Technology Characterization Models and Their Use in Systems Design

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Robert R. Parker ◽  
Edgar Galvan ◽  
Richard J. Malak
Keyword(s):  
Decision Making ◽  
Systems Engineering ◽  
Systems Design ◽  
Computational Effort ◽  
Engineering Problem ◽  
Component Technology ◽  
System Level ◽  
Support Vector ◽  
Engineering Project ◽  
Set Based Design

Prior research suggests that set-based design representations can be useful for facilitating collaboration among engineers in a design project. However, existing set-based methods are limited in terms of how the sets are constructed and in their representational capability. The focus of this article is on the problem of modeling the capabilities of a component technology in a way that can be communicated and used in support of system-level decision making. The context is the system definition phases of a systems engineering project, when engineers still are considering various technical concepts. The approach under investigation requires engineers familiar with the component- or subsystem-level technologies to generate a set-based model of their achievable technical attributes, called a technology characterization model (TCM). Systems engineers then use these models to explore system-level alternatives and choose the combination of technologies that are best suited to the design problem. Previously, this approach was shown to be theoretically sound from a decision making perspective under idealized circumstances. This article is an investigation into the practical effectiveness of different TCM representational methods under realistic conditions such as having limited data. A power plant systems engineering problem is used as an example, with TCMs generated for different technical concepts for the condenser component. Samples of valid condenser realizations are used as inputs to the TCM representation methods. Two TCM representation methods are compared based on their solution accuracy and computational effort required: a Kriging-based interpolation and a machine learning technique called support vector domain description (SVDD). The results from this example hold that the SVDD-based method provides the better combination of accuracy and efficiency.

scholarly journals The Naval Postgraduate School’s Department of Systems Engineering Approach to Mission Engineering Education through Capstone Projects

Systems ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 38 ◽  
Author(s):  
Douglas L. Van Bossuyt ◽  
Paul Beery ◽  
Bryan M. O’Halloran ◽  
Alejandro Hernandez ◽  
Eugene Paulo
Keyword(s):  
Systems Engineering ◽  
System Analysis ◽  
System Modeling ◽  
System Level ◽  
Naval Postgraduate School ◽  
Engineering Project ◽  
Educational Approach ◽  
Capstone Courses ◽  
Engineering Department ◽  
Capstone Projects

This article presents an educational approach to applied capstone research projects using a mission engineering focus. It reviews recent advances in mission engineering within the Department of Defense and integrates that work into an approach for research within the Systems Engineering Department at the Naval Postgraduate School. A generalized sequence of System Definition, System Modeling, and System Analysis is presented as an executable sequence of activities to support analysis of operational missions within a student research project at Naval Postgraduate School (NPS). That approach is detailed and demonstrated through analysis of the integration of a long-range strike capability on a MH-60S helicopter. The article serves as a demonstration of an approach for producing operationally applicable results from student projects in the context of mission engineering. Specifically, it demonstrates that students can execute a systems engineering project that conducts system-level design with direct consideration of mission impacts at the system of systems level. Discussion of the benefits and limitations of this approach are discussed and suggestions for integrating mission engineering into capstone courses are provided.

Combining Lean and Six Sigma in the context of Systems Engineering design

2015 ◽  
Vol 6 (4) ◽  
pp. 290-312 ◽  
Author(s):  
TR Sreeram ◽  
Asokan Thondiyath
Keyword(s):  
Product Development ◽  
Engineering Design ◽  
Systems Engineering ◽  
Social Action ◽  
Six Sigma ◽  
Systems Design ◽  
System Level ◽  
Future Research ◽  
Content Type ◽  
Future Work

Purpose – The purpose of this paper is to present a combined framework for system design using Six Sigma and Lean concepts. Systems Engineering has evolved independently and there are numerous tools and techniques available to address issues that may arise in the design of systems. In the context of systems design, the application of Six Sigma and Lean concepts results in a flexible and adaptable framework. A combined framework is presented here that allows better visualization of the system-level components and their interactions at parametric level, and it also illuminates gaps that make way for continuous improvement. The Deming’s Plan-Do-Check-Act is the basis of this framework. Three case studies are presented to evaluate the application of this framework in the context of Systems Engineering design. The paper concludes with a summary of advantages of using a combined framework, its limitations and scope for future work. Design/methodology/approach – Six Sigma, Lean and Systems Engineering approaches combined into a framework for collaborative product development. Findings – The present framework is not rigid and does not attempt to force fit any tools or concepts. The framework is generic and allows flexibility through a plug and play type of implementation. This is important, as engineering change needs vary constantly to meet consumer demands. Therefore, it is important to engrain flexibility in the development of a foundational framework for design-encapsulating improvements and innovation. From a sustainability perspective, it is important to develop techniques that drive rationality in the decisions, especially during tradeoffs and conflicts. Research limitations/implications – Scalability of the approach for large systems where complex interactions exist. Besides, the application of negotiation techniques for more than three persons poses a challenge from a mathematical context. Future research should address these in the context of systems design using Six Sigma and Lean techniques. Practical implications – This paper provides a flexible framework for combining the three techniques based on Six Sigma, Lean and Systems Engineering. Social implications – This paper will influence the construction of agent-based systems, particularly the ones using the Habermas’s theory of social action as the basis for product development. Originality/value – This paper has not been published in any other journal or conference.

scholarly journals Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jaeyoung Park ◽  
Xiang Zhong ◽  
Yue Dong ◽  
Amelia Barwise ◽  
Brian W. Pickering
Keyword(s):  
Decision Making ◽  
Systems Engineering ◽  
Regression Models ◽  
Invasive Mechanical Ventilation ◽  
Severity Of Illness ◽  
Care Team ◽  
Cognitive Capacity ◽  
System Level ◽  
Operational Conditions ◽  
Cognitive Overload

Abstract Background ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team’s cognitive capacity. Methods The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team’s decision making. Results Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19. Conclusions Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team’s cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload.

scholarly journals On the use of coordination strategies in complex engineered system design projects

2020 ◽  
Vol 6 ◽  
Author(s):  
Arianne X. Collopy ◽  
Eytan Adar ◽  
Panos Y. Papalambros
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Large Scale ◽  
System Level ◽  
Engineering Project ◽  
Design Work ◽  
Coordination Strategies ◽  
Technical Leadership ◽  
Complex Engineered Systems ◽  
Design Projects

Abstract Coordination of distributed design work is an important activity in large-scale and complex engineered systems (LSCES) design projects. Coordination strategies have been studied formally in system design optimization and organizational science. This article reports on a study to identify what strategies are used in coordination practice. While the literature primarily offers prescriptive coordination strategies, this study focussed on the contribution of individuals’ behaviours to system-level coordination. Thus, a coordination strategy is seen as a particular set of individual actions and behaviours. We interviewed professionals with expertise in systems engineering, project management and technical leadership at two large aerospace design organizations. Through qualitative thematic analysis, we identified two strategies used to facilitate coordination. The first we call authority-based and is enabled by technical know-how and the use of organizational authority; the second we call empathetic leadership and includes interpersonal skills, leadership traits and empathy. These strategies emerged as complementary and, together, enabled individuals to coordinate complex design tasks. We found that skills identified in competency models enable these coordination strategies, which in turn support management of interdependent work in the organization. Studying the role of individuals contributes an expanded view on how coordination facilitates LSCES design practice.

SYSTEM LEVEL SINGLE EVENT UPSET MITIGATION STRATEGIES

2004 ◽  
Vol 14 (02) ◽  
pp. 341-352 ◽  
Author(s):  
W. F. HEIDERGOTT
Keyword(s):  
Systems Engineering ◽  
Fault Tolerant ◽  
Systems Design ◽  
Essential Elements ◽  
Mitigation Strategies ◽  
System Level ◽  
Single Event ◽  
Single Event Upsets ◽  
Fault Masking ◽  
And Performance

Use of a systems engineering process and the application of techniques and methods of fault tolerant systems are applicable to the development of a mitigation strategy for Single Event Upsets (SEU). Specific methods of fault avoidance, fault masking, detection, containment, and recovery techniques are important elements in the mitigation of single event upsets. Fault avoidance through the use of SEU hardened technology, fault masking using coding and redundancy provisions, and solutions applied at the subsystem and system level are available to the system developer. Validation and verification of SEU mitigation and performance of fault tolerance provisions are essential elements of systems design for operation in energetic particle environments.

Abstraction: An alternative neurocognitive account of recognition, prediction, and decision making

2020 ◽  
Vol 43 ◽  
Author(s):  
Valerie F. Reyna ◽  
David A. Broniatowski
Keyword(s):  
Decision Making ◽  
Software Engineering ◽  
Systems Engineering ◽  
Evidence Based ◽  
Extensive Literature ◽  
Trace Theory ◽  
Fuzzy Trace Theory ◽  
Abstract Representations ◽  
Technical Systems

Abstract Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

scholarly journals Aircraft Requirements for Sustainable Regional Aviation

Aerospace ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 61
Author(s):  
Dominik Eisenhut ◽  
Nicolas Moebs ◽  
Evert Windels ◽  
Dominique Bergmann ◽  
Ingmar Geiß ◽  
...  
Keyword(s):  
Systems Engineering ◽  
System Level ◽  
Policy Initiative ◽  
The European Union ◽  
Calculation Methods ◽  
Figures Of Merit ◽  
Performance Requirements ◽  
High Level ◽  
Different Levels ◽  
The Eu

Recently, the new Green Deal policy initiative was presented by the European Union. The EU aims to achieve a sustainable future and be the first climate-neutral continent by 2050. It targets all of the continent’s industries, meaning aviation must contribute to these changes as well. By employing a systems engineering approach, this high-level task can be split into different levels to get from the vision to the relevant system or product itself. Part of this iterative process involves the aircraft requirements, which make the goals more achievable on the system level and allow validation of whether the designed systems fulfill these requirements. Within this work, the top-level aircraft requirements (TLARs) for a hybrid-electric regional aircraft for up to 50 passengers are presented. Apart from performance requirements, other requirements, like environmental ones, are also included. To check whether these requirements are fulfilled, different reference missions were defined which challenge various extremes within the requirements. Furthermore, figures of merit are established, providing a way of validating and comparing different aircraft designs. The modular structure of these aircraft designs ensures the possibility of evaluating different architectures and adapting these figures if necessary. Moreover, different criteria can be accounted for, or their calculation methods or weighting can be changed.

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

scholarly journals An Estimation of Ship Collision Risk Based on Relevance Vector Machine

2021 ◽  
Vol 9 (5) ◽  
pp. 538
Author(s):  
Jinwan Park ◽  
Jung-Sik Jeong
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Risk Estimation ◽  
Good Method ◽  
Relevance Vector Machine ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Collision Risk ◽  
Estimation Model ◽  
Ship Collision

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

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