1,720,957 research outputs found
ARTIFICIAL INTELLIGENCE FOR SYSTEMS ENGINEERING COMPLEXITY: A REVIEW ON THE USE OF AI AND MACHINE LEARNING ALGORITHMS
Get PDFThis review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing the complexities of systems engineering. It highlights how AI and ML are revolutionizing system design, integration, and lifecycle management by enabling automated design optimization, predictive maintenance, and efficient configuration management. These technologies allow for the analysis of large datasets to predict system failures and optimize performance, thereby enhancing the reliability and sustainability of engineering systems. Despite the promising applications, the integration of AI into systems engineering presents challenges, including technical hurdles, ethical considerations, and the need for comprehensive education and training. The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively. Concluding thoughts underscore AI's potential to redefine systems engineering, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements.
Keywords: Artificial Intelligence, Machine Learning, Systems Engineering, Automated Design, Predictive Maintenance, Configuration Management, Education and Training, Technology Integration
OPTIMIZATION IN SYSTEMS ENGINEERING: A REVIEW OF HOW DATA ANALYTICS AND OPTIMIZATION ALGORITHMS ARE APPLIED
Get PDFThis research review article provides a comprehensive examination of optimization techniques in systems engineering, highlighting their pivotal role in enhancing system performance, efficiency, and problem-solving capabilities. Through a structured exploration encompassing theoretical frameworks, methodologies, applications, and significant findings, the article synthesizes current knowledge and advancements in the field. It delves into various optimization methods, including traditional linear and nonlinear programming, alongside emerging trends such as swarm intelligence, nature-inspired algorithms, and the integration of machine learning. Case studies and recent research findings underscore the practical implications and effectiveness of these techniques across diverse engineering challenges. The review identifies key insights, demonstrating the versatility and potential of optimization techniques to drive innovation in systems engineering. Furthermore, it offers recommendations for future research directions and practical applications, emphasizing the importance of interdisciplinary approaches, algorithm development, and the adoption of advanced techniques in industry practices. This article aims to inform researchers and practitioners alike, fostering the continued evolution and application of optimization techniques in systems engineering.
Keywords: Optimization Techniques, Systems Engineering, Swarm Intelligence, Machine Learning, Algorithm Development
THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW
Get PDFThis research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved. To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems
A REVIEW OF DATA-DRIVEN DECISION MAKING IN ENGINEERING MANAGEMENT
Get PDFThis research review article explores the transformative impact of data-driven decision-making (DDDM) across various sectors, highlighting the integration of advanced analytics to enhance organizational efficiency, innovation, and strategic planning. Despite the potential benefits, the adoption of DDDM poses significant challenges, including data quality issues, integration complexities, and the need for a cultural shift towards valuing data analytics. Through a comprehensive analysis of recent research and case studies, this article synthesizes key findings, emerging trends, and future research areas in DDDM. It provides practical recommendations for practitioners aiming to implement and optimize DDDM processes, emphasizing the importance of fostering a data-driven culture, investing in robust data infrastructure, and ensuring the ethical use of data. Additionally, the article offers suggestions for continuous improvement and adaptation to technological advancements, advocating for regular strategy reviews, monitoring emerging trends, and fostering innovation. By addressing these challenges and leveraging the outlined recommendations, organizations can unlock the full potential of DDDM, driving significant advancements in efficiency, competitiveness, and strategic decision-making in the digital age.
Keywords: Data-driven Decision Making, Engineering Management, Machine Learning, Big Data, Advanced Analytics, Organizational Efficiency, Data Quality and Infrastructure, Technological Advancements
THE FUTURE OF ENGINEERING EDUCATION: A DATA ANALYTICS APPROACH
Get PDFThe landscape of engineering education is undergoing a significant transformation, driven by the integration of data analytics and machine learning (ML) technologies. This paper explores the potential of these technologies to revolutionize engineering education by personalizing learning experiences, predicting student performance, and enhancing curriculum development. Through a comprehensive review of current practices and case studies, we examine the application of data-driven approaches in identifying individual learning patterns, tailoring educational content, and implementing adaptive learning technologies. We also investigate the role of predictive analytics in forecasting academic success and enabling proactive interventions for at-risk students. Furthermore, the paper discusses the challenges and ethical considerations associated with the adoption of these technologies, including data privacy concerns and the digital divide. Our analysis highlights the importance of collaboration among educators, policymakers, and technologists to navigate these challenges and fully realize the benefits of a data-analytics approach in engineering education. The paper concludes with a vision for the future, emphasizing the need for continuous innovation and adaptation in curricula to prepare engineering graduates for the evolving demands of the workforce and society. This investigation sheds light on the transformative potential of data analytics and ML in engineering education and provides a roadmap for its successful integration into teaching and learning processes.
Keywords: Data Analytics, Machine Learning, Personalized Learning, Predictive Analytics, Curriculum Development, Adaptive Learning Technologies, Student Performance Prediction, Technological Integration
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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