Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Eleni Fotopoulou, Anastasios Zafeiropoulos, Eleftheria Arkadopoulou et al.

The positive impact of Social and Emotional Learning (SEL) activities in schools is widely recognized in various sectors, including mental health, social relationships, and academic performance of students. This impact can be efficiently realized and catalyzed based on the development of tools that help teachers to effectively and consistently apply SEL in classrooms. Such tools can leverage emerging Artificial Intelligence (AI) technologies, including Generative AI (GenAI) and Agentic AI, and Knowledge Graphs to enable their ease of adoption and use by teachers and provide accurate and context-aware guidance. In this perspective, this manuscript presents an Agentic Graph Retrieval-Augmented Generation (RAG) approach to support educators to apply methodologies for SEL. The approach is materialized through an agent, called MySELAgent, which synthesizes information and provides recommendations around SEL, taking advantage of GenAI and following an Agentic AI approach. MySELAgent is interoperable with third-party psychometric tools, enabling assessment and monitoring of the social and emotional competencies of students. It is able to suggest evidence-based targeted SEL activities and create holistic SEL intervention programs in the form of learning pathways, which are based on teachers’ pReferences and students’ emotional needs. A detailed evaluation of the implemented techniques is presented, analyzing the performance and effectiveness of MySELAgent on retrieving information and creating accurate responses based on the use of different Large Language Models (LLMs).

Dongdong Song, Lijun Geng, Zhendong Liu et al.

The on-load tap changer (OLTC), widely used as a voltage regulation device in power systems, requires regular assessment and maintenance to ensure reliable operation and avoid adverse impacts on the power system. These assessments encompass key parameters such as transition waveform, transition time, three-phase synchronization, and transition resistor, along with the operational status of the mechanical structure. However, the maintenance process, typically conducted offline, can diminish equipment efficiency. Moreover, the accuracy of some parameter measurements needs improvement. To bolster equipment reliability and refine detection methods for critical parameters, this study explores online detection techniques for key switching parameters of the OLTC body. This paper proposes a method to identify these key parameters in the switching circuit, using coordinate transformation as the core algorithm. We used a specific vacuum OLTC device for our research, conducted theoretical analyses, developed a simulation model to validate the proposed method for identifying OLTC switching parameters, and further built a test platform to verify the algorithm’s effectiveness. The results show a close alignment between simulation and actual measurement outcomes. Each switching process interval conforms to the manufacturer’s design specifications for the equipment, with the transition resistor parameter calculation accuracy ranging from approximately 95.39% to 100%. Similarly, the tap winding voltage calculation accuracy is between approximately 91.52% and 100%, satisfying engineering requirements and enterprise standard [1]. This method provides a basis for optimizing the measurement of working parameters in OLTC equipment and aims to offer ideas for the next step of prototype development.

Saba Sanami, Amir G. Aghdam

Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an unsupervised deep learning method for detecting anomalies in aero-engine multivariate sensor data, using a Gaussian mixture as the prior distribution of the latent space. The proposed method aims to minimize the Fisher divergence between the true and the modeled data distribution in order to train an autoencoder that can capture the normal patterns of aero-engine behavior. The Fisher divergence is robust to model uncertainty, meaning it can handle noisy or incomplete data. The Fisher autoencoder also has well-defined latent space regions, which makes it more generalizable and regularized for various types of aero-engines as well as facilitates diagnostic purposes. The proposed approach improves the accuracy of anomaly detection and reduces false alarms. Simulations using the CMAPSS dataset demonstrate the model's efficacy in achieving timely anomaly detection, even in the case of an unbalanced dataset.

Daniel N. Wilke

This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance.

Chien-Yeah Seng

Superallowed $0^+\rightarrow 0^+$ transitions between $T=1$ nuclei have been a perfect avenue avenue for determining the Cabibbo-Kobayashi-Maskawa matrix element $V_{ud}$, which imposes powerful constraints on physics beyond the Standard Model at low energies. For a long time, the precision of $V_{ud}$ has been limited by uncertainties in radiative corrections that arise from non-perturbative strong interaction physics at both the hadronic and nuclear levels. In this talk, I will describe some recent efforts to pin down these corrections by combining dispersive analysis with experimental data, lattice QCD, and nuclear many-body calculations.

Johan Cederbladh, Loek Cleophas, Eduard Kamburjan et al.

With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous, time-consuming, and resource-intensive experiments. In this work, we present a framework for managing experiments on digital and/or physical assets with a focus on case-based reasoning with domain knowledge to reuse experimental data efficiently by deciding whether an already-performed experiment (or associated answer) can be reused to answer a new (potentially different) question from the engineer/user without having to set up and perform a new experiment. We provide the general architecture for such an experiment manager and validate our approach using an industrial vehicular energy system-design case study.

Min Zhu, Haile Zhang, Lei Feng et al.

With technological advancements, the operational space expands into outer space. As a result, the space-based weapon “Rods from Gods” was developed, which is expected to become an important strategic force in future warfare due to its extraordinary lethality. Therefore, evaluating the destructive efficacy of this space-based weapon is necessary. Based on the available information, the space-based weapon “Rods from Gods” was reported to have a weight of several tons, a length of 6.1 m, and a diameter of 0.3 m. This study aims to calculate the penetration speed of the weapon and assess its destructive capability using software and the concept of TNT equivalent. This study primarily focuses on constructing two models: the projectile orbital and damage assessment models. In the projectile model, we used MATLAB function ode45 and developed a custom code to calculate the penetration speed of the space-based weapon “Rods from Gods,” which was determined to be 3108.00 m/s. Additionally, we used FlightGear to validate the accuracy of the results. Considering the high speed of the weapon, traditional physical methods are inadequate for evaluating the damage caused. Hence, we used the software to visualize the damage outcomes of the weapon and assess them using the concept of TNT equivalent. Furthermore, we developed two detailed models: the projectile penetration and TNT equivalent evaluation models. In the projectile penetration model, the software Ansys/le-PrePost aided us in visualizing the damage outcomes. In the TNT equivalent evaluation model, we plotted the change in the penetration depth, which measured 20.2573 m for the weapon. Additionally, we plotted the change in TNT weight, determining the equivalent TNT weight of the weapon to be 32.3289 tons. After the analysis of this study, the destructive effect of space-based kinetic energy weapon is great, and compared with nuclear weapon, there is no nuclear radiation, so it is important to evaluate the destructive effect of space-based kinetic energy weapon.

Humam M. Abdulsahib, Foad Ghaderi

Profit maximization and risk mitigation require good financial market predictions. Financial markets have a correlated nature, which means that there are some shared patterns between them; therefore, learning about one market might help understand the behavior of others. End-to-end training techniques have proven successful in financial markets, but they have flaws, such as picking up noise and failing to account for the complicated relationships across markets. We present a promising model for predicting financial markets using the correlation between the two markets, which draws inspiration from the recent progress in disentanglement learning. This model learns to disentangle representations of features shared between markets from specific representations, and removes features that cause interference. We utilized a dilated convolutional neural network as an encoder to extract features while using self-attention and cross-attention to capture specifics and shared patterns. Our model uses Dynamic Time Warping (DTW) to minimize the similarity between specific and shared patterns. It also combines DTW’s alignment-based similarity with the Mean Square Error (MSE) to determine the optimal balance between alignment and prediction accuracy. We conducted our experiments using datasets that included the closing prices of Apple, Samsung, Bitcoin, Ethereum, Meta platforms, and the X platform. Spearman’s rank correlation coefficient was used to evaluate the disentanglement by describing the relationship between the extracted representations. The findings confirm that our model surpasses state-of-the-art approaches in prediction error, financial risk assessment, correlation evolution, and prediction net curves, thereby giving market participants more trust in their decisions.

Tongtong Yang, Xiaolong Yang, Nanxing Gao et al.

The usage of cheap crude H2 in proton-exchange membrane fuel cells (PEMFCs) is still unrealistic to date, due to the suffering of the current Pt based nano-catalysts from impurities such as CO in anode. Recently, synergistic active sites between single atom (SA) and nanoparticle (NP) have been found to be promising for overcoming the poisoning problem. However, lengthening the nanoparticle-single atom (SA–NP) interface, i.e., constructing high density synergistic active sites, remains highly challenging. Herein, we present a new strategy based on molecular fusion strategy to create abundant SA–NP interfaces, with high density SA–NP interfaces created on a two dimensional nitrogen doped carbon nanosheets (Ir-SACs&NPs/NC). Owing to the abundance of SA–NP interface sites, the catalyst was empowered with a high tolerance towards up to 1000 ​ppm CO in H2 feed. These findings provide guidelines for the design and construction of active and anti-poisoning catalysts for PEMFC anode.

Vinicius Negri Machado, Fernando Ortiz Martinz, Wilson Komatsu et al.

The modeling process of a component or system consists of several steps, which are rarely entirely covered in undergraduate courses. Moreover, theoretical and laboratory lectures focus on different stages on modeling, and are commonly taught at different periods, which may negatively affect the learning process. Thus, this paper proposes a set of lectures that mixes theory and experiments, taught in an experimental laboratory, and which address all steps of modeling process. The case study is a single-phase transformer, where from a conceptual (electromagnetic) model, physical (electrical) models are developed up to a model capable of representing more complex phenomena such as inrush currents and magnetizing inductance saturation. Increasingly detailed theoretical modeling, using simulation tools and experimental measurements, guides the student in this process. It is shown that models can be improved at the expense of deeper understanding of the involved phenomena, and of more complex theoretical and experimental strategies to validate them. Moreover, this paper demonstrates that modeling complexity is only necessary up to a point which explains adequately the experimental results. Finally, the paper presents the perception of the students on the lectures, indicating that this teaching methodology can be adequate for other courses on system modeling.

Everton Cavalcante, Thais Batista, Flavio Oquendo

Modern systems are increasingly connected and more integrated with other existing systems, giving rise to \textit{systems-of-systems} (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within Software Engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The \textit{International Workshop on Software Engineering for Systems-of-Systems} (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in Software Engineering for SoS.

Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier et al.

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.

Jung-Hwan Park, Kyoung-Tak Kim, Chungu Lee et al.

M. I. Baranov, S. Buriakovskyi, V. Kniaziev

Goal. Receipt and analysis of close analytical correlations for the engineering calculation of maximal temperature of Tm and pressures of Pm in a plasma channel, time of tex explosion of explorer, active resistance of Rc and specific conductivity of γp of plasma channel, to entered in explorer Wi and selected in the channel of Wc of thermal energy and high speed of vmw distribution of shock acoustic wave in the plasma products of electric explosion (EE) in gas of explorer under the action of large impulsive current (LIC). Methodology. Basis of thermophysics, thermodynamics, theoretical and applied electrical engineering, electrophysics bases of technique of high-voltage and large impulsive currents, basis of heavy-current electronics, theory of explosion and plasma, measuring technique and electromagnetic compatibility. Results . Close formulas are got for the analytical calculation of temperature of Tm and pressures of Pm in a plasma channel, time of tex explosion of explorer, active resistance of Rc and specific conductivity of γp of plasma channel, to entered in explorer Wi and selected in the channel of Wc of thermal energy and speed of vmw of shock acoustic wave in «metallic plasma» at EE in gas of explorer, testing action LIC in the discharge chain of high-voltage generator of impulsive currents (GIC) with the stocked energy of W0. It is rotined that at EE in atmospheric air of copper explorer long 110 mm and by a radius 0,1 mm in the bit chain of GIC of the microsecond temporal range (Imc≈−190 кА; tmc≈42 μs; ωc≈26,18·103 s-1; W0≈121,4 кJ) levels of temperature of Tm, to time of tex explosion, pressures of Pm and speeds of vmw in the area of his explosion can arrive at numeral values: Tm≈121,6·103 K, tex≈3,32 μs; Pm≈14,19·109 Pa and vmw≈4693 m/s. The ways of receipt are formulated in the bit chain of GIC of «record» (most) values of temperature of Tm, pressures of Pm and speeds of vmw. It is set that at EE in atmospheric air of the indicated short thin copper explorer the coefficient of the useful use of ηc of electric energy of W0 of condenser battery of GIC arrives at the numeral value of ηc≈(Wi+Wc)/W0≈0,326 (32,6 %). Arising up in the plasma channel of discharge, initiated EE in gas of explorer, temperature of Tm and pressure of Pm, time of tex explosion of explorer, specific conductivity of γp of channel, thermal energy of Wc and speed of vmw of shock acoustic wave selected in a channel in «metallic plasma» can be certain experimental a way on results decoding of oscillograms of discharge current of ic(t) and high-voltage of uc(t) on an explorer in the chain of GIC. A formula is resulted for the close calculation of critical integral of current of Jk at EE in gas of explorers from different metals. Executed on powerful GIC heavy-current experiments were confirmed by substantive provisions offered approach near the analytical calculation of basic parameters of electro-explosive process for the probed explorer. Originality. Offered and the engineering going is scientifically grounded near the analytical calculation of the indicated thermophysical, gasodynamic and electroenergy parameters of Tm, Pm, tex, Rc, γp, Wi, Wc and vmw at EE in gas of metallic explorer, plugged in the discharge chain of GIC. Practical value. Application in electrophysics practice of the offered engineering going near a calculation in the chain of GIC of basic parameters of electro-explosive process will allow to facilitate labour of workers of scientific laboratories and promote efficiency of work of technicians-and-engineers during practical realization by them different electro-explosive technologies.

T. T. Cao, H. T. Nguyen, Hung Pham Van et al.

The push-pull DC-DC converter is classified as a switching power converter, which finds extensive utility across various domains within the realm of power electronics and electrical engineering. The push-pull converter is renowned for its capacity to offer galvanic isolation and achieve efficient voltage conversion. An iterative learning control is implemented in PSCAD to regulate the push-pull converter. The verification and comparison of the control performance have been conducted by numerical simulations, in relation to the present method.

Muhannad Drak Alsebai, Kishore Kumar Pedapenki

The controlling method of DSTATCOM device attracts many researchers because it affects the performance of the mentioned device. Recently, interval type II fuzzy logic controller has been used in many aspects of electrical engineering for controlling power electronics devices. MATLAB R2022b provides the researchers with two different types of interval type II fuzzy logic, Mamdani and Sugeno types and the ability to convert among them easily. In this paper, Mamdani and Sugeno interval type II fuzzy logic controllers with two different control algorithms were examined by means of MATLAB R2022b Simulink environment along with the tuning process. The Mamdani interval type II fuzzy logic controller with centroid defuzzification method gives the best performance of DSTATCOM device at controlled rectifier loads with both examined controlled algorithms UVT and SRF.

Fabio Calefato, Luigi Quaranta, Filippo Lanubile et al.

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.

Tao Chen, Miqing Li

Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this paper, we seek to close this gap. Firstly, we do that through a review of over 426 papers in the literature and 14 real-world requirements datasets. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. We found that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones.

O. Pishchukhina, D. Gordieieva, A. Rainer

This paper focuses on a formative assessment approach supported by a virtual learning environment (VLE) for a large post-graduate taught class, studying for the MSc Software Development degree, and delivered as a part-time distance course at the School of Electronics, Electrical Engineering and Computer Science, Queen’s University of Belfast. The paper provides a summary and analysis of statistics and students’ experiences with the online formative assessment components delivered in the VLE.We discuss and review the impact of the proposed assessment regime on class performance and learning outcomes for the MSc Computing Foundations module, comparing and contrasting supported and unsupported exam cases on a formative assessment basis. We observed a sustained level of student engagement with the formative assessment component based on data collected from student interactions with online module content and assessment in the VLE. The analysis of the data has indicated that students who attempted the mock exam multiple times, namely those with the highest number of attempts, showed a strong correlation with the highest scores on the final exam. An added benefit of using statistics was the item analysis carried out by the instructor, which has led to improvements in exam design, changes to some of the questions, and adjustments to the marking schemeEnd-of-term student feedback indicates that the mock exam, formative assessment feedback and repetitive practicing were very well received by the students. This experience reflects how students learn through online content delivery and make use of available technologies for self-directed learning and practice in computing. Frequent access to an online formative assessment tool followed by immediate feedback has helped to create a supportive learning environment and accommodate learners in the large class. The use of innovative VLE technologies for formative assessment has proven to be effective learning practice, that enhances student experience and promotes reflection and self-assessment in learning.

Tejas Asole, Rahul Karsal, Vishal Kanakdande et al.

– The conversion, control, and conditioning of electrical power are the focus of the electrical engineering discipline known as power electronics. The universal converter, which facilitates effective electrical energy transfer between various electrical system types, is one of the crucial parts of power electronics. The many types of universal converters, the requirements, design process, performance analysis, and applications are all covered in detail in this paper's complete study of the design and execution of a universal converter. All types of power conversions are included in a universal converter. Step-up and step-down conversions are also mentioned, as well as universal conversions such as rectifier (also known as AC-DC), inverter (also known as DC-AC), DC-DC converter, and AC-AC operations.