bluuebunny/crossref_metadata_2025_split huggingface dataset:
9.1.2 A Basic Primer in Life Cycle Cost Analysis
Authors: Redman F. Quentin , Crepea Andrew T. 
Published: November 2014

AbstractLife cycle cost (LCC) has become increasingly important as systems are designed with longer useful technological lives. In addition, deferred modernization due to limited budgets is causing many aging weapon systems to be extended, thus incurring greater support costs. This paper will present an overview of the process of life cycle cost analysis (LCCA). It will address the basic concepts and applications, along with a review of methodologies and some aspects of engineering economics.LCC is defined as the sum total of the direct, indirect, recurring, non‐recurring, and other related costs estimated to be incurred in the design, research and development (R&D), investment, operation, maintenance, and support of a system over its life cycle, i.e. its anticipated useful life span. It is normally organized into four phases: research, development, test, and evaluation (RDT&E); procurement (or acquisition), operations and support, and disposal.Economic Decisions are often made solely on initial investment or RDT&E and procurement cost. But this is only the proverbial tip of the iceberg – a portion of the total cost of ownership. More and more customers (especially government) are emphasizing and requiring an LCC perspective. To make intelligent acquisition decisions to meet a specific need, it is necessary to look beyond acquisition. In defense systems O&S costs can encompass up to 80% of the total LCC.LCC allows the evaluation of competing system proposals on the basis of total ownership cost and allows more effective budgeting of future funds such as O&S costs and disposal costs. A LCC perspective maximizes the benefit of applying strategies such as Cost as an Independent Variable (CAIV) and Design to Cost (DTC)Most LCCAs require a mix of estimating methodologies. The methods may be applied individually or in combination. Analogy and parametric tend to be most useful in the early stages of product development. As program and system design stabilizes parametric estimating becomes more useful. A detailed design facilitates engineering estimate and projection of actuals.In summary, this paper gives an overview of LCC as well as addressing many of the complexities of LCC analysis. More and more customers (especially government) are emphasizing and requiring an LCC perspective making LCC an increasingly important engineering design parameter. Total cost impact, not just initial near‐term cost, must be considered.
9.2.1 A Merlin Perspective Shines Light on Tough Issues
Authors: Carl Joseph W. 
Published: November 2014

AbstractShining light on tough issues requires leaders, including technical leaders, to commit to creation of a future that cannot reasonably be extrapolated from the place they are now standing. Fortunately, about a dozen years ago Dr. Charles Smith published an important paper (Smith, 1994) that has not received enough attention on the part of leaders responsible for solving the toughest issues of our time – affordable renewable energy, environmental health of the planet, failures of global cooperation, among others perhaps more mundane but of interest to systems engineers in their technical leadership roles. This paper reviews the advice in (Smith, 1994), and applies it to the establishment of visions for possible futures that are desired by a significant portion of the public, although the envisioned futures seem impractical to achieve. And that requires leaders to decide to identify with a future for which the way must be opened.
10.2.3 On the use of Semantic Web Technology for Requirements Satisfaction, or How do I find a good Bike?
Authors: Price David , Bodington Rob 
Published: November 2014

AbstractThis paper reports on an investigation into the use of Semantic Web technology in the Systems Engineering domain. In 2004 the World Wide Web Consortium standardized the Web Ontology Language (OWL). Software tools called Reasoners are available that support formal, logical analysis of concepts in a subset of OWL called OWL DL (for Description Logic). An approach to the use of this technology for the Systems Engineering task of identifying Systems that satisfy a set of Requirements is described.
11.1.2 Innovative Systems Engineering: A Creative System Development Approach
Authors: Powell Robert A. , Buede Dennis 
Published: November 2014

AbstractThe top‐level requirements for a system address the needs and objectives of the stakeholders and serves as a key input to the design process for a system's concept and detailed architecture. In today's systems engineering environment, the process of defining and choosing from multiple concepts is driven by top‐level (mission‐based) requirements. The use of requirements in engineering systems has gained such importance that an enormous amount of research focuses on various topics related to formulating, defining, and managing requirements. Recently, the US Department of Defense (DoD) and a few other organizations have moved to capability‐based systems engineering, which focuses on defining and trading off valuable capabilities rather than requirements.We have not always relied on requirements to initially define systems. Beginning with goals or defined system capabilities instead of requirements has proven as effective, if not more effective in the design of systems. The use of goals has been proven to produce 1) requirements that can be implemented, 2) minimal changes to requirements throughout system design since requirements are not yet fixed, and most importantly 3) a successful system. The purpose of this paper is to explore the use of goals as a precursor to requirements in developing a DoD military system. Case study analyses, of system development projects occurring as early as the late 1940s, reveal the use of goals as preeminent in developing successful operational systems. Although these cases represent DoD systems, specifically aerospace and missile systems, the concept of the use of goals as a precursor to requirements may generalize more broadly. This paper explores one case: the development of the Sidewinder missile.
11.3.1 How Good Is A Process? Evaluating Engineering Processes' Efficiency
Authors: Gilb Tom 
Published: November 2014

AbstractWhat is ‘best practice’ for an engineering process? How good is your current set of development, maintenance and service processes? How can we decide exactly which processes we are going to adopt in our organization, for example in a CMMI implementation?It is the assertion of this paper that such questions are often dealt with without explicit and quantified regard to the full set of real, and well‐defined business needs, as well as often not taking into consideration the current processes and the issues of changing them. We too often carry out and change processes because we are told to, not because there is a clearly defined need to do so.
11.3.2 Using Fuzzy Decision Support to Compare Systems Modelling Tools
Authors: Rajabally Eshan , Whittle Steve 
Published: November 2014

AbstractThe use of modelling in systems engineering is growing both in terms of model complexity and the importance attributed to model results. In accepting a model as fit‐for‐purpose, there are numerous considerations or criteria to be fulfilled and this acceptance is complicated by a number of issues, as reviewed in this paper. Consequently, model evaluation, comparison and selection may benefit from formal Multiple Criteria Decision Support (MCDS) and this benefit is explored here through application to a systems modelling case study. Specifically, an existing MCDS technique based upon fuzzy logic is used to compare five aerodynamics tools across fifteen key criteria in a typical context. An overview of the case study is presented in this paper and several benefits of fuzzy MCDS are identified. Despite the considerable effort required from model experts to conduct such MCDS, a more rigorous means of comparing models is provided than typical ad hoc efforts.
11.3.3 Enabling Measurement‐Driven System Development by Analyzing Testing Strategy Tradeoffs
Authors: Selby Richard W. 
Published: November 2014

ABSTRACTMeasurement‐driven system development focuses on using quantitative data to evaluate capabilities, benefits, progress, and tradeoffs as well as identify improvement opportunities. This paper describes a controlled study that addresses software testing effectiveness and focuses on the combination of individual testing techniques into team‐based testing strategies. This analysis is intended to enable measurement‐driven process improvement by characterizing how testing effectiveness relates to several factors, including testing strategy, software type, and developer expertise. In this study, a representative group of software development professionals applied common testing techniques to different types of software. This study compares the six possible team combinations of three testing techniques: (1) code reading by stepwise abstraction, (2) functional testing using equivalence partitioning and boundary value analysis, and (3) structural testing using 100% statement coverage criteria. Thirty‐two professional developers applied the techniques to three unit‐sized programs in a fractional factorial experimental design.The major results of this study are the following.
The six combined testing strategies detected 17% more of the programs' faults on the average than did the three single techniques, which was a 35% improvement in fault detection.
The highest percentages of the programs' faults were detected when there was a combination of either two code readers or a code reader and a functional tester. However, a pairing of two code readers detected more faults per hour than did a pairing of a code reader and a functional tester.
The pairing of two individuals of advanced expertise resulted in the highest percentage of faults being detected.
The most cost‐effective (number of faults detected per hour) testing strategy overall was when code reading was applied by an individual. The most cost‐effective combined testing strategy was when a code reader was paired with either another code reader or a structural tester.
Both the percentage of faults detected and the fault detection cost‐effectiveness depended on the type of software being tested.
In conclusion, we outline future research directions that build on these strategies and ideas.
11.4.1 Generating Predictive Models Using Decision Trees and Neural Networks for Large‐Scale Systems Engineering
Authors: Selby Richard W. 
Published: November 2014

ABSTRACTSystems engineering must tackle the challenges of computational systems that are increasingly large‐scale and software‐intensive in terms of system size, component breadth and maturity, and development heterogeneity. This research describes and empirically evaluates techniques for generating predictive models for enabling large‐scale system development and management. We describe two types of metric‐driven decision models, decision trees and neural networks, which classify software components in large systems according to their likelihood of having user‐specified properties such as high fault‐proneness or high development effort. The metric‐driven decision models enable coarse‐grain analysis of large‐scale multi‐component heterogeneous systems, and they identify high‐payoff areas for directing the application of fine‐grain analysis techniques for fault detection or redesign. The decision models serve as metric integration mechanisms that enable the synergistic use of numerous metrics simultaneously and integrate measurements collected by development tools or infrastructure. Model generation techniques automatically generate the decision models to calibrate them to new projects and organizations.We evaluate the predictive effectiveness of the decision models in terms of correctness, consistency, and completeness using fault and effort data from large NASA systems. Correctness is defined as the percent of components correctly identified, consistency is defined as 100% minus the percent of false positives, and completeness is defined as 100% minus the percent of false negatives. On average, the decision models had 83.44% correctness, 71.96% consistency, and 65.25% completeness in predictions of high fault and high effort software components. The network models had 89.63% correctness, 79.49% consistency, and 69.09% completeness, while the tree models had 77.25% correctness, 64.42% consistency, and 61.40% completeness. Non‐parametric ANOVA comparisons showed that the network models were statistically more accurate than the tree models (α < 0.0001).
Cost Models with Explicit Uncertainties for Electronic Architecture Trade‐off and Risk Analysis
Authors: Axelsson Jakob 
Published: November 2014

AbstractThis paper discusses how the early phases of developing embedded electronic systems can be improved by enhanced modelling of cost and performance that includes explicit handling of uncertainties. The approach is to add cost information to existing UML models, capture uncertainties using probability distributions, and use Monte Carlo simulation to analyze the risk of not reaching the cost targets. It is demonstrated how the information obtained can be used when evaluating different architecture alternatives, while including both development and product cost as well as risk in the trade‐off.
Process in Enterprise Systems Engineering
Authors: DeRosa Joseph K. , McCaughin Keith 
Published: November 2014

AbstractThis paper addresses the importance of process in enterprise systems engineering. It relates process to the balance between complexity and order in a complex adaptive system, and it extends the rationale for process from efficiency to both efficiency and effectiveness in enterprise adaptation. It develops several enterprise principles based on the works of Ashby, Ackoff, Axelrod and Cohen, Barabasi, Gharajedaghi, and others. It makes no attempt to be mathematical or rigorous, but rather postulates several heuristics. It discusses the deep structure of engineering process.
Capturing Total System of Systems Costs Using SEER‐H with Total System Vision (TSV) and SEER‐SEM: An Example Application
Authors: Falque Joseph P. 
Published: November 2014

AbstractThis paper1 discusses how a parametric life cycle cost model (LCC), SEER‐H with Total System Vision (TSV) and SEER‐SEM software estimation model, are used to facilitate the cost impact assessment of Systems Engineering labor and materials for a Major System of Systems project containing both hardware and software components. The main purpose is to introduce the underlying methodology and demonstrate the flexibility of the cost model for developing System Level Costs at the System of Systems Level. Readers are introduced to the model, its premise, and how engineers use it to obtain system level engineering labor and material estimates on a Constellation of 18 earth orbiting Optical Satellites. The system includes the launch vehicles and services, mission control and a world wide tracking antenna sites as well as operational and support costs.
Universal Electronic Health Record: Just what the doctor ordered
Authors: Shahi Rajiv 
Published: November 2014

AbstractThe time has come for health informatics to unify; this call for integration will help in better patient care across various systems spread globally. This feat requires standardization of health related information. The implementation of Electronic Health Record (EHR) is such standard which will facilitate collaboration of disparate systems. This emerging standard proposes to digitally accumulate patient's entire healthcare information and allow real time effortless exchange between different care providing sites or individuals. This will result in non redundant patient data accessibility (Any time, any where, any device) resulting in improved efficiency and quality of patient care. The realization of this effort requires different standards organizations like Health Level 7 (HL7), Clinical Data Interchange Standards Consortium (CDISC) and European Committee for Standardization (CEN) to collaborate and produce standards that are interoperable between different organizations. This collaborated effort will result as building blocks for foundation of neural health care network. This state of the art health informatics grid has to overcome barriers in technical implementation, health care domain, globalization, and education and training before realisation of the dream to have an integrated EHR.
1.3.1 Modeling of hardware software performance of high‐tech systems
Authors: van den Bosch Peter , Muller Gerrit , Verhoef Marcel , Florescu Oana 
Published: November 2014

AbstractThe performance of the control system is an important aspect of a machine. It would be a waste if a high‐tech machine has been build such that it can physically achieve a high throughput, for example printed sheets of paper, but is limited because the software controlling it cannot keep up. Unfortunately, with current techniques it is hard to “predict” beforehand what the performance of the software will be when it finally runs in the real system on the real processor(s). There are two (extreme) ways to deal with it:
Over‐dimension the hardware platform to make sure the software will run.
Implement the software, then run and evaluate its performance on the target hardware platform. Then use this information in the next design cycle.The disadvantages of both approaches are clear. In the first situation the cost price of the entire system will surely be higher than necessary. In the second case, the design time is increased dramatically because more design cycles are needed. Therefore, it is important to strive to a development method that leads to fast design cycles for software performance, while having an accurate enough prediction. In this paper we will discuss a pragmatic modeling approach to design for performance in the domain of software intensive systems.
1.3.3 Hybrid System Dynamic, Petri Net, and Agent‐Based Modeling of the Air and Space Operations Center
Authors: Mathieu Jennifer , James John , Mahoney Paula , Boiney Lindsley , Hubbard Richard , White Brian 
Published: November 2014

AbstractIn an earlier paper, an existing Air and Space Operations Center (AOC) process model (i.e., Petri net) and new global and mission models for the environment in which the AOC operates (i.e., System Dynamics) were linked (federated). The focus of this paper is the development of an operator‐environment model (i.e., Agent‐Based Model). An existing systems framework for attention allocation of operators within the AOC has been implemented that supports multiple modeling paradigms. The results for linking the Petri net and System Dynamics models are summarized, and new results for the Agent‐Based Model are presented based on a pilot‐down scenario. It has been observed that many AOC operators can become distracted by a pilot‐down critical event, even if the operator is not able to directly assist in the rescue. Furthermore, this distraction has been hypothesized to have a detrimental effect on the activities the non‐involved operators are currently handling.
1.4.1 An Integrated Approach to Developing Systems Professionals
Authors: Davidz Heidi L. , Maier Mark W. 
Published: November 2014

AbstractAs the level of integration and the complexity of engineering systems increase, there is an increasing need to develop systems capabilities in engineers. Often, the demand for increased systems capabilities is automatically translated into a need to develop the systems capabilities of individuals. However, the systems capabilities of groups and organizations are what really matter for complex engineering systems, and individual capabilities do not automatically translate into group and organizational capabilities. This paper describes an integrated approach to developing systems professionals. To guide practitioners in utilizing relevant theoretical issues and research results, a five‐step plan to developing systems professionals is given. First, there is a description of how to frame the relevant problem space. Next, desired roles and competencies are linked to this problem spectrum. After describing how to develop an appropriate multi‐level strategy, the paper discusses how individual systems skills combine. Potential assessments for the development approach are shown. Since group and organizational issues can trump efforts to develop the systems capabilities of individuals, it is important to frame the relevant problem space, match the desired capabilities to that space, and create a multi‐level integrated strategy to develop those capabilities in individuals, groups, and organizations.
1.4.4 Measurably Improving Your Systems Engineering Requirements
Authors: Olson Timothy G. 
Published: November 2014

AbstractRequirements continue to be a major problem area for most organizations. According to industry reports, the leading causes of quality, cost, and schedule problems are lack of understanding of the customer's needs, incomplete requirement specifications, and managing changing requirements. So what can an organization focus on now to improve their systems engineering requirements? This paper will describe some practical strategies that organizations can use to measurably improve their requirements as well as their requirements process.The objectives of this paper are to:
Present some requirements problems from industry.
Present a useful classification of requirements problems from the literature.
Provide specific strategies to address the requirements problems from the literature.
Describe some practical strategies that organizations can use to measurably improve their requirements.
Provide some requirements lessons learned and provide some industry references.
1.5.1 A Decision Support System to Schedule Design Activities in Aircraft Industry
Authors: LIZARRALDE Iban , ESQUIROL Patrick , RIVIERE Arnaud 
Published: November 2014

AbstractThis paper highlights the relationship between Project Management and Systems Engineering through a framework proposal that links dependencies management and project scheduling. It investigates the problems of project scheduling at the design stage of the development of a civil aircraft, mainly characterised by a dynamic environment and uncertainties concerning the duration of activities.This framework is supported by methods dedicated to the management of schedules at tactical levels with fully elastic tasks that takes into account dependencies between design teams as new constraints.For end users, this framework can be considered as a Decision Support System (DSS). The DSS is a tool to check if all scheduling constraints are satisfied or to solve over‐constrained problems through interactions with end‐users. This approach, illustrated with AIRBUS use cases, is flexible enough to be implemented within the aerospace industry, by facilitating cooperation between design teams and by providing the possibility to carry out schedules simulations.