Systems Engineering Research Center Research Articles

The Updated SERC AI and Autonomy Roadmap 2023

AbstractThe first Systems Engineering Research Center (SERC) Artificial Intelligence (AI) and Autonomy Research Roadmap was developed in 2020 and published in the first quarter 2021 special INSIGHT issue on Systems Engineering and AI. This roadmap development was heavily informed by the INCOSE Future of Systems Engineering (FuSE) initiatives. Following on in 2020, INCOSE and the SERC collaborated with the Association for Advancement of AI (AAAI) to execute two workshops entitled “AI meets Systems Engineering.” These resulted in version two of the roadmap which was published as an introductory chapter to the book “Systems Engineering and Artificial Intelligence.” In 2020 through 2023 the SERC hosted four SE4AI/AI4SE workshops with the U.S. Army that have further informed research and application at the intersection of AI and SE. This paper presents the updated version of the roadmap resulting from engagement across those four workshops. It is provided as a means to inform the SE community of the critical research needs and related applications emerging at the intersection of AI and SE.

Exploring over a decade of systems engineering research center: A community detection and text analytics approach

AbstractThe Systems Engineering Research Center (SERC) is a University Affiliated Research Center (UARC) of the US Department of Defense (DoD) formed in 2008 with more than 20 collaborator universities in the United States. Over the last decade, SERC has conducted research with Principal Investigators from universities within the SERC network, as reflected in technical reports (TR). These reports describe detailed information and analysis of the conducted research for every project under SERC support, such as written records of experiments or results of a scientific project. We analyzed the TRs from 2009 to early 2023 to identify research streams, topics, and evolution in systems engineering (SE) research using text mining and network analysis techniques, such as Louvain Community Detection and word similarity. As a result, we identified four major research streams over a decade of research projects, along with insights about topics and the evolution of SE within this time frame. Finally, we distinguished most profile authors and their most significant collaborations and networks.

Lessons learned from establishing the Systems Engineering Research Center, a networked University Affiliated Research Center

AbstractA University Affiliated Research Center (UARC) is a highly valued engineering, research, and development resource to the federal government, largely within the US Department of Defense (DoD). Only 15 exist, all under special authority of the US Code. The Systems Engineering Research Center (SERC), established in 2008, was the first UARC established as a networked research center, gradually growing to include more than two dozen university members, each offering systems engineering research capabilities. This networked model for a UARC has found support within the DoD—exemplified by the newest UARC established recently by the US Air Force. A large diverse pool of faculty and student researchers arguably delivers the most consequential academic research in systems engineering anywhere in the world. In its first few years, the SERC had to overcome challenges that single‐university UARCs did not face in exchange for the advantages that such a sizable network of universities offers. This review paper explores those challenges, offering lessons for new network‐based UARCs and other network‐based university research centers. Systems engineering communities, globally, are exploring such constructs, and this reflective paper may offer useful insights.

The Supra‐System Model

ABSTRACTThis article presents an initial set of concepts resulting from research by the Office of the Undersecretary of Defense for Research and Engineering (OUSD/RE) and the Systems Engineering Research Center (SERC) under an initiative called “systems engineering modernization” (SEMOD). This article discusses the “supra‐system model,” which evolved as a different view of systems engineering lifecycle activities across the entire life of an engineered system. This view promotes systems engineering as a continuous process that is 1) iterative across the full life of a system and 2) managed through a digital transformation centered on data and models. This article also discusses the value of “shared and authoritatively managed data and models” in the lifecycle of future systems. These together present a modernized view of systems engineering where “seamless and efficient transfer of data and models” will support practices that are “more agile and responsive to changing stakeholder needs.”

AI4SE and SE4AI: Setting the Roadmap toward Human‐Machine Co‐Learning

ABSTRACTArtificial intelligence (AI) and machine learning (ML) technology are becoming increasingly critical in systems: both to provide new capabilities and in the practice of systems engineering itself, especially as digital transformation improves the automation of many routine engineering tasks. The application of AI, ML, and autonomy to complex and critical systems encourage the development of new systems engineering methods, processes, and tools. This article highlights a series of workshops conducted jointly by the US Army Combat Capabilities Development Command Armaments Center (CCDC AC) Systems Engineering Directorate and the Systems Engineering Research Center (SERC). These workshops focus on the relationships between AI and systems engineering and elicit input from hundreds of stakeholders across government, industry, and academia. They also provide critical direction to the SERC's research roadmap on AI/autonomy as it looks towards the long‐term outcome of “human‐machine co‐learning.” Though the workshops are US‐centric, the lessons and insights gained are applicable globally.

Guiding Systems Engineering Research for Enhanced Impact in the Development of Increasingly Complex Cyber‐Physical Systems

ABSTRACTIn 2019, the research council of the Systems Engineering Research Center (SERC), a US Defense Department sponsored university affiliated research center (UARC), developed a set of roadmaps (SERC 2019) structuring and guiding four areas of systems engineering research: digital engineering, velocity, security, and artificial intelligence (AI) and autonomy. This paper presents the development of these roadmaps and the key underlying transformation aspects.

The Digital Engineering Competency Framework (DECF): Critical Skillsets to Support Digital Transformation

ABSTRACTIn the US, the Department of Defense (DoD) is engaging in major digital transformation efforts to modernize the defense workforce, particularly the engineering acquisition workforce to ensure that the workforce remains technically competent. This effort was kicked off with the publication of its Digital Engineering Strategy in 2018. Digital engineering is necessary to update and support systems engineering practices to maintain a competitive advantage in data analytics, data science, computation technology, modeling, and simulation. The DoD envisions successful digital engineering transformation through the modernization of the way the Department designs, develops, delivers, operates, maintains, and sustains systems. One of the key aspects of this transformation is developing a digital engineering competency model to develop and sustain the workforce. In 2019, the DoD tasked the Systems Engineering Research Center (SERC) with the creation of a “Digital Engineering Competency Framework (DECF)” as part of its critical digital transformation and workforce development modernization efforts. Though created in the context of the US DoD, the DECF is not intended to be a defense‐ or US‐centric and instead provides a foundation for any individual or organization looking to embark on digital transformation. This paper provides an overview of the DECF and its potential relationships with other competency frameworks.

Is Systems Engineering Effectiveness the Heart of Today's Employability Skills?

ABSTRACTThere are three essential and interrelated sets of personal characteristics and skills that are acquired by the most valued and effective individuals as they develop their careers. These are (1) self‐leadership and learning, (2) complex problem solving, and (3) team leadership and collaboration. These are interdependent and develop iteratively through practice in authentic work‐related contexts. They are at the heart of the Helix study which was conducted by the Systems Engineering Research Center to discover the sets of proficiencies that make a systems engineer effective in their roles (Hutchison and Verma 2018). They are also at the heart of the “employability skills” so desired by today's businesses. Each of these three skillsets is supported by tools of different types and can be embedded into education and organizational learning programs. In this article we relate the development of effective systems engineers as a model for all professionals and frame a set of learning objectives that individuals and organizations can use to accelerate desired employability skills.

Impacts of work‐at‐home policies on systems engineers and the general population

AbstractThe COVID‐19 pandemic presented many challenges, one of them being the imposition of “work‐at‐home” policies in March 2020. The Systems Engineering Research Center (SERC) and the International Council on Systems Engineering (INCOSE) conducted two online surveys—one during the first months of the pandemic in 2020 and the second survey 1 year after, in March 2021—to understand the impact of these policies within the systems engineering community. The surveys' format consisted of multiple‐choice questions and open‐answer questions, which were analyzed using LDA for topic modeling. Data were also collected from social media during the same timeframes to compare the feelings and experiences of systems engineers with those of the general population.

Digital Engineering Measures: Research and Guidance

ABSTRACTDigital Engineering and Model‐Based Engineering continue to grow in adoption across the systems engineering community. However, programs and enterprises still struggle to quantify the value of this digital transformation. Research conducted by the Systems Engineering Research Center in collaboration with a government/industry Digital Engineering Measures Working Group is creating the first formal measurement framework for this transformation. This article describes the research, formation of a causal measurement model, and initial specification of candidate measures.

Workforce and Evaluation and Training for Digital Engineering in the US Department of Defense

AbstractDigital engineering (DE) is “an integrated digital approach that uses authoritative sources of systems’ data and models as a continuum across disciplines to support lifecycle activities from concept through disposal. A DE ecosystem is an interconnected infrastructure, environment, and methodology that enables the exchange of digital artifacts from an authoritative source of truth.” (DAU, 2017) Digital transformation is fundamentally changing the way acquisition and engineering are performed across a wide range of government agencies, industries, and academia and is characterized by the integration of digital technology into all areas of a business, changing fundamental operations and how results are delivered in terms of new value to customers. It includes cultural change centered on alignment across leadership, strategy, customers, operations, and workforce evolution. In 2020, the Systems Engineering Research Center (SERC) developed a Digital Engineering Competency Framework (DECF) to support the US Department of Defense (DoD). This paper provides an update to the DECF (version. 1.1) and a mapping of some existing DoD training resources against the DECF. It discusses gaps and provides a few high‐level recommendations on how to build DE competency in the DoD acquisition workforce. Though the DECF and the training included here are specific to the DoD, the competency framework is intended to be relevant to anyone working in the DE space.

A Zero Trust Hybrid Security and Safety Risk Analysis Method

Abstract Designing complex, socio-technical, cyber-physical systems has become increasingly challenging in recent years. Interdependencies between engineering domains can lead to emergent behavior that is difficult to predict and manage. The recent shift toward model-based design has demonstrated significant advantages for minimizing these challenges (McDermott et al. 2020. Benchmarking the benefits and current maturity of model-based systems engineering across the enterprise. results of the model-based systems engineering (MBSE) maturity survey, part 1: Executive summary. Technical Report SERC-2020-SR-001, Systems Engineering Research Center.). Further, the early identification of safety and security design weaknesses in safety-critical systems leads to reduced redesign costs in later design phases (Yang and EI-Haik, 2003. Design for Six Sigma. McGraw-Hill, New York City; Clausing and Frey, 2005. Improving system reliability by failure-mode avoidance including four concept design strategies. Systems Engineering, 8(3), pp. 245–261.). As a result, this article contributes the Multidisciplinary Early Design Risk Assessment Framework (MEDRAF) methodology for early combined safety and security assessment based on interdisciplinary dependency models of a system. The focus is on factors contributing to the estimation of the probabilities of successful attacks on system components. The Zero Trust paradigm is applied in which all humans, hardware, and processes interacting with the system are considered to pose a security risk. A calculation of security-related probability estimates is presented which is dependent on the current global security environment. Subsequently, security and safety probability estimates are combined to present an overall safety-security risk calculation using hybrid safety-security trees. The risk values help designers assess the loss of specific key components and safety functions. The methodology is demonstrated with a case study of a spent fuel pool cooling system in a nuclear reactor. The results of the case study show that the risk of losing one key system component doubles when combining security and safety compared to only assessing safety events. This paper is based on a paper presented at the CIE 2020 conference (Papakonstantinou et al., 2020. Towards a zero trust hybrid security and safety risk analysis method. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers.).

AI4SE and SE4AI: A Research Roadmap

ABSTRACTIn 2019, the Research Council of the Systems Engineering Research Center (SERC), a US Defense Department sponsored University Affiliated Research Center (UARC), developed a roadmap structuring and guiding artificial intelligence (AI) and autonomy research. This paper presents that roadmap and key underlying Digital Engineering transformation aspects both enabling traditional systems engineering practice automation (AI4SE), and encourage new systems engineering practices supporting a new wave of automated, adaptive, and learning systems (SE4AI).

From the Editor‐in‐Chief and Systems Engineering Research Center Executive Director

From the Editor‐in‐Chief and Systems Engineering Research Center Executive Director

Design and Construction of Microwave-Assisted Pyrolysis of Waste Coconut Shell for the Isolation of Pyroligneous Acid

As a country with a large amount of natural resources, Indonesia should be able to convert this material into more value added product. However, most of the natural resources were sold as a raw material. Process system engineering research center is one of the solution to overcome this problem by developing an integrated and systematic technology. Through this research center, output of the research can be scaled up for large scale production and also can be commercialized to increase the community welfare. One of natural resources which has not been optimally utilized is waste coconut shell (WCS). Indonesia is the largest coconut producer in the world with areal production of 3.88 ha and 3.2 million ton of coconut products. Several problems arefacedbycoconutagroindustry,i.e.thelackofcoconutbasedproductdiversification and also the large number of WCS. WCS is one of organic waste, however it is quite hard to be decomposed by the microorganism due to its hard texture. This problem may gave high potential in the environmental pollution. In this research, WCS is going to be used as a raw material for pyroligneous acid through pyrolysis process. Pyrolysis is a method that is usually used to convert a biomass waste sources into a valuable product through thermal decomposition process without the presence of oxygen. This process will produce solid (char), liquid (bio-oil, tar and pyroligneous acid) and gas. Pyroligneous acid is commonly obtained as a side product from the production of active carbon and to date it has not been utilized economically. In the other hand, pyroligneous acid can be used as an anti-oxidant, antimicrobial, antifungal, anti-biofilm and also as an anti inflammatory. This properties are available due to the presence of organic matter and phenolic compound in the pyroligneous acid. This characteristics showedthatpyroligneousacidishighlypotentialasrawmaterialindrugsandpharmacy industries. Pyrolysis process requires high temperature which has range between 500 – 600 ∘C. In this paper, it will be discussed a pyrolysis equipment design and productionofpyroligneousacidfromWCSbyusingmicrowave-assistedpyrolysis(MAP).

Triple‐mode bandpass filter

triple‐mode bandpass filter

Evolution of the Helix Project: From Investigating the Effectiveness of Individual Systems Engineers to Systems Engineering Organizations

AbstractThe U.S. Department of Defense (DoD) and the Defense Industrial Base (DIB) have faced challenges in systems engineering in recent years (GAO, 2008, 2011, 2012, 2013, 2018). The Helix project started in 2012 as a multi‐year longitudinal research study mainly to understand what makes systems engineers effective. The previous Helix work on individual systems engineers is a critical input to this project focus. In 2018, the Helix team shifted from an exclusively workforce focus (within the Systems Engineering Research Center (SERC) Human Capital Development Strategy area) to encompass how organizations can become more effective at systems engineering as a discipline and includes organizational characteristics that influence the effectiveness of the systems engineering workforce, as well as how they better enable their systems engineering workforce. This paper provides an update on the research including the updated methodology to investigate the organizational systems engineering effectiveness.

SYSTEMS SCIENCES AND SYSTEMS ENGINEERING RESEARCH NEEDS

ABSTRACTThe Systems Engineering Research Center (SERC) Pathfinder project in collaboration with INCOSE identified systems engineering research and workforce development needs. The results indicate five broad areas of systems engineering research needs in order of emphasis: mission engineering; agile development and engineering at the scale of the enterprise; model‐based systems engineering; modeling and assessing system trust, security, and resilience; and development of pragmatic methods to deal with and to leverage increasing complexity and analytics, and leveraging machine learning for this purpose. Within INCOSE, the survey of Fellows worldwide emphasizes model‐based systems engineering along with more general systems engineering modeling and measures. For both SERC and INCOSE, the results will guide priorities over the next two to five years.

Transforming systems engineering through digital engineering

This article describes Department of Defense (DoD) Systems Engineering Research Center (SERC) efforts leading to and supporting the DoD Digital Engineering (DE) initiative. Topics include the SERC’s initial collaboration with Naval Air Systems Command (NAVAIR) in 2013 as well as ongoing SERC DE research. The article also identifies future research needed to continue to develop the DE ecosystem for system of systems acquisition, which will require rapidly changing mission strategies to address ever-evolving threats.

First Report of Maize yellow mosaic virus Infecting Panicum miliaceum and Sorghum bicolor in South Korea

Maize yellow mosaic virus (MaYMV) is a tentative new Polerovirus, which was recently identified from maize (Zea mays) in China (Chen et al. 2016). MaYMV has also been reported to infect sugarcane (Saccharum spp.) and itch grass (Rottboellia cochinchinensis), and it has been reported in Asia, Africa and South America (Goncalves et al. 2017; Palanga et al. 2017; Yahaya et al. 2017). In this study, MaYMV was detected in Panicum miliaceum and Sorghum bicolor using Illumina HiSeq2500 system by Theragen Etex Bio Institute (Suwon, Korea) and SG-VIPdb by SeqGenesis (Daejeon, Korea), as described by Lim et al. (2015). Twenty samples of P. miliaceum and sixty three samples of S. bicolor were collected from July to September and from June to October, 2016, respectively, in South Korea. The high-throughput RNA sequencing of the samples mixed into one pool resulted in a single large contig (5606-nt) with nearly complete MaYMV genome coverage. The contig was assembled from a total of 234,537 reads; the maximum, minimum...