Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131068
Gisela A. Garza Morales, K. Nizamis, G. M. Bonnema
Background: An alternative to the difficulty of defining complexity is to explore its origins. This promising way of dealing with complexity, however, is currently hindered by a major shortcoming. We currently have various perspectives, terms, contexts, complexity study objectives, etc. This impedes consensus and overview of the complexity origins within the systems engineering communityObjective: We explored this variety through a scoping review covering the variety in the complexity terms (RQ1), complexity classifications (RQ2), engineering contexts (RQ3), and complexity study objectives (RQ4).Design: Four online databases were used to identify papers published 2012-2022, from which we selected 72 publications. Included publications had the word "complexity" in their title and abstract and discussed its origins or classifications.Results: We mapped 42 terms referring to complexity origins. We found over 300 classes and subclasses of complexity, which we organized in 31 clusters. We identified 29 engineering contexts interested in complexity origins. Finally, we identified five complexity study objectives, and their mapping showed that less than half the screened papers (31) were concered with identification of complexity origins.Conclusions: While it might not be necessary (or even possible) to have one single term or one single classification, it is currently very difficult to work with the extremely large number of different terms, and classes. Future efforts should also focus on unification, clarification, and standardization of the terminology and the classifications of complexity origins, which can get us closer to reaping the benefits of the already existing contributions.
{"title":"Why is there complexity in engineering? A scoping review on complexity origins","authors":"Gisela A. Garza Morales, K. Nizamis, G. M. Bonnema","doi":"10.1109/SysCon53073.2023.10131068","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131068","url":null,"abstract":"Background: An alternative to the difficulty of defining complexity is to explore its origins. This promising way of dealing with complexity, however, is currently hindered by a major shortcoming. We currently have various perspectives, terms, contexts, complexity study objectives, etc. This impedes consensus and overview of the complexity origins within the systems engineering communityObjective: We explored this variety through a scoping review covering the variety in the complexity terms (RQ1), complexity classifications (RQ2), engineering contexts (RQ3), and complexity study objectives (RQ4).Design: Four online databases were used to identify papers published 2012-2022, from which we selected 72 publications. Included publications had the word \"complexity\" in their title and abstract and discussed its origins or classifications.Results: We mapped 42 terms referring to complexity origins. We found over 300 classes and subclasses of complexity, which we organized in 31 clusters. We identified 29 engineering contexts interested in complexity origins. Finally, we identified five complexity study objectives, and their mapping showed that less than half the screened papers (31) were concered with identification of complexity origins.Conclusions: While it might not be necessary (or even possible) to have one single term or one single classification, it is currently very difficult to work with the extremely large number of different terms, and classes. Future efforts should also focus on unification, clarification, and standardization of the terminology and the classifications of complexity origins, which can get us closer to reaping the benefits of the already existing contributions.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130759372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131227
Nandith Narayan, Parth Ganeriwala, Randolph M. Jones, M. Matessa, S. Bhattacharyya, Jennifer Davis, Hemant Purohit, Simone Fulvio Rollini
Autonomous agents are expected to intelligently handle emerging situations with appropriate interaction with humans, while executing the operations. This is possible today with the integration of advanced technologies, such as machine learning, but these complex algorithms pose a challenge to verification and thus the eventual certification of the autonomous agent. In the discussed approach, we illustrate how safety properties for a learning-enabled increasingly autonomous agent can be formally verified early in the design phase. We demonstrate this methodology by designing a learning-enabled increasingly autonomous agent in a cognitive architecture, Soar. The agent includes symbolic decision logic with numeric decision preferences that are tuned by reinforcement learning to produce post-learning decision knowledge. The agent is then automatically translated into nuXmv, and properties are verified over the agent.
{"title":"Assuring Learning-Enabled Increasingly Autonomous Systems*","authors":"Nandith Narayan, Parth Ganeriwala, Randolph M. Jones, M. Matessa, S. Bhattacharyya, Jennifer Davis, Hemant Purohit, Simone Fulvio Rollini","doi":"10.1109/SysCon53073.2023.10131227","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131227","url":null,"abstract":"Autonomous agents are expected to intelligently handle emerging situations with appropriate interaction with humans, while executing the operations. This is possible today with the integration of advanced technologies, such as machine learning, but these complex algorithms pose a challenge to verification and thus the eventual certification of the autonomous agent. In the discussed approach, we illustrate how safety properties for a learning-enabled increasingly autonomous agent can be formally verified early in the design phase. We demonstrate this methodology by designing a learning-enabled increasingly autonomous agent in a cognitive architecture, Soar. The agent includes symbolic decision logic with numeric decision preferences that are tuned by reinforcement learning to produce post-learning decision knowledge. The agent is then automatically translated into nuXmv, and properties are verified over the agent.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131051
Natalie Holliday, L. D. Otero
This research describes the construction of a discrete-event simulation model of the operations of servicing offshore wind turbines within offshore wind farms with the objective of optimizing cash flow. Specifically, the simulation model looks at alternate support vessels that carry crew to and from farms while completing operation and maintenance activities. Historical data were used to validate the model as well as data from prior simulation models. The simulation is able to identify feasibility concerns by analyzing four alternatives: the use of crew transport vessels (CTVs), adding an additional CTV, use of standard surface effect ships (SESs), and the use of optimized SESs. Of these alternatives, the number of support vessels, type of support vessel, and replacement of heavy-failure components can be assessed. The results showed that the average cash flows of the different alternatives were significantly different. Conclusions were made based on the results from the simulation study, and further research opportunities were identified.
{"title":"A Simulation Model for the Optimization of Crew Transport Vessels to Service Offshore Wind Farms","authors":"Natalie Holliday, L. D. Otero","doi":"10.1109/SysCon53073.2023.10131051","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131051","url":null,"abstract":"This research describes the construction of a discrete-event simulation model of the operations of servicing offshore wind turbines within offshore wind farms with the objective of optimizing cash flow. Specifically, the simulation model looks at alternate support vessels that carry crew to and from farms while completing operation and maintenance activities. Historical data were used to validate the model as well as data from prior simulation models. The simulation is able to identify feasibility concerns by analyzing four alternatives: the use of crew transport vessels (CTVs), adding an additional CTV, use of standard surface effect ships (SESs), and the use of optimized SESs. Of these alternatives, the number of support vessels, type of support vessel, and replacement of heavy-failure components can be assessed. The results showed that the average cash flows of the different alternatives were significantly different. Conclusions were made based on the results from the simulation study, and further research opportunities were identified.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131108535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131112
Shayan Sheikhrezaei, H. Yeh, S. Kwon
In this paper, we propose the piecewise technique of Rapidly-exploring Random Tree-Star (P-RRT*) algorithm used in low or medium specification agent(s) (rovers) in the two- dimensional (2-D) workspace. The traditional RRT, RRT*, and other path planning algorithms however efficient they have become; all treat a given environment as a whole and attempt to find a feasible path. This may result in higher memory utilization and a significant increase in processing time.We utilize the RRT* algorithm as the base and integrate it with the piecewise approach. Through P-RRT* technique, given an environment with no obstacles, we attempt to minimize the three vital elements used in the RRT* path planning algorithm (memory, power consumption, and time).A 2D simulation is utilized for demonstration purposes. Given a large workspace, we simulate over subregional workspaces where the number of nodes and step size are adjusted properly to minimize the cost. The simulation results show that dividing the entire simulation workspace into subregions and treating each subregion as a new workspace not only reduces memory utilization and processing time but also the power consumption as a result.The simulation results are shown versus the traditional RRT* algorithm; similar constraints are set for both the piecewise RRT* technique and the traditional RRT* algorithm; meaning that the number of nodes and step size is the same for both methods.
{"title":"Piecewise Rapidly-Exploring Random Tree Star","authors":"Shayan Sheikhrezaei, H. Yeh, S. Kwon","doi":"10.1109/SysCon53073.2023.10131112","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131112","url":null,"abstract":"In this paper, we propose the piecewise technique of Rapidly-exploring Random Tree-Star (P-RRT*) algorithm used in low or medium specification agent(s) (rovers) in the two- dimensional (2-D) workspace. The traditional RRT, RRT*, and other path planning algorithms however efficient they have become; all treat a given environment as a whole and attempt to find a feasible path. This may result in higher memory utilization and a significant increase in processing time.We utilize the RRT* algorithm as the base and integrate it with the piecewise approach. Through P-RRT* technique, given an environment with no obstacles, we attempt to minimize the three vital elements used in the RRT* path planning algorithm (memory, power consumption, and time).A 2D simulation is utilized for demonstration purposes. Given a large workspace, we simulate over subregional workspaces where the number of nodes and step size are adjusted properly to minimize the cost. The simulation results show that dividing the entire simulation workspace into subregions and treating each subregion as a new workspace not only reduces memory utilization and processing time but also the power consumption as a result.The simulation results are shown versus the traditional RRT* algorithm; similar constraints are set for both the piecewise RRT* technique and the traditional RRT* algorithm; meaning that the number of nodes and step size is the same for both methods.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"66 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125869268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131064
J. Al-Jaroodi, N. Mohamed, Nader Kesserwan, I. Jawhar
Recent advances in digitalizing healthcare services and related systems show great promise of effective use of technology to enhance the healthcare sector. Healthcare and healthcare-related industries are moving forward with the adoption of various smart services and Healthcare 4.0 systems to reduce costs, improve care, and enhance patient satisfaction. However, the research and development communities are offering a lot of innovative frameworks, applications and techniques to fully transform the healthcare industry into Healthcare 4.0. Adopting such technologies and solutions face various challenges. Some are technical and many of these are solvable, some financial, which can be addressed, somehow; yet, there are other obstacles hindering the efforts, namely, humans! Smart systems in general are invasive and require people to accept how these systems will be involved in their every day life. This leads to another important aspect, trust, as such invasive behavior require humans to trust these systems and those who operate them. They may also replace human interactions, which many see as essential for successful patient/practitioner relationships. Moreover, some view these as replacement for human workers, which may lead to layoffs and financial hardships. In this paper, we investigate the human factor affecting the acceptance and adoption of Healthcare 4.0 and smart healthcare systems among the different stakeholders. We will review current work on the topic and identify the major factors with respect to how they affect stakeholders and whether these are solvable with advances in technology. We see that technology can help solve many of the issues affecting human’s acceptance, yet there are also ones that are not, no matter how advanced the technology can get.
{"title":"Human Factors Affecting the Adoption of Healthcare 4.0","authors":"J. Al-Jaroodi, N. Mohamed, Nader Kesserwan, I. Jawhar","doi":"10.1109/SysCon53073.2023.10131064","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131064","url":null,"abstract":"Recent advances in digitalizing healthcare services and related systems show great promise of effective use of technology to enhance the healthcare sector. Healthcare and healthcare-related industries are moving forward with the adoption of various smart services and Healthcare 4.0 systems to reduce costs, improve care, and enhance patient satisfaction. However, the research and development communities are offering a lot of innovative frameworks, applications and techniques to fully transform the healthcare industry into Healthcare 4.0. Adopting such technologies and solutions face various challenges. Some are technical and many of these are solvable, some financial, which can be addressed, somehow; yet, there are other obstacles hindering the efforts, namely, humans! Smart systems in general are invasive and require people to accept how these systems will be involved in their every day life. This leads to another important aspect, trust, as such invasive behavior require humans to trust these systems and those who operate them. They may also replace human interactions, which many see as essential for successful patient/practitioner relationships. Moreover, some view these as replacement for human workers, which may lead to layoffs and financial hardships. In this paper, we investigate the human factor affecting the acceptance and adoption of Healthcare 4.0 and smart healthcare systems among the different stakeholders. We will review current work on the topic and identify the major factors with respect to how they affect stakeholders and whether these are solvable with advances in technology. We see that technology can help solve many of the issues affecting human’s acceptance, yet there are also ones that are not, no matter how advanced the technology can get.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126932984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131174
Jacqueline Heaton, S. Givigi
Motion planning and control is a necessary aspect of incorporating robots into the real world. There are a variety of different types of control tasks that involve collision avoidance and fine control, that are difficult to program without the use of artificial intelligence (AI), especially in an non-stationary environment. In this paper, one method for applying deep reinforcement learning (RL) to the motion planning of a manipulator robot is described. Using a soft actor-critic (SAC) network, a model is trained to direct the manipulator to various locations so as to avoid colliding either its hand or the object it carries with a game tower. This demonstrates a simple and effective method for training an agent to achieve its goal that generalizes to similar but different environments.
{"title":"A Deep Reinforcement Learning Solution for the Low Level Motion Control of a Robot Manipulator System","authors":"Jacqueline Heaton, S. Givigi","doi":"10.1109/SysCon53073.2023.10131174","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131174","url":null,"abstract":"Motion planning and control is a necessary aspect of incorporating robots into the real world. There are a variety of different types of control tasks that involve collision avoidance and fine control, that are difficult to program without the use of artificial intelligence (AI), especially in an non-stationary environment. In this paper, one method for applying deep reinforcement learning (RL) to the motion planning of a manipulator robot is described. Using a soft actor-critic (SAC) network, a model is trained to direct the manipulator to various locations so as to avoid colliding either its hand or the object it carries with a game tower. This demonstrates a simple and effective method for training an agent to achieve its goal that generalizes to similar but different environments.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114136438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131078
R. Chen, Guoxin Wang, Shouxuan Wu, Jinzhi Lu, Yan Yan
When using Model-Based Systems Engineering (MBSE) to develop complex systems, models using different syntax and semantics are typically implemented in a heterogeneous environment which leads to difficulties to realize data integrations across the entire lifecycle. Specifically, seamless exchanges between models of different modeling tools are needed to support system lifecycle activities such as requirement analysis, function analysis, verification and validation. This article illustrates a service-oriented approach to support model integration for model-based systems engineering, especially between architecture design and system verification. First, a set of semantic mapping rules between architecture models and simulation models based on Open Service of Lifecycle Collaboration (OSLC) are proposed to support the formalization of technical resources (models, data, APIs). Then OSLC adapters are developed to transform models, data and APIs into web-based services. The services are deployed by a service discovering plug-in within a specific modeling tool for model information exchange. The approach is illustrated by a case study on KARMA architecture model and Modelica simulation model for a six-degree-of-freedom robot (RobotR3) system. We evaluate the availability and efficiency of this method from both qualitative and quantitative perspectives. The results show that our approach is effective in model and data integration.
当使用基于模型的系统工程(MBSE)开发复杂系统时，使用不同语法和语义的模型通常在异构环境中实现，这会导致难以实现跨整个生命周期的数据集成。具体来说，需要在不同建模工具的模型之间进行无缝交换，以支持系统生命周期活动，例如需求分析、功能分析、验证和确认。本文说明了一种面向服务的方法来支持基于模型的系统工程的模型集成，特别是在体系结构设计和系统验证之间。首先，提出了一套基于生命周期协作开放服务(Open Service of Lifecycle Collaboration, OSLC)的体系结构模型和仿真模型之间的语义映射规则，以支持技术资源(模型、数据、api)的形式化。然后开发OSLC适配器来将模型、数据和api转换为基于web的服务。服务由特定建模工具中的服务发现插件部署，用于模型信息交换。以六自由度机器人(RobotR3)系统的KARMA体系结构模型和Modelica仿真模型为例说明了该方法的可行性。我们从定性和定量两方面评价了该方法的有效性和效率。结果表明，该方法在模型和数据集成方面是有效的。
{"title":"A Service-oriented Approach Supporting Model Integration in Model-based Systems Engineering","authors":"R. Chen, Guoxin Wang, Shouxuan Wu, Jinzhi Lu, Yan Yan","doi":"10.1109/SysCon53073.2023.10131078","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131078","url":null,"abstract":"When using Model-Based Systems Engineering (MBSE) to develop complex systems, models using different syntax and semantics are typically implemented in a heterogeneous environment which leads to difficulties to realize data integrations across the entire lifecycle. Specifically, seamless exchanges between models of different modeling tools are needed to support system lifecycle activities such as requirement analysis, function analysis, verification and validation. This article illustrates a service-oriented approach to support model integration for model-based systems engineering, especially between architecture design and system verification. First, a set of semantic mapping rules between architecture models and simulation models based on Open Service of Lifecycle Collaboration (OSLC) are proposed to support the formalization of technical resources (models, data, APIs). Then OSLC adapters are developed to transform models, data and APIs into web-based services. The services are deployed by a service discovering plug-in within a specific modeling tool for model information exchange. The approach is illustrated by a case study on KARMA architecture model and Modelica simulation model for a six-degree-of-freedom robot (RobotR3) system. We evaluate the availability and efficiency of this method from both qualitative and quantitative perspectives. The results show that our approach is effective in model and data integration.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130136509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131094
Mohammadreza Torkjazi, Ali K. Raz
Multi-Attribute Decision Making (MADM) methods are an integral component of trade-off studies which are frequently employed in Systems Engineering when multiple interdependent decision criteria are involved. In MADM methods, each decision criterion is assigned a weight based on how important it is to the Decision-Makers (DMs), and a decision matrix is populated with values representing assessments of each alternative with respect to the decision criteria. MADM methods, therefore, are susceptible to subjectivity due to inherent bias in DM’s preferences where slight fluctuation in stated DM’s preference can drastically impact the outcome. In this paper, we propose a data-driven methodology with Machine Learning to improve the effectiveness of MADM methods by reducing DMs’ subjective biases resulting from criteria weights. In addition, the proposed methodology leverages Exploratory Data Analysis to better determine the type of criteria as cost or benefit, depending upon whether it positively or negatively affects the MADM outcome. A sample trade study example of selecting a metropolitan area based on housing affordability is provided to illustrate how the proposed method is applied to generate data-based true criteria weights and types.
{"title":"Data-Driven Approach with Machine Learning to Reduce Subjectivity in Multi-Attribute Decision Making Methods","authors":"Mohammadreza Torkjazi, Ali K. Raz","doi":"10.1109/SysCon53073.2023.10131094","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131094","url":null,"abstract":"Multi-Attribute Decision Making (MADM) methods are an integral component of trade-off studies which are frequently employed in Systems Engineering when multiple interdependent decision criteria are involved. In MADM methods, each decision criterion is assigned a weight based on how important it is to the Decision-Makers (DMs), and a decision matrix is populated with values representing assessments of each alternative with respect to the decision criteria. MADM methods, therefore, are susceptible to subjectivity due to inherent bias in DM’s preferences where slight fluctuation in stated DM’s preference can drastically impact the outcome. In this paper, we propose a data-driven methodology with Machine Learning to improve the effectiveness of MADM methods by reducing DMs’ subjective biases resulting from criteria weights. In addition, the proposed methodology leverages Exploratory Data Analysis to better determine the type of criteria as cost or benefit, depending upon whether it positively or negatively affects the MADM outcome. A sample trade study example of selecting a metropolitan area based on housing affordability is provided to illustrate how the proposed method is applied to generate data-based true criteria weights and types.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133980622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131099
Dean Revell, David C. Gross, Gong Zhou, Adrian Hernandez
The present work reports on experiences gleaned in the application of systems engineering on a research project whose objective is technology development. The benefits of systems engineering for system development throughout the lifecycle are well known. Inclusion of systems engineering has a measurable and significant beneficial effect on system development project performance measured by costs, schedules, and features implemented. Technology development however differs from product or service-oriented development and the potential systems engineering for such are unknown. The authors sought to utilize proven MBSE methodologies to aid in a research project for an emerging technology. The project addressed by the present work was a Phase I Small Business Innovative Research Project to determine the feasibility and utility of wireless acoustic power transfer systems in an operational environment. The purpose of this paper therefore is to report on the selection and application of a MBSE methodology for this small-scale technology development program. A summary of the work performed by the MBSE team is given, along with example modeling products and their intended uses. It discusses missteps as well as successes and from its findings proposes additional research to develop a method to evaluate these methodologies based on the nature of the project.
{"title":"Applying a MBSE Methodology in Small Scale Technology Development 1","authors":"Dean Revell, David C. Gross, Gong Zhou, Adrian Hernandez","doi":"10.1109/SysCon53073.2023.10131099","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131099","url":null,"abstract":"The present work reports on experiences gleaned in the application of systems engineering on a research project whose objective is technology development. The benefits of systems engineering for system development throughout the lifecycle are well known. Inclusion of systems engineering has a measurable and significant beneficial effect on system development project performance measured by costs, schedules, and features implemented. Technology development however differs from product or service-oriented development and the potential systems engineering for such are unknown. The authors sought to utilize proven MBSE methodologies to aid in a research project for an emerging technology. The project addressed by the present work was a Phase I Small Business Innovative Research Project to determine the feasibility and utility of wireless acoustic power transfer systems in an operational environment. The purpose of this paper therefore is to report on the selection and application of a MBSE methodology for this small-scale technology development program. A summary of the work performed by the MBSE team is given, along with example modeling products and their intended uses. It discusses missteps as well as successes and from its findings proposes additional research to develop a method to evaluate these methodologies based on the nature of the project.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114569572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1109/SysCon53073.2023.10131138
Muhammad Hayyan Bin Shahid, Akramul Azim
Faults in a transmission line (TL) are the most common faults faced by almost every power station. Suppose these faults are not detected in time. In that case, they can result in multiple losses, such as a loss in an estimated power generation w.r.t predicted time and financial losses. In order to investigate the fault, the systematic approach of an engineer would be first to detect whether there is a fault or not. If a fault is detected in the transmission line, it should be classified as soon as possible. The following classifications would help the maintenance team identify the fault type: line fault, line-to-line fault, double line fault, triple line fault, single-line-to-ground fault, double line-to-ground fault, three-phase fault, and no fault. This paper proposes that the ensemble method, using the Machine Learning (ML) technique, will help the engineers detect and classify the faults in the transmission line. The investigation also trained and tested multiple ML classifiers to inform better recommendations. The shared research will help the user find the best possible ML results for predicting faults in the transmission line. Hence early and accurate fault detection will enhance safety and reliability and reduce interruption and downtime.
{"title":"Ensemble Method For Fault Detection & Classification in Transmission Lines Using ML","authors":"Muhammad Hayyan Bin Shahid, Akramul Azim","doi":"10.1109/SysCon53073.2023.10131138","DOIUrl":"https://doi.org/10.1109/SysCon53073.2023.10131138","url":null,"abstract":"Faults in a transmission line (TL) are the most common faults faced by almost every power station. Suppose these faults are not detected in time. In that case, they can result in multiple losses, such as a loss in an estimated power generation w.r.t predicted time and financial losses. In order to investigate the fault, the systematic approach of an engineer would be first to detect whether there is a fault or not. If a fault is detected in the transmission line, it should be classified as soon as possible. The following classifications would help the maintenance team identify the fault type: line fault, line-to-line fault, double line fault, triple line fault, single-line-to-ground fault, double line-to-ground fault, three-phase fault, and no fault. This paper proposes that the ensemble method, using the Machine Learning (ML) technique, will help the engineers detect and classify the faults in the transmission line. The investigation also trained and tested multiple ML classifiers to inform better recommendations. The shared research will help the user find the best possible ML results for predicting faults in the transmission line. Hence early and accurate fault detection will enhance safety and reliability and reduce interruption and downtime.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114617873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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