Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Abhiraam Eranti, Yogesh Tewari, Rafael Palacios et al.

Deep-learning methods have boosted the analytical power of Raman spectroscopy, yet they still require large, task-specific, labeled datasets and often fail to transfer across application domains. The study explores pre-trained encoders as a solution. Pre-trained encoders have significantly impacted Natural Language Processing and Computer Vision with their ability to learn transferable representations that can be applied to a variety of datasets, significantly reducing the amount of time and data required to create capable models. The following work puts forward a new approach that applies these benefits to Raman Spectroscopy. The proposed approach, RSPTE (Raman Spectroscopy Pre-Trained Encoder), is designed to learn generalizable spectral representations without labels. RSPTE employs a novel domain adaptation strategy using unsupervised Barlow Twins decorrelation objectives to learn fundamental spectral patterns from multi-domain Raman Spectroscopy datasets containing samples from medicine, biology, and mineralogy. Transferability is demonstrated through evaluation on several models created by fine-tuning RSPTE for different application domains: Medicine (detection of Melanoma and COVID), Biology (Pathogen Identification), and Agriculture. As an example, using only 20% of the dataset, models trained with RSPTE achieve accuracies ranging 50%–86% (depending on the dataset used) while without RSPTE the range is 9%–57%. Using the full dataset, accuracies with RSPTE range 81%–97%, and without pre-training 51%–97%. Current methods and state-of-the-art models in Raman Spectroscopy are compared to RSPTE for context, and RSPTE exhibits competitive results, especially with less data as well. These results provide evidence that the proposed RSPTE model can effectively learn and transfer generalizable spectral features across different domains, achieving accurate results with less data in less time (both data collection time and training time).

Bo Hou, Qiushui Chen, Luying Yi et al.

K. Daqrouq, S. Alghamedi, S. Alwaseli et al.

A study to examine past research about artificial intelligence (AI) technology applications within electrical engineering (EE) systems is presented. EE stands out as one of the areas where research into AI acquires the most substantial increase in attention. This study explores AI transformation possibilities for EE because of modern technology progress and the critical need for field innovations. Recent research papers obtained from open literature form the survey dataset to create a complete understanding of AI deployment across EE major areas. Particularly, renewable energy (RE), power systems, control systems, and power electronics domains are studied. The research results showcase the advantages and difficulties that AI implementation produces in these individual EE domains. A research review of the main AI methods includes both their effective use cases and potential research paths for future work. This research seeks to provide academic directions for scholars who want to use AI in EE by explaining current approaches as well as inspiring further innovation in this continuously progressing field.

Sayan Majumder, Debika Bhattacharyya, Swati Chowdhuri

Abstract Mobile ad hoc networks (MANETs) facilitate data communication across multiple nodes and hop stations, characterized by their dynamic topology. This inherent flexibility, however, makes MANETs vulnerable to various security threats, notably blackhole and wormhole attacks, where malicious nodes can intercept and manipulate data. This study investigates the security vulnerabilities of MANETs, particularly against blackhole, Sybil, and wormhole attacks, and introduces the Advanced Blockchain Dynamic Source Routing (ABCD) algorithm to address these challenges. Motivated by the need for robust and decentralized security solutions in MANETs, the proposed algorithm integrates blockchain technology and homomorphic encryption to secure data communication without intermediate decryption. The ABCD algorithm leverages Dijkstra’s algorithm for optimal routing and employs a tamper-proof, decentralized data storage approach. Comparative analysis under attack scenarios reveals that the ABCD algorithm outperforms the standard DSR protocol across multiple quality of service metrics, demonstrating a significant improvement in MANET security over equivalent studies. The packet delivery rate is also improved from 81 to 92% using the modified ABCD algorithm.

Bing Li, Jiangtao Dong, Xile Wang et al.

To investigate the impact of cross-grained sentiments on user feature representation and address the issue of data sparsity, this paper proposes a Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features (ICSR). The algorithm begins by employing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model and a Bi-GRU (Bidirectional Gated Recurrent Units) network to derive feature vectors from user and item reviews. Sentiment dictionaries and attention mechanisms are then applied to assign appropriate weights to the review features of users and items, respectively. To capture a richer set of sentiment features, a cross-grained sentiment feature fusion module is introduced. This module leverages an LDA (Latent Dirichlet Allocation) model and dependency syntactic analysis techniques to extract fine-grained sentiment features, while a word2vec pre-trained model and sentiment dictionaries are used to capture coarse-grained sentiment features. These features are then fused to form comprehensive cross-grained sentiment representations. Finally, rating interaction features are extracted using matrix factorization techniques, and all features are integrated and fed into a DeepFM model for rating prediction. Experimental results on Amazon datasets demonstrate that the proposed ICSR algorithm significantly outperforms baseline algorithms in terms of recommendation performance.

Marina Araújo, Júlia Araújo, Romeu Oliveira et al.

[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.

Sonja M. Hyrynsalmi, Grischa Liebel, Ronnie de Souza Santos et al.

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.

Yining Hong, Christopher S. Timperley, Christian Kästner

Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.

Adeola Ona-Olapo Esho, Adeoye Taofik Aderamo, Henry Chukwuemeka, Olisakwe

This paper explores the transformative potential of AI-driven solutions in optimizing energy efficiency and the development of eco-friendly materials for electronics. As global energy consumption and electronic waste continue to rise, innovative technologies are essential to mitigate their environmental impact. AI models have shown significant promise in enhancing the performance of smart grids and residential and industrial energy systems by predicting and adjusting energy usage in real time. Additionally, the research and development of sustainable materials for semiconductors and electronic components offer an important pathway to reducing the environmental footprint of electronics manufacturing. By aligning these advancements with national sustainability and climate goals, this paper highlights the critical role of AI and sustainable materials in creating a more energy-efficient and environmentally responsible future. The paper concludes by suggesting areas for future research, emphasizing the long-term impact of these technologies on the electrical engineering field and their contribution to sustainability.

Saman Mahmoodi, Hadi Tarimoradi

The growing emphasis on power quality has posed significant challenges for distribution system operators (DSOs). Among these challenges, short-term voltage fluctuations, specifically voltage sag, have drawn considerable attention. In this study, three concepts of average edge (AE), lower average edge (LAE), and upper average edge (UAE) based on the electrical connection matrix and voltage-magnitude sensitivity matrix are defined and used as the partitioning first level. At the second level, a kernel smoothing function is employed to refine the zoning process. Subsequently, strategic locations within each zone are identified: the vertex and middle buses. These carefully selected buses serve as installation points for dynamic voltage restorers (DVRs). In response, this study proposes a novel solution by partitioning the distribution network into distinct zones. The focus lies in developing a two-level offline partitioning approach for active distribution networks (ADNs) that incorporate photovoltaic (PV) systems. To evaluate the effectiveness of the proposed method, numerical studies were conducted on modified IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus systems, with simulations performed using MATLAB/Simulink. The proposed method provides good performance and fast calculation speed for distribution network partitioning, as confirmed by the results. Test results show improved bus voltage with PV unit integration. Additionally, power loss in the IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus networks decreased by 47.73 kW, 56.87 kW, and 69.63 kW, respectively. Furthermore, the voltage profile improved from 0.75 p.u. to 0.928 p.u. during a voltage sag in the IEEE 33-bus system, and in steady state, the voltage increased from 0.933 to 0.959 p.u.

Yao Meng, Xinyu Yang, Haitao Wang et al.

This paper proposes a new dual-stator hybrid-magnet flux modulation machine (DS-FMHMM) for direct-drive applications, which employs NdFeB magnet excitation and Ferrite magnet excitation on the rotor and outer stator sides, respectively. With this design, the proposed DS-FMHMM can not only fully use the bidirectional flux modulation effect, but also effectively alleviate the magnetic saturation issue. The machine configuration is described, together with the operating principle. Then, the design parameters of DS-FMHMM are globally optimized for obtaining high torque quality, and the influence of magnet dimensions on torque is analyzed. To evaluate the merits of the proposed DS-FMHMM, the electromagnetic performances of machines under different magnet excitation sources are analyzed, and a comprehensive electromagnetic performance comparison of DS-FMHMM and two existing dual-stator flux modulation machines (DSFMMs) is developed.

Julles Mitoura dos Santos Junior, Adriano Pinto Mariano

As one of the main industrial segments of the current geoeconomics scenario, agro-industrial activities generate excessive amounts of waste. The gasification of such waste using supercritical water (SCWG) has the potential to convert the waste and generate products with high added value, hydrogen being the product of greatest interest. Within this context, this article presents studies on the SCWG processes of lignocellulosic residues from cotton, rice, and mustard husks. The Gibbs energy minimization (minG) and entropy maximization (maxS) approaches were applied to evaluate the processes conditioned in isothermal and adiabatic reactors, respectively. The thermodynamic and phase equilibria were written as a nonlinear programming problem using the <i>Peng–Robinson</i> state solution for the prediction of fugacity coefficients. As an optimization tool, TeS (Thermodynamic Equilibrium Simulation) software v.10 was used with the help of the <i>trust-constr</i> algorithm to search for the optimal point. The simulated results were validated with experimental data presenting surface coefficients greater than 0.99, validating the use of the proposed modeling to evaluate reaction systems of interest. It was found that increases in temperature and amounts of biomass in the process feed tend to maximize hydrogen formation. In addition to these variables, the H₂/CO ratio is of interest considering that these processes can be directed toward the production of synthesis gas (syngas). The results indicated that the selected processes can be directed to the production of synthesis gas, including the production of chemicals such as methanol, dimethyl ether, and ammonia. Using an entropy maximization approach, it was possible to verify the thermal behavior of reaction systems. The maxS results indicated that the selected processes have a predominantly exothermic character. The initial temperature and biomass composition had predominant effects on the equilibrium temperature of the system. In summary, this work applied advanced optimization and modeling methodologies to validate the feasibility of SCWG processes in producing hydrogen and other valuable chemicals from agro-industrial waste.

Yiming Zhang, Zhongjin Huang, Ronghuan Xie et al.

Inductive power transfer (IPT), as a method of wireless power transfer (WPT) via magnetic induction, can be applied to electric vehicles (EVs) due to its convenience and automation. Interoperability and misalignment tolerance are both major research difficulties for WPT of EVs. This paper proposes a two-channel topology and a coil optimization method, which can improve misalignment tolerance for the unipolar coil (Q) and interoperate with the bipolar coil (DD). Firstly, a topology with phase shift strategy is constructed to increase output ability with Y misalignments and the mathematical model of the proposed topology is established. Secondly, a coil density optimization method is presented to smooth the transmitting mutual inductances fluctuation. Finally, a 1-kW prototype is built to verify the proposed system which can achieve load-independent constant-current charging. With the Y misalignment of 150 mm, the experimental results agree well with the theoretical analysis. The proposed system is able to interoperate with two types of coils and can achieve misalignment tolerance.

Li Wei, Lei Zhao, Xun Zhu et al.

In this study, polylactic acid/graphene oxide/Dopamine (PLA/GO/DA) porous nanofiber membrane was prepared by electrospinning. L _16 (4 ^3 ) orthogonal experiment was designed to investigate the effects of reaction temperature, reaction time, and DA concentration on the adsorption performance of DA oxidized and self-polymerized on the fiber. Based on the characterization of scanning electron microscopy and the determination of the adsorption performance of the fiber membrane to methylene blue (MB) dye, data visualization analysis, variance analysis, and F-test were conducted to determine the optimal process parameters: reaction temperature of 45 °C, reaction time of 30 h, and DA concentration of 2 mg ml ^−1 . PLA/GO/PDA(Polydopamine) nanofiber was prepared and characterized under the optimal process parameters. The results showed that the average diameter of the PDA-loaded nanofiber increased from 737 nm to 996 nm, and a layer of PDA with a thickness of about 129 nm was loaded on the outer surface of the fiber, making the contact angle of the fiber membrane with 0° and becoming a hydrophilic material. In adsorption performance testing of MB, the PLA/GO/PDA nanofiber membrane prepared based on the PLA/GO/DA fiber membrane with an adsorption rate of 98.81 % in 24 h was superior to the PLA/GO/PDA nanofiber membrane prepared based on the PLA/GO fiber membrane.

Sayan Chatterjee, Ching Louis Liu, Gareth Rowland et al.

The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks of active testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control and Copilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction.

Sergio Rico

Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context description, study classification, generalizability, and the handling of validity threats. This paper presents a reflective analysis aiming to share insights that can enhance the quality and impact of case study reporting. We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies. Additionally, particular focus is placed on articulating generalizable findings and thoroughly discussing generalizability threats. We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound, resonate with, and apply to software engineering practitioners and the broader academic community. The reflections and recommendations offered in this paper aim to ensure that insights from case studies are transparent, understandable, and tailored to meet the needs of both academic researchers and industry practitioners. In doing so, we seek to enhance the real-world applicability of academic research, bridging the gap between theoretical research and practical implementation in industry.

A. Castillo-Paz, A. Elizondo-Noriega, A. Benavides et al.

This study introduces a System Dynamics model developed to optimize Engineering Change Administration (ECA) in Electronics Assembly within automotive manufacturing. The model employs historical data and expert insights to accurately simulate the complexities involved in Current Product ECA Operations. It methodically explores key pathways including the Direct Implementation Pathway and the Serial Production Feasibility Pathway, each designed to accommodate engineering changes with varying degrees of complexity and urgency. This model addresses the critical challenges of maintaining production efficiency while effectively integrating innovative electronic components into automotive vehicles. Detailed outcomes highlight the pivotal roles of the Complexity Index and Quality Coordination Cost in predicting the success of implementations and in managing potential disruptions. By providing insights into the nuances of engineering change impact and operational adaptability, the model enhances operational efficiency and supports strategic decision-making within the automotive industry. This research demonstrates how dynamic modeling can be a vital tool in managing the sophisticated dynamics of automotive electronics assembly, contributing to both theoretical frameworks and practical applications in industrial operations.

Sholpan Zhussipbekova, Gulshakhan Alimbekova, Venera Rystygulova et al.

The aim of this research is to study the main pedagogical experiments conducted for the purpose of practical study of the level of mastery by students of pharmaceutical specialties of electrical engineering and electronics fundamentals. The basis of the methodological approach in this research work is a combination of existing system analysis methods of teaching disciplines related to the fundamentals of electrical engineering and electronics for nontechnical specialties of modern educational institutions, with experimental studies of pedagogical features of the perception of the taught discipline by students of pharmaceutical specialties. The results of this research indicate a weak interest of students of the pharmaceutical specialty of the Asfendiyarov Kazakh National Medical University in mastering the discipline ‘Fundamentals of Electrical Engineering and Electronics’, which indicates the need to improve existing teaching methods and develop new ones that can provide more tangible results. The results and conclusions of this research work are of significant practical importance from the standpoint of the prospects for improving the teaching of this group of disciplines for students of nontechnical specialties of educational institutions and are also of great importance for teachers of the fundamentals of electrical engineering and electronics, faced with the urgent need to teach this kind of material to students during lectures and practical classes.