Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Jorge Melegati

The adoption of Generative AI (GenAI) suggests major changes for software engineering, including technical aspects but also human aspects of the professionals involved. One of these aspects is how individuals perceive themselves regarding their work, i.e., their work identity, and the processes they perform to form, adapt and reject these identities, i.e., identity work. Existent studies provide evidence of such identity work of software professionals triggered by the adoption of GenAI, however they do not consider differences among diverse roles, such as developers and testers. In this paper, we argue the need for considering the role as a factor defining the identity work of software professionals. To support our claim, we review some studies regarding different roles and also recent studies on how to adopt GenAI in software engineering. Then, we propose a research agenda to better understand how the role influences identity work of software professionals triggered by the adoption of GenAI, and, based on that, to propose new artifacts to support this adoption. We also discuss the potential implications for practice of the results to be obtained.

Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém et al.

The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and reliable systems. However, brittle, prototype-derived architectures often plague industrial applications and a lack of systemic oversight, creating a ``responsibility vacuum'' where safety and accountability are compromised. This paper presents an industry case study of the ``Maria'' platform, a production-grade AI system in primary healthcare that addresses this gap. Our central hypothesis is that trustworthy clinical AI is achieved through the holistic integration of four foundational engineering pillars. We present a synergistic architecture that combines Clean Architecture for maintainability with an Event-driven architecture for resilience and auditability. We introduce the Agent as the primary unit of modularity, each possessing its own autonomous MLOps lifecycle. Finally, we show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement. We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.

Yongwen Liu, Lei Zhang, Pu Li et al.

This paper presents a twisting controller (TC) based on a high-order sliding mode observer (HOSMO) for linear induction motors (LIMs), accounting for dynamic end effects. Based on the LIM model in the field-oriented frame, two extended subsystems are developed: a velocity extended model and a flux extended model. Using these models, two HOSMOs are designed to estimate the disturbances in each subsystem. The HOSMO outputs are then used for disturbance rejection, resulting in two second-order systems with small bounded disturbances. Two TCs are subsequently implemented to achieve finite-time velocity and flux tracking of the LIM. The primary advantage of this strategy lies in its ability to reduce chattering through active disturbance rejection. Hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed TC-HOSMO scheme.

Jefferson S. Costa, Angelo Lunardi, Alessio Iovine et al.

Model predictive control (MPC) has emerged as a highly regarded control strategy in power electronics for renewable energy applications. While it minimizes tracking errors and control effort, a significant challenge is the lack of systematic tuning strategies to meet these systems’ energy quality performance requirements. This paper proposes a comprehensive MPC tuning methodology for grid-integrating converters with LCL filters, incorporating modulation and delay compensation. We conduct a stability analysis to define precise constraints for cost function weights. The fine-tuning strategy systematically maps a Figure of Merit (FoM) for system performance using an advanced computational model, revealing that optimal tunings reside in narrow parameter regions. Experimental validation on a 2 kW workbench confirmed that all proposed MPC tunings met IEEE Std. 519-2014 power quality criteria and consistently outperformed a two-sample deadbeat controller, exhibiting enhanced dynamic response and power quality.

Haibo Wang, Zhuolin Xu, Shin Hwei Tan

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed.

Ziyou Li, Agnia Sergeyuk, Maliheh Izadi

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.

Jingzhong ZHANG, Fei MENG, Yang SUN et al.

A dual-layer robust optimization control method based on the droop principle of reactive power and voltage is proposed for renewable energy in distribution networks, considering active power uncertainties. First, in the reference-point optimization layer, the typical control quantities that affect the reactive power distribution in the power system are optimized with the objective of minimizing the overall cost over the multi-period, such as the static reactive power compensation devices, the coordinated control instructions of voltage regulating transformers, and the reactive power and port voltage reference values for renewable energy. Secondly, in the slope optimization layer, based on the column-and-constraint generation (C&CG) algorithm framework, the main problem model of slope instruction optimization and the sub-problem model of extreme scenarios set filtering are established. The results demonstrate that the proposed optimization control method can not only effectively adapt to the random fluctuations of renewable energy generation output but also maximize the utilization of reactive power capacity of grid-connected converters. It can optimize the system network losses and the operational cost of voltage regulation devices and enhance the operation reliability of power systems.

Z. Wang, L. Mei, X. Ma

<p>To improve the trajectory tracking performance and robustness for uncertain robot manipulators, a generalized sliding mode controller (GSMC) including an ideal controller and a continuous sliding mode controller (SMC) is proposed from the standpoint of motion constraints. First, the trajectory tracking requirements are formulated as the motion constraints, based on which an ideal controller is proposed to satisfy the motion constraints for robot manipulators whose dynamics are precisely known. Second, an additional continuous SMC is presented to compensate for the effects of uncertainty, and the chattering phenomenon that commonly exists in the SMC can be avoided by the introduction of a smoothing function. Third, Lyapunov analysis is conducted to verify that the proposed GSMC enables the tracking error restricted to a small region around zero. Finally, the numerical simulation and experiment are performed to verify the effectiveness and superiority of the proposed GSMC.</p>

Sun Dajun, Ouyang Yujie, Han Yunfeng

Time synchronization is crucial for effective collaboration among underwater sensors. However, existing synchronization protocols primarily cater to low-speed or simple motion scenarios, neglecting variations in radial velocity during message propagation. A novel Doppler compensation time synchronization scheme, called DC-Sync, was developed in this study to address this issue by targeting complex moving underwater sensors. DC-Sync includes a practical Doppler compensation and estimation method. Simulation results demonstrate that when the target motion follows a specific pattern, DC-Sync outperforms existing similar schemes in terms of time skew and time offset accuracy. Furthermore, the scheme maintains high estimation accuracy even with incomplete Doppler measurement values. Its performance was also validated through physical experiments.

Emanuele Aiello, Mirko Agarla, Diego Valsesia et al.

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural designs. However, the use of self-supervised pretraining lags behind in several domains, such as hyperspectral images, due to data scarcity. This paper addresses the challenge of data scarcity in the development of methods for spatial super-resolution of hyperspectral images (HSI-SR). We show that state-of-the-art HSI-SR methods are severely bottlenecked by the small paired datasets that are publicly available, also leading to unreliable assessment of the architectural merits of the models. We propose to capitalize on the abundance of high resolution (HR) RGB images to develop a self-supervised pretraining approach that significantly improves the quality of HSI-SR models. In particular, we leverage advances in spectral reconstruction methods to create a vast dataset with high spatial resolution and plausible spectra from RGB images, to be used for pretraining HSI-SR methods. Experimental results, conducted across multiple datasets, report large gains for state-of-the-art HSI-SR methods when pretrained according to the proposed procedure, and also highlight the unreliability of ranking methods when training on small datasets.

Cuiyun Gao, Xing Hu, Shan Gao et al.

With the advent of large language models (LLMs) in the artificial intelligence (AI) area, the field of software engineering (SE) has also witnessed a paradigm shift. These models, by leveraging the power of deep learning and massive amounts of data, have demonstrated an unprecedented capacity to understand, generate, and operate programming languages. They can assist developers in completing a broad spectrum of software development activities, encompassing software design, automated programming, and maintenance, which potentially reduces huge human efforts. Integrating LLMs within the SE landscape (LLM4SE) has become a burgeoning trend, necessitating exploring this emergent landscape's challenges and opportunities. The paper aims at revisiting the software development life cycle (SDLC) under LLMs, and highlighting challenges and opportunities of the new paradigm. The paper first summarizes the overall process of LLM4SE, and then elaborates on the current challenges based on a through discussion. The discussion was held among more than 20 participants from academia and industry, specializing in fields such as software engineering and artificial intelligence. Specifically, we achieve 26 key challenges from seven aspects, including software requirement & design, coding assistance, testing code generation, code review, code maintenance, software vulnerability management, and data, training, and evaluation. We hope the achieved challenges would benefit future research in the LLM4SE field.

Umm-e- Habiba, Markus Haug, Justus Bogner et al.

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.

James S. Wheaton, Daniel R. Herber

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Without that model connectivity, requirement quality can suffer due to imprecision and inconsistent terminology, frustrating communication during system development. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. The Model-Based Structured Requirement SysML Profile was extended to comply with the INCOSE Guide to Writing Requirements updated in 2023 while conforming to the ISO/IEC/IEEE 29148 standard requirement statement templates. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition and requirements V&V. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to explore its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support with the system architecture modeling software.

ZHANG Sen, LIU Yi, SU Xiaolei et al.

In order to improve the microwave absorption performance of TiC powder, TiC/Ni composite powder was prepared by coating nickel particles with mass fraction of 10%, 20% and 30% on the surface of TiC by typical coarsening and sensitizing electroless nickel plating process. The composite powder was characterized by SEM and XRD, and the effect of nickel content on the microwave absorbing properties of TiC/Ni composite powder was analyzed. The results shows that the dielectric constant and permeability of TiC/Ni powder increases with the increase of nickel mass fraction. With -5 dB as the scale, the absorption bandwidth of the sample with a thickness of 3.0 mm increases from 0 to 3.34 GHz. When the thickness is 3.8 mm, the minimum reflection loss of TiC is -3.66 dB. For TiC/Ni samples with a thickness of 2.8 mm and a mass fraction of Ni of 30%, the minimum reflection loss value of -9.95 dB is obtained at 9.67 GHz. It can be seen that electroless nickel plating can improve the microwave absorption performance of TiC.

Qiyuan Zhang, Xiaochen Yuan, Tong Liu

In this paper we propose a blind dual watermarking scheme using Set Partitioning in Hierarchical Trees (SPIHT) and Stucki Kernel halftone technique for the tamper detection and image self-recovery. The watermark consists of authentication bits for tampering area location and recovery bits for image restoration. We generate two recovery bits to ensure the high-quality recovery of the tampered image. The primary recovery bit is generated by the SPIHT encoding, and the secondary recovery bit is generated by the Stucki Kernel halftone technique. Then the authentication bit is generated based on the recovery bits. Before embedding the watermark, we shuffle the watermark bits through Arnold cat mapping and diagonal mapping to improve the security and quality of the restored image. LSB-based watermarking technique is used to embed the watermark into the original image to ensure the invisibility of the watermarked image. Experiments have been conducted on two datasets, BOW2 and USC-SIPI, and results show that the proposed scheme can achieve high restoration quality. Comparison with the existing works demonstrate the good performance and superiority of the proposed scheme.

Kashumi Madampe, Rashina Hoda, John Grundy

Background: Requirements changes (RCs) are inevitable in Software Engineering. Research shows that emotional intelligence (EI) should be used alongside agility and cognitive intelligence during RC handling. Objective: We wanted to study the role of EI in-depth during RC handling. Method: We conducted a socio-technical grounded theory study with eighteen software practitioners from Australia, New Zealand, Singapore, and Sri Lanka. Findings: We found causal condition (software practitioners handling RCs), intervening condition (mode of work), causes (being aware of own emotions, being aware of others' emotions), direct consequences (regulating own emotions, managing relationships), extended consequences (sustaining productivity, setting and sustaining team goals), and contingencies: strategies (open and regular communication, tracking commitments and issues, and ten other strategies) of using EI during RC handling. We also found the covariances where strategies co-vary with the causes and direct consequences, and ease/ difficulty in executing strategies co-vary with the intervening condition. Conclusion: Open and regular communication is key to EI during RC handling. To the best of our knowledge, the framework we present in this paper is the first theoretical framework on EI in Software Engineering research. We provide recommendations including a problem-solution chart in the form of causes, direct consequences, and mode of work against the contingencies: strategies for software practitioners to consider during RC handling, and future directions of research.

Gunnar Kudrjavets, Jeff Thomas, Nachiappan Nagappan

Satisfactory software performance is essential for the adoption and the success of a product. In organizations that follow traditional software development models (e.g., waterfall), Software Performance Engineering (SPE) involves time-consuming experimental modeling and performance testing outside the actual production environment. Such existing SPE methods, however, are not optimized for environments utilizing Continuous Integration (CI) and Continuous Delivery (CD) that result in high frequency and high volume of code changes. We present a summary of lessons learned and propose improvements to the SPE process in the context of CI/CD. Our findings are based on SPE work on products A and B conducted over 5 years at an online services company X. We find that (a) SPE has mainly become a post hoc activity based on data from the production environment, (b) successful application of SPE techniques require frequent re-evaluation of priorities, and (c) engineers working on SPE require a broader skill set than one traditionally possessed by engineers working on performance.

Hironori Washizaki

It is essential to discuss the role, difficulties, and opportunities concerning people of different gender in the field of software engineering research, education, and industry. Although some literature reviews address software engineering and gender, it is still unclear how research and practices in Asia exist for handling gender aspects in software development and engineering. We conducted a systematic literature review to grasp the comprehensive view of gender research and practices in Asia. We analyzed the 32 identified papers concerning countries and publication years among 463 publications. Researchers and practitioners from various organizations actively work on gender research and practices in some countries, including China, India, and Turkey. We identified topics and classified them into seven categories varying from personal mental health and team building to organization. Future research directions include investigating the synergy between (regional) gender aspects and cultural concerns and considering possible contributions and dependency among different topics to have a solid foundation for accelerating further research and getting actionable practices.

Minglong Kang, Li Zhou, Yunlai Deng et al.

Multilayer brazeable aluminum alloy sheet is prone to collapse during high-temperature brazing process. The sagging resistance of the aluminum composite sheet needs to be further improved for quality control. Effects of annealing and rate of reduction on sagging resistance, microstructure, and Si diffusion of a hot-rolled, four-layered Al clad sheet (4343/3003/6111/3003) were investigated by means of a sagging device, OM, SEM, and TEM. Results showed that once annealed at 360°C, the sagging distance was increased from 3 to 15.7 mm as the reduction rate changed from 10% to 40%. By increasing annealing temperature to 410°C, those were changed from 3.1 to 20.8 mm accordingly. At 360°C/40% and 410°C/40%, specimens exhibited weak sagging resistance, whereas fine recrystallized grains were formed in the core promoting Si penetration along grain boundaries. While the specimens were treated at 360°C/10% and 410°C/10%, better sagging resistance was observed due to the formation of coarse recrystallized grains that can suppress erosion of Si. At the same reduction rate, the sagging resistance was higher for the sample annealed at a lower temperature as more precipitates appeared in the core (at 360°C), thus leading to an increase in strength.