Hasil untuk "Industrial engineering. Management engineering"

D. Gupta, B. Denton

V. Venkatasubramanian, R. Rengaswamy, K. Yin et al.

Dongli Deng, Sen Zhao, Meixia Miao

Local Differential Privacy (LDP) and its personalized variants (PLDP) have been widely used for privacy-preserving data analytics. However, existing schemes often enforce a uniform indistinguishability level among users, failing to accommodate the nuanced privacy needs of diverse individuals. To address this, we propose User-Distinguished Local Differential Privacy (UDPLDP), a novel framework that formalizes user-level distinguishability to support more flexible, non-uniform privacy budgets. Under this framework, we tackle the fundamental task of frequency range queries, namely UDPLDP-Tree, which overcomes the challenge due to limited user-level distinguishability, insufficient robustness in estimation under complex data distributions, and the assumption of uniform privacy requirements across different attributes in existing multi-dimensional schemes. To demonstrate the effectiveness, we conduct extensive experiments and the results show that UDPLDP-Tree reduces the mean squared error (MSE) by about 30–50% compared with a recent state-of-the-art baseline.

Golnoush Abaei, Mojtaba Shahin, Maria Spichkova

The inclusion of internship courses in Software Engineering (SE) programs is essential for closing knowledge gaps and improving graduates' readiness for the software industry. Our study focuses on year-long internships at RMIT University (Melbourne, Australia), which offers in-depth industry engagement. We analysed how the course evolved over the last 10 years to incorporate students' needs and summarised the lessons learned that can be helpful for other educators supporting internship courses. Our qualitative analysis of internship data based on 91 reports during 2023-2024 identified three challenge themes the students faced, and which courses were found by students to be particularly beneficial during their internships. On this basis, we proposed recommendations for educators and companies to help interns overcome challenges and maximise their learning experience.

Subhalaxmi Dey, Surender Ontela, P.K. Pattnaik et al.

In recent trends science and technology is developed due to the utilization of modern devices of high quality and their longevity with potential efficiency. The implementation of nanoparticles now characterizes the effectiveness and efficiency. Specifically, in biomedical research drug delivery into the target, hyperthermia treatment for cancer, etc. the use of nanofluid is vital. The present article brings the characteristic of the blood-based tri-hybrid nanofluid through a porous channel embedding within a porous matrix with the interaction of magnetization and Darcy-Forchheimer inertial drag in the flow behavior. Further, the inclusion of thermal radiation, and heat source energies the heat transport properties. The formulated model for the interaction of alloy nanoparticles AA7072 and AA7075 with Zirconium oxide ZrO2 in the base liquid blood is characterized by their physical properties. The designed model is transformed into a non-dimensional form with the utilization of similarity rules. Further, a semi-analytical approach Adomian Decomposition Method (ADM) is proposed for the solution of the model. The validation with the existing article shows the convergence properties of the current methodology and the significant behavior of the factors involved in the flow phenomena are presented through graphs. Finally, the important findings are reported as; The enhanced Reynolds number decelerates the inertia force and a velocity profile shows a dual characteristic for the increasing deformation factor. Further, in comparison to the single and hybrid nanofluid, the tri-hybrid nanofluid encourages the fluid temperature due to the increasing thermal conductivity.

Mahdis Haddadi, Saman A. Gorji, Samson S. Yu

Inertia is a critical factor in maintaining the frequency stability of power systems. However, the growing integration of power electronics-based renewable energy sources (RESs) has significantly reduced system inertia. AC and dc microgrids have emerged as key solutions for integrating RESs. Unlike traditional synchronous generators, power electronic converters interfacing RESs lack inherent inertia and damping, posing challenges to the control and stability of these microgrids. To address these challenges, virtual inertia control strategies, which emulate the behavior of synchronous generators, have been widely adopted to enhance the stability of ac microgrids. Drawing on the analogies between ac and dc systems, similar virtual inertia concepts have been extended to dc microgrids, demonstrating their potential to improve system stability. This article provides a comprehensive review of inertia enhancement strategies for dc microgrids, examining their key features, benefits, and limitations. The analogy between synchronous generators/dc machines and energy storage systems is explored, with a particular focus on the implementation of virtual inertia and damping control in energy storage converters as a promising solution to mitigate power fluctuations. In addition, this article investigates the grid-forming and grid-following converter analogies in ac and dc microgrids. Various stability analysis methods applied to inertia enhancement strategies are also reviewed, offering readers a comprehensive understanding of the current state of research. By addressing the conceptual and technical analogies between ac and dc systems, this review aims to provide valuable insights for developing advanced control strategies for next-generation microgrids.

Jürgen Soom, Isabel Boavida, Renan Leite et al.

The need for efficient approaches to track and assess fish behavior in rivers impacted by hydropeaking is increasing. Nonetheless, employing an automated camera system for underwater monitoring requires that the algorithms function under highly variable environmental conditions, which affect the ability to detect and assess fish size. Additionally, there is a lack of openly accessible freshwater fish classification and size estimation datasets. To address these limitations, we propose a binocular underwater fish monitoring system capable of real-time fish detection and size estimation. The system was deployed and tested over one week in two Portuguese rivers affected by hydropeaking. The week-long analysis also provided new insights regarding wild fish behavior in rivers affected by hydropeaking. Results indicate that hydropeaking strongly influences how fish may use instream flow refuges during hydropeaking. Fish were less frequently detected in the flow refuge during peak flow events, suggesting that the flow conditions created habitat instability and difficulty accessing the flow refuge. In contrast, fish in the non-hydropeaking river consistently used refuge areas, reinforcing their importance as shelter during natural flow variations. This study demonstrates the potential of a computer vision-based pipeline for real-time, fully automated fish monitoring of hydropeaking’s impacts on riverine fish. Additionally, we provide PTFish, an open dataset with 18,523 manually annotated frames featuring infrared and color video frames. These findings emphasize that automated, camera-based solutions for hydropeaking monitoring can be used to develop evidence-based mitigation strategies to sustain fish populations in rivers impacted by hydropeaking.

Amaratou Mahamadou Saley, Thierry Moyaux, Aïcha Sekhari et al.

The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is crucial for reducing downtime and operational costs. However, the effectiveness of data-driven methodologies in the nuclear sector requires extensive domain knowledge due to the complexity of the systems involved. Thus, this paper proposes a novel predictive maintenance methodology that combines data-driven techniques with domain knowledge from a nuclear equipment. The methodological originality of this paper is located on two levels: highlighting the limitations of purely data-driven approaches and demonstrating the importance of knowledge in enhancing the performance of the predictive models. The applicative novelty of this work lies in its use within a domain such as a nuclear industry, which is highly restricted and ultrasensitive due to security, economic and environmental concerns. A detailed real-world case study which compares the current state of equipment monitoring with two scenarios, demonstrate that the methodology significantly outperforms purely data-driven methods in failure prediction. While purely data-driven methods achieve only a modest performance with a prediction horizon limited to 3 h and a F1 score of 56.36%, the hybrid approach increases the prediction horizon to 24 h and achieves a higher F1 score of 93.12%.

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.

Mauro Marcelino, Marcos Alves, Bianca Trinkenreich et al.

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

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.

Gianluigi Rozza, Oliver Schütze, Nicholas Fantuzzi

This Special Issue comprises the first collection of papers submitted by the Editorial Board Members (EBMs) of the journal <i>Mathematical and Computational Applications</i> (MCA), as well as outstanding scholars working in the core research fields of MCA [...]

Jakub Pokrywka

Mobayode O. Akinsolu, Khalil Zribi

In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>T</mi></mrow></semantics></math></inline-formula>), <i>k</i>-NN20 (<i>k</i>-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>S</mi></mrow></semantics></math></inline-formula>), DTR (decision tree regressor) ranks first with average and median values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.6784</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.8691</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s), and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.9929</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.8806</mn><mo>×</mo><msup><mn>10</mn><mn>6</mn></msup></mrow></semantics></math></inline-formula> observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><mi>T</mi><mi>R</mi></mrow></semantics></math></inline-formula> and <i>k</i>-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

Romulus Costache, Hazem Ghassan Abdo, Arun Pratap Mishra et al.

AbstractIn this work, the vulnerability to flooding in the Prahova River basin was calculated and analyzed using advanced methods and techniques. Thus, 2 hybrid models represented by Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) and Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) were generated, which had as input data the values of 10 flood predictors and a number of 158 points where historical floods occurred. In the first step, the Certainty Factor values were calculated, which were then used in the Fuzzy-Analytical Hierarchy Process and Multiclass Alternating Decision Tree models. It should be mentioned that the Multiclass Alternating Decision Tree model was optimized with the help of the Iterative Classifier Optimizer. In the case of both ensemble models the slope angle was the most important flood conditioning factor. Moreover, according to Certainty Factor modelling the 8 classes/categories achieved the maximum value of 1. Next, the susceptibility to floods on the surface of the study area was derived. On average, about 20% of the study area has areas with high and medium susceptibility to flash floods. After evaluating the quality of the models through Receiver Operating Characteristics (ROC) Curve, the following results emerged: Success Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.985) and Flood Potential Index (FPI) Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.967); Prediction Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.952) and Flood Potential Index Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.913). At the same time, the accuracies of the models were: Training dataset − 0.943 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.931 (Fuzzy-Analytical Hierarchy Process – Certainty Factor); Validating dataset − 0.935 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.926 (Fuzzy-Analytical Hierarchy Process – Certainty Factor). As main conclusion, it can be mentioned that the 2 ensemble models outperform the previous machine learning models applied on the same study area before.

Jishi Zheng, Junhui Li, Zhigang Ding et al.

To prevent products with missing character information from reaching the market, manufacturers need an automatic character recognition method. One of the key problems of this recognition method is to recognise text under complex package patterns. In addition, some products use dot matrix characters to reduce printing costs, which makes text extraction more difficult. We propose a character detection algorithm using DBNet as the base network, combined with the Convolutional Block Attention Module (CBAM) to improve its feature extraction of characters in complex contexts. After the character area has been located by the detection algorithm, it is intercepted and fed into a fully convolutional character recognition network to achieve print character recognition. We use ResNet as the backbone network and CTC loss for training. In addition, the CBAM module was added to the backbone network to enhance its recognition of dot matrix characters. The algorithm was finally deployed on the jetson nano. The experimental results show that the character detection accuracy reaches 97.9%, an improvement of 1.9% compared to the original network. As for the character recognition algorithm, the inference speed is doubled when deployed to the nano platform compared to the CRNN network, with an accuracy of 97.8%.

Carolyn A Lin, Xihui Wang, Linda Dam

Extant research addressing the relations between TikTok videos and sustainable apparel consumption behavior is limited. This study explores these relations by testing the following theories and constructs: social consciousness, prior sustainable apparel purchasing, attitude toward TikTok videos (featuring sustainable apparel content), and theory of planned behavior. Results from an online survey supported the proposed conceptual framework, suggesting that cognitive, affective, and behavioral factors relevant to sustainable apparel consumption had a positive influence on sustainable apparel purchase intention.

S. Jazebi, F. de León, A. Nelson

This two-part paper is intended to inform power system engineers, electrical engineering academicians, and suppliers of electrical apparatus of the threat of wildfires initiated from mal-operation of electrical grids and the unexploited opportunity to develop proper solutions and preventive means to such lethal events. This part (Part I) reviews and categorizes research in different fields of science and industrial projects that attempt to address wildfire issues. The topics include prediction and prevention means, detection methods, monitoring and surveillance techniques, suppression methods, allocation and mapping algorithms, and a summary of research and educational efforts. Subsequently, this paper highlights the damages and negative effects that a wildfire can cause to the electric grid and the interruptions to its continuous operation. Finally, this paper analyzes and categorizes the various scenarios of faulty electrical networks that may lead to wildfires. Part I of this paper provides the ground work and information for the solutions and discussions presented in Part II.

T. Berger, J. Steghöfer, T. Ziadi et al.

Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.

L. Bosman, Bhavana Kotla, Aasakiran Madamanchi et al.

ABSTRACT Artificial intelligence (AI) is a rapidly developing field with growing importance in engineering, mainly as it serves to better understand and manipulate big data. However, the literature is sparse on how to educate engineers on the use of AI applications. For many years, a role-playing simulation known as the ‘Beer Distribution Game' has been a staple of industrial engineering education and an established vehicle for teaching the fundamentals of supply chain management (SCM) technology. The purpose of the study is to suggest an alternative and effective way to introduce AI and web-based games to students. This paper introduces and applies two types of web-based AI-enabled tools in a second-year industrial engineering classroom including OpexAnalytics (a web-based AI-enabled Beer Distribution Game learning module) and CompareAssess (a web-based AI-enabled tool to promote ‘learning by evaluation’). Findings provide evidence of this 5-week web-based AI-enabled learning module's effectiveness in improving student perceptions and learning outcomes related to the intersection between supply chain management and artificial intelligence. The intention for this module was not to sufficiently prepare students for data science and artificial intelligence work assignments; however, this module offers a starting point for further skill development in higher-level coursework.