feedforward (LSTM-FF) Neural Network to tackle existing challenges in detecting low-rate DoS (LR-DoS) attacks. Unlike previous models, our approach combines temporal sequence learning with feature refinement, thereby improving the detection of LR-DoS. Additionally, we incorporate automated feature selection using Random Forest, which optimizes efficiency while maintaining interpretability. For model training and evaluation, we use the CIC-DOS2017 dataset, which includes eight distinct types of LR-DoS attacks. To enhance generalizability, we also utilize the CSE-CIC-IDS2018 dataset and the newly introduced LR-HR-DDOS2024 dataset, specifically designed for Software-Defined Networking (SDN)-based environments. To address the class imbalance, we implement a stratified k-fold cross-validation strategy, ensuring robust performance across various attack scenarios. To thoroughly evaluate model performance, we adopt a comprehensive set of metrics, including accuracy, precision, recall, F1-score, specificity, False Alarm Rate (FAR), and ROC-AUC. This ensures a well-rounded validation of our approach. The model surpassed all previous state-of-the-art models with an impressive accuracy of 99.70%, precision of 99.47%, specificity of 99.97%, and an F1-score of 97.52%, all while retaining a low FAR of roughly 0.03%. The LSTM-FF approach also worked well in multi-class classification, with a 99.54% accuracy rate, 93.19% precision, 99.59% specificity, 90.28% F1 score, and 0.40% FAR.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102049"},"PeriodicalIF":5.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807384","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":"Fitting method of concrete damage Poisson’s ratio model based on Kolmogorov-Arnold network","authors":"Yu Yang,&nbsp;Jiahui Xu,&nbsp;Qiangbing Zhou,&nbsp;Shichao Kong,&nbsp;Keyi Lin","doi":"10.1016/j.jestch.2025.102052","DOIUrl":"10.1016/j.jestch.2025.102052","url":null,"abstract":"<div><div>The prediction model for the Poisson’s ratio of concrete damage is of significant importance in the field of Structural Health Monitoring (SHM). Seeking a concrete damage Poisson’s ratio prediction model that comprehensively reflects the characteristics of concrete while also being simple and accurate is a challenging task. This study proposes a combination of the Kolmogorov-Arnold Network (KAN), which can fit complex nonlinear relationships with high precision, and the Finite Element Method (FEM) to address this challenge. The research first summarizes the influencing factors of the concrete damage Poisson’s ratio model from classical theories, then uses data obtained from measurements and finite element analysis to train the KAN to develop the concrete damage Poisson’s ratio prediction model. Finally, the accuracy of the model is validated on a test set, and its performance is compared with that of Multi-Layer Perceptron (MLP) networks and classical models. The validation results indicate that the formula model trained by KAN achieves a Root Mean Square Error (RMSE) of 0.055 when predicting the damage Poisson’s ratio of actual test specimens, outperforming four classical models (RMSE ≥ 0.176). The novelty of this study lies in the innovative application of KAN in the concrete damage Poisson’s ratio prediction model, as well as the approach of combining a small amount of measured data with FEM to enhance the efficiency of generating training and testing data. This research not only validates the interpretability and accuracy of KAN but also demonstrates the practicality and effectiveness of the KAN and FEM combination method in the application of predicting the concrete damage Poisson’s ratio, making a significant contribution to the field.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102052"},"PeriodicalIF":5.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817427","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":"Bayesian curriculum generation in sparse reward reinforcement learning environments","authors":"Onur Akgün ,&nbsp;N. Kemal Üre","doi":"10.1016/j.jestch.2025.102048","DOIUrl":"10.1016/j.jestch.2025.102048","url":null,"abstract":"<div><div>This paper introduces the Bayesian Curriculum Generation Algorithm, a sophisticated approach for curriculum learning in sparse reward reinforcement learning contexts. Diverging from traditional methodologies, this algorithm utilizes Bayesian networks to dynamically create tasks by altering problem parameters, thereby impacting task difficulty. It operates independently from the core reinforcement learning algorithm, enabling compatibility with a variety of RL techniques. A notable feature of our algorithm is its capability for unsupervised task classification, utilizing a clustering process applicable to both image outputs and scalar values. This method efficiently categorizes tasks based on difficulty, circumventing the need for exhaustive training for each task. However, the effectiveness of this approach is contingent upon the presence of definable parameters within the environment and necessitates domain expertise to determine the appropriate tool, be it image output or scalar parameter analysis. The algorithm selects tasks from a curated pool corresponding to specific difficulty levels and adapts according to the agent’s performance. Successful task completion triggers the generation of more complex tasks, whereas encountering challenges results in the maintenance or minor adjustment of task complexity. This adaptive feature significantly enhances the efficiency of the learning process. Empirical evaluations conducted in various environments, characterized by maze-like structures, discrete or continuous settings, and the presence of adversarial entities hindering the agent’s mission, demonstrate the algorithm’s efficacy and its superiority over conventional methods. The Bayesian Curriculum Generation Algorithm represents a significant advancement in reinforcement learning, providing a dynamic and adaptable solution for complex learning challenges.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102048"},"PeriodicalIF":5.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807383","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":"Time–frequency ensemble network for wind turbine mechanical fault diagnosis","authors":"Haiyu Guo ,&nbsp;Xingzheng Guo ,&nbsp;Xiaoguang Zhang ,&nbsp;Fanfan Lu ,&nbsp;Chuang Liang","doi":"10.1016/j.jestch.2025.102056","DOIUrl":"10.1016/j.jestch.2025.102056","url":null,"abstract":"<div><div>Wind turbines typically operate under variable speed conditions, so the collected vibration signals are affected by non-linearity and information mixing, while also containing a large amount of noise interference. However, most existing methods extract fault features from a single domain, failing to capture the signals’ diverse and complex characteristics. To fully exploit multi-domain discriminative features under variable speed conditions, this paper proposes a time–frequency ensemble network (TFNet). First, the feature representation is improved by constructing an adaptive spectral block (ASB) using Fourier analysis, while an adaptive threshold is introduced to reduce noise interference. Second, the Transformer and Graph Convolutional Network (GCN) are combined to extract the time–frequency discriminative features of defects. Specifically. In the time domain module, the global time domain features of faults are extracted by the Transformer encoder block. In the frequency domain module, a mixhop graph convolutional network is used to extract the multi-scale frequency domain features of different neighbours, and a Multi Head Attention (MHA) mechanism is introduced to capture the intra-feature dependencies. To achieve better diagnostic results under variable speed conditions, a label smoothing algorithm is used to assist the training of the model. A case study is conducted using the WT-Planetary gearbox dataset and the XJTUSuprgear variable speed gearbox dataset as well as the CWRU Bearing dataset. The experimental results show that the proposed model has high diagnostic accuracy and strong generalisation ability compared to other fault diagnosis models.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102056"},"PeriodicalIF":5.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complex latency dynamics of biological neuron model under effect of electromagnetic induction","authors":"Ali Calim","doi":"10.1016/j.jestch.2025.102038","DOIUrl":"10.1016/j.jestch.2025.102038","url":null,"abstract":"<div><div>In this study, the effect of electromagnetic induction on spike latency dynamics in the Hodgkin–Huxley (H–H) neuron is investigated. It has been shown that the timing of first spikes is an effective information carrier and delay in the first spike contains more neuronal information compared to subsequent spikes. The first spike latency can increase significantly by stochastic perturbations, and this is known as noise delayed decay (NDD) phenomenon. On the other hand, due to micro level biophysical activities, particularly transport of ions across the cell membrane causes a temporary and changing electromagnetic field, which forms a feedback contribution to that neuron. Here, we aim to understand the effects of induction current produced by such electromagnetic field on the first spike timing behavior in a single stochastic Hodgkin–Huxley model neuron. To achieve this aim, we demonstrate the dynamic behavior of stochastic neuron regarding spike latency depending on channel noise intensity at varying signal frequency. We show that NDD behavior apparently emerges at critical suprathreshold frequencies. Our results have also shown that electromagnetic induction can decrease the first spike latency and that it becomes easier for the neuron exposed to relatively higher electromagnetic fields to emit reasonably rapid firings. This implies that electromagnetic induction can regulate the functional role of spike latency and remove undesired impacts of NDD.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"66 ","pages":"Article 102038"},"PeriodicalIF":5.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental investigation of the effect of nano fluid use on heat transfer in unmanned aircraft cooling system with different types of wing geometry","authors":"Abdulsamed Güneş ,&nbsp;Beytullah Erdoğan ,&nbsp;Gülşah Çakmak","doi":"10.1016/j.jestch.2025.102059","DOIUrl":"10.1016/j.jestch.2025.102059","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) have performed critical tasks such as air dominance, national security, wildlife surveillance, damage detection studies after natural disasters, early intervention in forest fires, management activities, explosions, and logistics. UAVs, which perform these critical tasks and whose importance has been increasing in the world in recent years, experience a loss of thermal efficiency in their cooling systems at critical times during flight. In order to optimize cooling mechanisms for UAVs, this study aims to redesign existing cooling fins. In addition, it is planned to use nanofluids instead of traditional coolants in these radiators. In addition to the increased cooling performance with the use of nanofluid, it has been determined that this effect is further increased by the use of new design parts (Radiator-F1, Radiator-F2, Radiator-F3) consisting of louver type fin structures with different fin geometries in number and pattern compared to standard flat fins (Radiator-S). In these newly designed cooling systems were tested at flow rates of 20 and 22 lt/min, a temperature of 70 °C, and an inlet pressure of 0.2 bar are focused on increasing the cooling efficiency of the coolers. Experiments were carried out on standard and newly designed radiators using Al₂O₃, ZnO and CuO nanofluids at 0.3 % volumetric concentration for thermal performance measurement. The heat transfer in Radiator-S using pure water was calculated as 9.02 kW. The heat transfer amount in the newly designed Radiator-F1 using pure water was the highest and increased by approximately 23 %. The heat transfer increase in Radiator-F1 using CuO nanofluid was the highest and was determined to be approximately 38 % compared to using pure water in Radiator-S. Thermal conductivity and viscosity ratios increased compared to pure water. The highest increase in thermal conductivity was approx 18 % in ZnO nanofluid and viscosity was approx 16 % in Al₂O₃ nanofluid.</div><div>The improvement resulting from the findings increases the operational capabilities of the (UAVs). In addition, the lightness resulting from a more compact system indirectly increases the flight duration. These results demonstrate significant improvements in cooling performance for UAV applications.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"65 ","pages":"Article 102059"},"PeriodicalIF":5.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}