Hasil untuk "Mechanical engineering and machinery"

Ahmad Farooq, Kamran Iqbal

The software engineering research community faces a systemic crisis: peer review is failing under growing submissions, misaligned incentives, and reviewer fatigue. Community surveys reveal that researchers perceive the process as "broken." This position paper argues that these dysfunctions are mechanism design failures amenable to computational solutions. We propose modeling the research community as a stochastic multi-agent system and applying multi-agent reinforcement learning to design incentive-compatible protocols. We outline three interventions: a credit-based submission economy, MARL-optimized reviewer assignment, and hybrid verification of review consistency. We present threat models, equity considerations, and phased pilot metrics. This vision charts a research agenda toward sustainable peer review.

محمد خاکباز, مسعود جوادی, رضا سرخوش

روش شکل‌دهی مقطع به عنوان یک روش مؤثر و رایج برای کاهش سطح مقطع راداری اهداف در سیستم‌های دفاعی و جنگ الکترونیکی شناخته می‌شود. در این پژوهش، کاهش سطح مقطع راداری و افت سیگنال الکترومغناطیسی موج تابیده به سطح مقاطع مخروطی و رویه‌های درجه دوم از جنس آلومینیوم، به‌صورت عددی در نرم‌افزار کامسول بررسی می‌شود. این تحقیق با بیان طرح مسئله و معادلات بنیادین امواج الکترومغناطیسی بر اساس روابط ماکسول و معادلات جذب و گذردهی نیکلسون-راس-ویر در باند ایکس آغاز می‌شود.ستاپ تست برای تعریف پارامترهای تابش و بازتاب بر روی سطح مقاطع مخروطی غیر اقلیدسی و درجه دوم، شامل اشکالی نظیر بیضی‌گون، استوانه، مخروط، هذلولوی یکپارچه و سهمی‌گون بیضوی طراحی می‌شود. شبیه‌سازی‌ها در محدوده باند ایکس (۸-۱۲ گیگاهرتز) و در شرایط جوی خاص با یک پورت ورودی و یک پورت خروجی انجام خواهد شد. نتایج شامل ماتریس پراکندگی و درایه اول آن (S11) است که رفتار بازتابی موج را تحت تأثیر جنس و شکل سازه نشان می‌دهد. به‌علاوه، نتایج حاکی از آن است که مقطع سهمی‌گون بیضوی بیش‌ترین افت انعکاس الکترومغناطیسی را، معادل -8.2 دسی‌بل، در بازه فرکانسی 10 گیگاهرتز ثبت کرده که معادل جذب بالای 80 تا 90 درصد موج تابشی است.

Luca Cocconi, Paolo Malgaretti, Holger Stark

Isothermal information engines operate by extracting net work from a single heat bath through measurement and feedback control. In this work, we analyze a realistic active Szilard engine operating on a single active particle by means of steric interaction with an externally controlled mechanical element. In particular, we provide a comprehensive study of how finite measurement accuracy affects the engine's work and power output, as well as the cost of operation. Having established the existence of non-trivial optima for work and power output, we study the dependence of their loci on the measurement error parameters and identify conditions for their positivity under one-shot and cyclic engine operation. By computing a suitably defined information efficiency, we also demonstrate that this engine design allows for the violation of Landauer's bound on the efficiency of information-to-work conversion. Notably, the information efficiency for one-shot operation exhibits a discontinuous transition and a non-monotonic dependence on the measurement precision. Finally, we show that cyclic operation improves information efficiency by harvesting residual mutual information between successive measurements.

Robin Kimmel, Judith Michael, Andreas Wortmann et al.

Digital twins promise a better understanding and use of complex systems. To this end, they represent these systems at their runtime and may interact with them to control their processes. Software engineering is a wicked challenge in which stakeholders from many domains collaborate to produce software artifacts together. In the presence of skilled software engineer shortage, our vision is to leverage DTs as means for better rep- resenting, understanding, and optimizing software engineering processes to (i) enable software experts making the best use of their time and (ii) support domain experts in producing high-quality software. This paper outlines why this would be beneficial, what such a digital twin could look like, and what is missing for realizing and deploying software engineering digital twins.

Tommaso Poliero, Matteo Sposito, Stefano Toxiri et al.

Prabhu Azhagapillai, Karthikeyan Gopalsamy, Israa Othman et al.

Organic pollutants such as 4-nitrophenol (4-NP) pose serious environmental extortions due to their chemical stability for which efficient catalytic materials are indispensable in treating them. In this regard, the present work involves the synthesis of two different types of ferrites (NiFe2O4, and CuFe2O4), and a combination of NixCuxFe2O4 with various ratios that systemically work as efficient photocatalysts without any additional reducing agents is reported. The structural, and morphological properties of NiFe2O4, CuFe2O4, and NiCuFe2O4 were characterized by XRD, FT-IR, SEM, and HRTEM techniques. Then, the catalytic role of individual ferrite catalysts was evaluated towards catalytic reduction of 4-NP under visible light. The progress dye reduction was examined via UV–vis spectrophotometry. The effect of various concentrations, and reduction time were investigated. The kinetic rate constants determined for NiFe2O4, CuFe2O4, and NixCuxFe2O4 revealed that Ni and Cu in bimetallic ferrites promoted the reduction reaction under visible light. The results demonstrated that the photo-reduction efficiency of the Ni0.7Cu0.3Fe2O4 catalyst over 4-NP (conc. 10 ppm) to 4-AP was determined as 82 % under 120 miniutes with good recyclability up to six cycles. The mechanism of photocatalytic reduction of ferrites without the use of a reducing agent was studied. Such facile and productive ferrite materials could be employed as efficient photocatalysts for the reduction of toxic organic contaminants in environmental treatment.

Julian Frattini

[Context and Motivation]: The quality of requirements specifications impacts subsequent, dependent software engineering activities. Requirements quality defects like ambiguous statements can result in incomplete or wrong features and even lead to budget overrun or project failure. [Problem]: Attempts at measuring the impact of requirements quality have been held back by the vast amount of interacting factors. Requirements quality research lacks an understanding of which factors are relevant in practice. [Principal Ideas and Results]: We conduct a case study considering data from both interview transcripts and issue reports to identify relevant factors of requirements quality. The results include 17 factors and 11 interaction effects relevant to the case company. [Contribution]: The results contribute empirical evidence that (1) strengthens existing requirements engineering theories and (2) advances industry-relevant requirements quality research.

Marco Vieira

LLMs are transforming software engineering by accelerating development, reducing complexity, and cutting costs. When fully integrated into the software lifecycle they will drive design, development and deployment while facilitating early bug detection, continuous improvement, and rapid resolution of critical issues. However, trustworthy LLM-driven software engineering requires addressing multiple challenges such as accuracy, scalability, bias, and explainability.

Xiangping Chen, Xing Hu, Yuan Huang et al.

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many papers have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However, although several surveys have provided overall pictures of the application of deep learning techniques in software engineering, they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically.

Yilin Wang, Yifei Yu, Kong Sun et al.

In industry, the reliability of rotating machinery is critical for production efficiency and safety. Current methods of Prognostics and Health Management (PHM) often rely on task-specific models, which face significant challenges in handling diverse datasets with varying signal characteristics, fault modes and operating conditions. Inspired by advancements in generative pretrained models, we propose RmGPT, a unified model for diagnosis and prognosis tasks. RmGPT introduces a novel generative token-based framework, incorporating Signal Tokens, Prompt Tokens, Time-Frequency Task Tokens and Fault Tokens to handle heterogeneous data within a unified model architecture. We leverage self-supervised learning for robust feature extraction and introduce a next signal token prediction pretraining strategy, alongside efficient prompt learning for task-specific adaptation. Extensive experiments demonstrate that RmGPT significantly outperforms state-of-the-art algorithms, achieving near-perfect accuracy in diagnosis tasks and exceptionally low errors in prognosis tasks. Notably, RmGPT excels in few-shot learning scenarios, achieving 82\% accuracy in 16-class one-shot experiments, highlighting its adaptability and robustness. This work establishes RmGPT as a powerful PHM foundation model for rotating machinery, advancing the scalability and generalizability of PHM solutions. \textbf{Code is available at: https://github.com/Pandalin98/RmGPT.

Mudassir Shams, Nasreen Kausar, Naveed Khan et al.

Numerous real-world applications can be solved using the broadly adopted notions of intuitionistic fuzzy sets, Pythagorean fuzzy sets, and q-rung orthopair fuzzy sets. These theories, however, have their own restrictions in terms of membership and non-membership levels. Because it utilizes benchmark or control parameters relating to membership and non-membership levels, this theory is particularly valuable for modeling uncertainty in real-world problems. We propose the unique concept of linear Diophantine fuzzy set with benchmark parameters to overcome these restrictions. Different numerical, analytical, and semi-analytical techniques are used to solve linear systems of equations with several fuzzy numbers, such as intuitionistic fuzzy number, triangular fuzzy number, bipolar fuzzy number, trapezoidal fuzzy number, and hexagon fuzzy number. The purpose of this research is to solve a fuzzy linear system of equations with the most generalized fuzzy number, such as Triangular linear Diophantine fuzzy number, using an analytical technique called Homotopy Perturbation Method. The linear systems co-efficient are crisp when the right hand side vector is a triangular linear Diophantine fuzzy number. A numerical test examples demonstrates how our newly improved analytical technique surpasses other existing methods in terms of accuracy and CPU time. The triangular linear Diophantine fuzzy systems of equations’ strong and weak visual representations are explored.

Liu Fen, Zhang Jin, Sang Hongqiang et al.

The wave glider is a new type of unmanned observation platform on water surface, which can generate forward thrust by obtaining wave energy through the swing of submerged glider's hydrofoils. By analyzing the wave energy loss caused by the umbilical cable inclination angle of the submerged glider of the conventional wave glider, a hydrofoil swing mechanism that can change the lower limit of hydrofoil swing with the change of the umbilical cable inclination angle is designed. Firstly, the adjustment ability of the lower limit follow-up mechanism to the hydrofoil swing angle is simulated and analyzed by the computational fluid dynamics software Fluent; secondly, the prototype of the lower limit follow-up mechanism is designed by adding a set of parallelogram transmission mechanism in the conventional submerged glider. A large wave simulation test platform is setup and the propulsion performance of the prototype is verified by pool tests. The research shows that the new hydrofoil swing mechanism can improve the propulsion performance of the submerged glider.

Sajid Ibrahim Hashmi, Jouni Markkula

One of the objectives of software engineering education is to make students to learn essential teamwork skills. This is done by having the students work in groups for course assignments. Student team composition plays a vital role in this, as it significantly affects learning outcomes, what is learned, and how. The study presented in this paper aims to better understand the student team composition in software engineering education and investigate the factors affecting it in the international software engineering education context. Those factors should be taken into consideration by software engineering teachers when they design group work assignments in their courses. In this paper, the initial findings of the ongoing Action research study are presented. The results give some identified principles that should be considered when designing student team composition in software engineering courses.

Dedik Romahadi, Dafit Feriyanto, Wiwit Suprihatiningsih et al.

Expert system design is an effective and sophisticated way of diagnosing a fault in a 12 kW DC Motor. This study aims to design an ANN system to determine damage to the motor. The research method uses spectrum data from the vibration analyzer which is collected based on different types of damage. The training data patterns from the spectrum characteristics to be used in the system, the goal is that the systems can recognize the patterns that have been made. The training data patterns that have been successfully recognized by the system are then tested. The results of training and ANN testing are quite good, with the greatest Cross-Entropy value of 9.94, having 0% error value, the largest Mean Square Error value 8.33e-6 and the smallest regression 0.998. A testing of 8 new spectrums resulted in accurate predictions.

Eriks Klotins, Tony Gorschek

Software is becoming a critical component of most products and organizational functions. The ability to continuously improve software determines how well the organization can respond to market opportunities. Continuous software engineering promises numerous advantages over sprint-based or plan-driven development. However, implementing a continuous software engineering pipeline in an existing organization is challenging. In this invited position paper, we discuss the adoption challenges and argue for a more systematic methodology to drive the adoption of continuous engineering. Our discussion is based on ongoing work with several industrial partners as well as experience reported in both state-of-practice and state-of-the-art. We conclude that the adoption of continuous software engineering primarily requires analysis of the organization, its goals, and constraints. One size does not fit all purposes, meaning that many of the principles behind continuous engineering are relevant for most organizations, but the level of realization and the benefits may still vary. The main hindrances to continuous flow of software arise from sub-optimal organizational structures and the lack of alignment. Once those are removed, the organization can implement automation to further improve the software delivery.

Abdul Mukhlis Ritonga, Masrukhi Masrukhi, Azis Imam Safi’i

Biogas is a combustible gas produced from the fermentation process of organic materials by anaerobic bacteria. Biogas can be made by using a digester. A digester is a place where the process of decomposing organic matter by bacteria. The result of biogas still contains impurity gases, so that the quality of biogas is not good. Therefore, efforts to filter the gas are necessary. The purifier is a device to filter a gas. The use of purifiers in a series of digester installations aims to filter out unnecessary gases. The purpose of this research is to design a down-up purifier type biogas purification plant, to determine the changes in substrate characteristics during fermentation and conduct a gas quality test after purification. The results showed that the biogas installation type down-up purifier was designed and assembled using 150 liter drums for gas digesters and reservoirs, 1/2 inch hoses for connecting, 2 purifiers for purification and activated charcoal adsorbents. The C/N ratio is 36.37, an average substrate temperature of 28.62oC and an average pH of 5.9. Initial and final Biological Oxygen Demand (BOD) values are 960.12 mg/l and 9.312.53 mg/l. The initial and final Chemical Oxygen Demand (COD) values are 313,500.00 mg/l and 29,100.00 mg/l. Then Total Solid (TS) decreased by 1.45% and Volatile Solid (VS) increased by 0.21%. The use of activated charcoal adsorbents in the two purifiers can reduce CO2 gas content by 83.79% in biogas with the most optimal purification time of 60 minutes.

Sunday Oke, Stephen Chidera Nwafor, Chris Abiodun Ayanladun

In recent years, novel products from out–of–use A356 alloy engine components are increasingly produced for the automobile industries. Despite being a promising method the sand casting of these products reveals an inadequately understood cast geometry phenomenon for the process. At present, there is no technical solution to the optimisation of cast geometries for A356 alloy reconfigured into composites through organic matter reinforcements. This paper models and analyse sand casting process product geometries in a two–phase method. It utilises the response surface methodology with data on inputs and outputs to create the regression. Volume and density of the first casting process and the weight loss were evaluated for the various groupings of casting process variables, including length, weight, height, width of product for the first casting, weight, length, breadth of the product for the second casting, and the total weight of organic materials. The input and output associations were established in two models of regression analysis representing the central composite design, CCD. The influences of the cast geometrical variables on the evaluated responses were analysed. Furthermore, the predictive accuracy of the two regression models was evaluated. Results revealed that the applied CCD and the regression models reveals statistical adequacy and are competent to predict accurately.

Haiou Sun, Meng Wang, Zhongyi Wang et al.

To improve the understanding of unsteady flow in modern advanced axial compressor, unsteady simulations on full-annulus multi-stage axial compressor are carried out with the harmonic balance method. Since the internal flow in turbomachinery is naturally periodic, the harmonic balance method can be used to reduce the computational cost. In order to verify the accuracy of the harmonic balance method, the numerical results are first compared with the experimental results. The results show that the internal flow field and the operating characteristics of the multi-stage axial compressor obtained by the harmonic balance method coincide with the experimental results with the relative error in the range of 3%. Through the analysis of the internal flow field of the axial compressor, it can be found that the airflow in the clearance of adjacent blade rows gradually changes from axisymmetric to non-axisymmetric and then returns to almost completely axisymmetric distribution before the downstream blade inlet, with only a slight non-axisymmetric distribution, which can be ignored. Moreover, the slight non-axisymmetric distribution will continue to accumulate with the development of the flow and, finally, form a distinct circumferential non-uniform flow field in latter stages, which may be the reason why the traditional single-passage numerical method will cause certain errors in multi-stage axial compressor simulations.

Stephen P. Long

Arquimedes Canedo, Palash Goyal, Di Huang et al.

Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supported by a suite of software tools including integrated development environments (IDE), hardware configurators, compilers, and runtimes. These tools focus on the automation code itself, but leave the automation engineer unassisted in their decision making. This can lead to increased time for software development because of imperfections in decision making leading to multiple iterations between software and hardware. To address this, this paper defines multiple challenges often faced in automation engineering and propose solutions using machine learning to assist engineers tackle such challenges. We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators. We validate our architecture on two real datasets consisting of 2,927 Arduino projects, and 683 Programmable Logic Controller (PLC) projects. Our results show that paragraph embedding techniques can be utilized to classify automation using code snippets with precision close to human annotation, giving an F1-score of 72%. Further, we show that such embedding techniques can help us find similar code snippets with high accuracy. Finally, we use autoencoder models for hardware recommendation and achieve a p@3 of 0.79 and p@5 of 0.95.