Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Alharbi Abdulaziz H.

The aim of this research is to analyse the combined effect of variable thermal conductivity and nonlinear thermal radiation on magnetohydrodynamic (MHD) hybrid nanofluid flow in convergent-divergent channels. The effects of two nanoparticles (i.e. ZrO2{\text{ZrO}}_{\text{2}} and SiO2{\text{SiO}}_{\text{2}}) in base fluid (i.e. H2O{\text{H}}_{\text{2}}\text{O}) are considered in this work. The partial differential equations modelling the problem are reduced to ordinary differential equations following the application of the similarity transformations. The system has been solved analytically with the differential transform method and numerically with the Runge–Kutta–Fehlberg 4th–5th order method with the assistance of the shooting technique. Comprehensive analysis and discussion have been conducted regarding the impact of multiple governing parameters on the dimensionless velocity and temperature distributions. These parameters include variable thermal conductivity, nonlinear thermal radiation, Hartman number, and hybrid nanoparticle volume fraction. Finally, this method will provide some insights into the usefulness of MHD hybrid nanofluid flow in convergent-divergent channels, and the results produced by the analytical data have also been strengthened and verified by the use of numerical data as well as data from the literature.

阎 明洋, 胡 志良, 张 银鸿 et al.

粒子加速器束流损失产生脉冲中子辐射场，计数型中子剂量仪测量脉冲中子存在漏计数现象。为从根源上解决漏计数问题，提出了基于中子核反应电荷量校准中子反应数的探测物理模型。研究中子核反应弱电流产生的物理机制，据此研制了弱电流积分型电子学系统，并集成为脉冲中子剂量仪。在中国散裂中子源（China Spallation Neutron Source，CSNS）上的实验表明：单次脉冲中子风暴核反应数可达10³，剂量率水平覆盖μSv∙h^-1至数百mSv∙h^-1量级，并可观测ms级脉冲中子时间结构；测量值与仿真值相符性较好，两者相差<50%。目前该研究不仅填补了国内测量宽能区脉冲中子辐射剂量的技术空白，且满足了国家大科学装置的应用需求。

Sercan Yalçın

Tire failures pose significant safety risks, necessitating advanced inspection techniques. This research investigates the application of magnetic sensors and deep learning for detecting defects in steel belts of the tires. It was aim to develop a robust and accurate fault detection system by measuring magnetic field variations caused by defects. In this study, the magnetic image sensor circuit had been designed and then the images obtained from it have been classified as none, crack, and delamination type steel belt errors. Various deep learning models and their hybrid architectures, were explored and compared. Experimental results demonstrate that all models exhibit strong performance, with the Transformer model achieving the highest accuracy of 96.12%. The developed system offers a potential solution for improving tire safety and reducing maintenance costs in industries.

Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa et al.

This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.

Mahdi Jaberzadeh Ansari, Ann Barcomb

Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.

Kaicheng Huang, Fanyu Wang, Yutan Huang et al.

Advancements in large language models (LLMs) have led to a surge of prompt engineering (PE) techniques that can enhance various requirements engineering (RE) tasks. However, current LLMs are often characterized by significant uncertainty and a lack of controllability. This absence of clear guidance on how to effectively prompt LLMs acts as a barrier to their trustworthy implementation in the RE field. We present the first roadmap-oriented systematic literature review of Prompt Engineering for RE (PE4RE). Following Kitchenham's and Petersen's secondary-study protocol, we searched six digital libraries, screened 867 records, and analyzed 35 primary studies. To bring order to a fragmented landscape, we propose a hybrid taxonomy that links technique-oriented patterns (e.g., few-shot, Chain-of-Thought) to task-oriented RE roles (elicitation, validation, traceability). Two research questions, with five sub-questions, map the tasks addressed, LLM families used, and prompt types adopted, and expose current limitations and research gaps. Finally, we outline a step-by-step roadmap showing how today's ad-hoc PE prototypes can evolve into reproducible, practitioner-friendly workflows.

Jonathan Engel

This paper reviews the calculation of nuclear Schiff moments, which one must know in order to interpret experiments that search for time-reversal-violating electric dipole moments in certain atoms and molecules. After briefly reviewing the connection between dipole moments and CP violation in and beyond the Standard Model of particle physics, Schiff's theorem, which concerns the screening of nuclear electric dipole moments by electrons, Schiff moments, and experiments to measure dipole moments in atoms and molecules, the paper examines attempts to compute Schiff moments in nuclei such as $^{199}$Hg and octupole-deformed isotopes such as $^{225}$Ra, which are particularly useful in experiments. It then turns to ab initio nuclear-structure theory, describing ways in which both the In-Medium Similarity Renormalization Group and coupled-cluster theory can be used to compute important Schiff moments more accurately than the less controlled methods that have been applied so far.

Mathilde Prudent, Alejandro Borroto, Florent Bourquard et al.

Manufacturing multifunctional nanocomposite materials and engineered surface nanopatterns involves a strategic blend of topography, crystal structures, and chemistry. Here, we report the controllable formation of crystalline nanoparticles and intermetallic compounds on thin films of metallic glasses (Zr50Cu50, Ti50Cu50, and Zr67Ag33) irradiated by ultrafast laser beams. Mapping the structural modification of the photoexcited and subsequently heated alloys reveals previously neglected chemical reactions with air, offering a direct solution for incorporating nanoparticles into an amorphous oxide matrix and broadening the range of laser-induced surface self-organization features. Our findings are attributed to the occurrence and enrichment of oxygen surface contamination that reacts with selected elements of the metallic glasses. Additionally, the growth of the crystalline phase from undercooled liquid may originate from the dissolution of oxides. Finally, our results establish that the combination of crystalline nanoparticles on amorphous periodic patterns can be universally obtained in a wide range of binary systems of irradiated metallic glasses.

Renan Lima Baima, Tiago Miguel Barao Caetano, Ana Carolina Oliveira Lima et al.

The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering. This direction promises a holistic and applied trajectory for Computer Engineering education, supported by the outcomes of our case study, where artifact-centric learning proved effective, with 73% of students achieving the highest grade. Self-assessments further corroborated academic excellence, emphasizing students' engagement in skill enhancement and knowledge acquisition.

Matteo Raggi, Gennaro Boccia, Luca Mesin

Objective: Crosstalk in surface electromyogram (EMG) is an important open problem and the common strategy of reducing it through spatial filters needs improvements. Methods: We evaluated experimentally the optimal spatio-temporal filter (OSTF), i.e., a recent approach that adapts to the subject, filtering different EMG channels both in time and space to emphasize the signal of a target muscle discarding that of adjacent ones. EMGs were recorded by a high-density recording system from pronator teres (target muscle) and flexor carpi radialis (crosstalk muscle) of 8 healthy subjects. OSTF was tested in different conditions, considering one channel per muscle (either single or double differential, SD and DD, respectively), changing the selectivity of detection (small electrodes close to each other, or large ones with higher inter-electrode distance), the force applied by the muscles (whose EMGs were summed to simulate different levels of crosstalk), and the duration of the signal to train the method. Results: OSTF was less affected by crosstalk than SD and DD filters. Statistically significant improvements were obtained in reducing the crosstalk-induced variations: for example, considering small electrodes, we obtained a percentage error of 157.30±57.11 % and 38.54±10.47 % (mean±std) in the estimation of the average rectified value (ARV), and an error of 23.57±3.92 % and 8.31±0.88 % in the estimation of the median frequency (MDF), for SD and OSTF, respectively. Conclusion: The OSTF can be applied in real-time, is easy to use, and is feasible even when using only few detection channels, as is customary in many applications.

Cencen LIU, Tian XIA, Yan LI et al.

Modern distribution networks are often constructed in grid and operated in a radial manner to improve the transfer capacity under fault conditions. The traditional distribution network planning method generally adopts the two-stage iterative calculation method of planning design and reliability evaluation, which can only obtain an extensive planning scheme; the resulting planning scheme is either over invested or unable to meet specific reliability requirements. Therefore, a multi-stage distribution network expansion planning method considering reliability constraints is proposed. The reliability index calculation process is analyzed and embedded into the planning model, which can accurately consider the fault isolation, load transfer and recovery strategies. Based on the linearized power flow, the planning model is a typical mixed integer linear optimization problem, which can be effectively solved. The performance of the proposed method is verified in the Portugal 54-node system. The simulation result proves the effectiveness and flexibility of this method.

Hanna Abi Akl

The "Information Retrieval in Software Engineering (IRSE)" at FIRE 2023 shared task introduces code comment classification, a challenging task that pairs a code snippet with a comment that should be evaluated as either useful or not useful to the understanding of the relevant code. We answer the code comment classification shared task challenge by providing a two-fold evaluation: from an algorithmic perspective, we compare the performance of classical machine learning systems and complement our evaluations from a data-driven perspective by generating additional data with the help of large language model (LLM) prompting to measure the potential increase in performance. Our best model, which took second place in the shared task, is a Neural Network with a Macro-F1 score of 88.401% on the provided seed data and a 1.5% overall increase in performance on the data generated by the LLM.

Brian S. Mitchell

Cloud compute adoption has been growing since its inception in the early 2000's with estimates that the size of this market in terms of worldwide spend will increase from \$700 billion in 2021 to \$1.3 trillion in 2025. While there is a significant research activity in many areas of cloud computing technologies, we see little attention being paid to advancing software engineering practices needed to support the current and next generation of cloud native applications. By cloud native, we mean software that is designed and built specifically for deployment to a modern cloud platform. This paper frames the landscape of Cloud Native Software Engineering from a practitioners standpoint, and identifies several software engineering research opportunities that should be investigated. We cover specific engineering challenges associated with software architectures commonly used in cloud applications along with incremental challenges that are expected with emerging IoT/Edge computing use cases.

Jocelyn Ahmed Mazari, Antoine Reverberi, Pierre Yser et al.

In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to set up a shape optimization study of the Duisburg Test Case (DTC) container vessel. We present two different applications: (1) sensitivity analysis to detect the most promising generic basis hull shapes, and (2) multi-objective optimization to quantify the trade-off between optimal hull forms. DLPO framework allows for the evaluation of design iterations automatically in an end-to-end manner. We achieved these results by coupling Extrality's Deep Learning Physics (DLP) model to a CAD engine and an optimizer. Our proposed DLP model is trained on full 3D volume data coming from RANS simulations, and it can provide accurate and high-quality 3D flow predictions in real-time, which makes it a good evaluator to perform optimization of new container vessel designs w.r.t the hydrodynamic efficiency. In particular, it is able to recover the forces acting on the vessel by integration on the hull surface with a mean relative error of 3.84\% \pm 2.179\% on the total resistance. Each iteration takes only 20 seconds, thus leading to a drastic saving of time and engineering efforts, while delivering valuable insight into the performance of the vessel, including RANS-like detailed flow information. We conclude that DLPO framework is a promising tool to accelerate the ship design process and lead to more efficient ships with better hydrodynamic performance.

Haoyan Wei, C. T. Wu, Wei Hu et al.

Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach, which predicts nonlinear behaviors of composite materials and structures at a computational speed orders-of-magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding-induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning-based multiscale method for SFRC modeling.

Xifeng Mi

In order to improve the convergence and diversity of multi-objective differential evolutionary algorithm in solving problems, a fuzzy adaptive sorting variation multi-objective differential evolution algorithm is proposed. First of all, using an adaptive fuzzy system by adjusting the parameters of the sorting variation, the balance of local search ability and global exploring ability of the algorithm, at the same time of accelerate the algorithm convergence speed, reduce the possibility of a fall in local optimum; Secondly, using the homogeneous population initialization method, based on the distribution of the algorithm was beginning to get a uniform initial population, improving the stability and diversity; Finally, add a temporary population to store is discarded by individuals, the optimized choice finally, for each generation to improve the population diversity in the process of evolution. Matlab was used to conduct simulation experiments and compared the proposed algorithm with four other multi-target evolutionary algorithms. The experimental results show that the proposed algorithm is superior to several other contrasting algorithms in convergence and diversity, and can effectively approach the frontier of real Pareto. At the same time, the experiment also verifies the validity of fuzzy adaptive sort variation strategy in the proposed algorithm.

ZHU YuYang, ZHAI JianGuang, GAO Chun et al.

In order to explore the influence of the size effect on the fracture strength of unidirectional continuous glass fiber reinforced polypropylene composites（unidirectional GF/PP composites）, the Zwick/Roell Z005 universal material testing machine was used to test different gauge lengths through orthogonal test methods.（60 mm, 100 mm, 200 mm）, unidirectional GF/PP composites with different widths（10 mm, 15 mm, 20 mm） are subjected to tensile test. The test results show that as the test size increases, the breaking strength of unidirectional GF/PP composites gradually decreases. Because the fracture strength of unidirectional GF/PP composites has a large dispersion, the distribution law of the strength of unidirectional GF/PP composites under different size conditions is obtained through the classic two-parameter Weibull strength statistical model, and the improved generalized The two-parameter weakest chain strength statistical model introduces the exponential parameterβto modify the influence of the spatial distribution of defects. The fracture strength data under different sizes are processed in a unified manner. The resulting failure function comprehensively reflects the size effect on the unidirectional GF/PP composite The effect of material breaking strength.

Julian Cendrero, Julio Gonzalo, Marcos Galletero et al.

A time-series of numerical data and a sequence of time-ordered documents are often correlated. This paper aims at modeling the impact that the underlying themes discussed in the text data have on the time series. To do so, we introduce an original topic model, Time Series Impact Through Topic Modeling (TSITM), that includes contextual data by coupling Latent Dirichlet Allocation (LDA) with linear regression, using an elastic net prior to set to zero the impact of uncorrelated topics. The resulting topics act as explanatory variables for the regression of the numerical time series, which allows us to understand the time series movements based on the events described on the text data. We have tested our model on two datasets: first, we used political news to explain the US president&#x2019;s disapproval ratings; then, we considered a corpus of economic news to explain the financial returns of 4 different multinational corporations. Our experiments show that an appropriate selection of hyperparameters (via repeated random subsampling validation and Bayesian optimization) leads to significant correlations: both an intrinsic baseline and state of the art methods were significantly outperformed by TSITM in MSE, MAE and out-of-sample <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>, according to our hypothesis tests. We believe that this framework can be useful in the context of reputational risk management.

Meng Yuan, Lei Li, Zhezhuang Xu

In biaxial contouring control applications, the inherent structural flexibility of machines can lead to position discrepancies between the manipulator and actuator, and thus deteriorate the manufacturing performance, especially when the controller is designed without available end-effector side feedback. In this work, we focus on the end-effector contouring control problem for industrial machines with position-dependent flexibility to improve the contouring performance while eliminating the effect of mechanical vibration. A model for the widely used cantilever beam machine is developed to describe the dynamics of the end-effector by capturing the rotation and coupled dynamics between axes. The proposed model is validated through experiment and systematically reduced to switched linear time-invariant models for controller design. By adopting the extended state observer, the proposed control architecture decouples the dynamics between the X and Y-axis and simplifies the controller design process. The model predictive control method is utilised for improving the contouring performance while reducing mechanical vibration. The efficacy of the proposed control framework is demonstrated and validated on the designed high-fidelity model. Performance comparisons between the proposed approach with benchmark controllers are presented.

Daniele Di Pompeo, Michele Tucci

Software model optimization is the task of automatically generate design alternatives, usually to improve quality aspects of software that are quantifiable, like performance and reliability. In this context, multi-objective optimization techniques have been applied to help the designer find suitable trade-offs among several non-functional properties. In this process, design alternatives can be generated through automated model refactoring, and evaluated on non-functional models. Due to their complexity, this type of optimization tasks require considerable time and resources, often limiting their application in software engineering processes. In this paper, we investigate the effects of using a search budget, specifically a time limit, to the search for new solutions. We performed experiments to quantify the impact that a change in the search budget may have on the quality of solutions. Furthermore, we analyzed how different genetic algorithms (i.e., NSGA-II, SPEA2, and PESA2) perform when imposing different budgets. We experimented on two case studies of different size, complexity, and domain. We observed that imposing a search budget considerably deteriorates the quality of the generated solutions, but the specific algorithm we choose seems to play a crucial role. From our experiments, NSGA-II is the fastest algorithm, while PESA2 generates solutions with the highest quality. Differently, SPEA2 is the slowest algorithm, and produces the solutions with the lowest quality.