Bingfeng Ge

An Interactive Portfolio Decision Analysis Approach for System-of-Systems Architecting Using the Graph Model for Conflict Resolution

A novel approach based on the graph model for conflict resolution (GMCR) methodology is proposed to address the problem of multistakeholder system portfolio decision analysis encountered in architecting a system of systems (SoS) with desired capabilities. More specifically, a flexible four-process framework for capability-based SoS architecting containing interactive portfolio decision analysis to promote multistakeholder design negotiations on system portfolio selections is presented. By taking full advantage of the inherent realistic and flexible design of the GMCR paradigm, an interactive portfolio decision analysis approach is designed to facilitate the systematic modeling and analysis of system portfolio decisions at the SoS level in order to achieve potential compromises among all key stakeholders having disparate preferences and interacting according to different conflict behavior patterns. This approach permits the prediction of possible mutually agreeable system portfolios for SoS architecture development. Last, the feasibility of the proposed approach is demonstrated using an illustrative example.

A Novel Executable Modeling Approach for System-of-Systems Architecture

A novel executable modeling approach is proposed for system-of-systems architecture by taking full advantage of the Department of Defense Architecture Framework (DoDAF) Meta-model (DM2) and defining the common model transformation specification at the metamodel level. This methodology provides more flexibility and adaptability for the automated construction of executable models directly from the architectural data rather than from static models. More specifically, the architectural data metamodel is first established to guide architectural data modeling of core data elements and associations in DM2 as the common and consistent data dictionary for architecture modeling, while the executable formalism metamodel is designed to formally define executable models. Then, the mapping rules between both metamodels are presented as the common transformation specification regardless of which modeling language or methodology is employed in developing architectural descriptions. Finally, eXtensible Markup Language (XML) technology, like XML schema languages and eXtensible Stylesheet Language specifications, is discussed to facilitate the automated transformation of executable models from architectural instance data. The feasibility of the proposed approach is illustrated using a published example where colored Petri net (CPN) is used as the executable formalism.

Development trend forecasting for coherent light generator technology based on patent citation network analysis

A forecasting methodology for technology development trends is proposed based on a two-level network model consisting of knowledge-transfer among patents and patent subclasses, with the aim to confront the increasing complex challenge in technology investment and management. More specifically, the patents of the “coherent light generators” classification, granted from 1976 to 2014 by examiners of the United States Patent and Trademark Office, are collected and with which the first-level citation network is constructed first. Then, a new approach to assess patent importance from the perspective of topological structure is provided and the second-level citation network, which consists of patent subclasses, is produced with the evaluation results. Moreover, three assessment indices of the subclass citation network are abstracted as impact parameters for technology development trends. Finally, two typical time series models, the Bass and ARIMA model, are utilized and compared for development trend forecasting. Based on the results of evolution prediction and network analysis, the highlighted patent subclasses with more development potential are identified, and the correlation between technology development opportunity and topological structure of the patent citation network is discussed.

Structural robustness of combat networks of weapon system-of-systems based on the operation loop

ABSTRACT The structural robustness of the combat network of weapon system-of-systems (CNWSOS) has been widely used to characterise its operation ability and survivability in the integrated joint operations. In this paper, after introducing the concept of the operation loop, a directed CNWSOS network model is first put forward. Then, the directed natural connectivity, a new measure of the CNWSOS robustness that extends the concept of natural connectivity to the directed combat networks, is proposed to provide both a definite physical meaning and a simple mathematical formulation. Last, the feasibility and effectiveness of the proposed structural robustness measure are demonstrated using both numerical experiments and an empirical case study. The directed natural connectivity facilitates a more sensitive and precise quantitative analysis of the structural robustness, which in turn provides useful insights for designing a more robust CNWSOS, including improving the accuracy of intelligence acquisition, enhancing the communication capabilities of weapon systems, and strengthening the capability of precision strikes.

An integrated technology pushing and requirement pulling model for weapon system portfolio selection in defence acquisition and manufacturing

An integrated technology pushing and requirement pulling model is presented to address the problem of selecting a suitable portfolio from several candidate multi-function weapon systems in the defence acquisition and manufacturing process. The optimal weapon system portfolios both satisfy the technical effects and compound capability requirements with relative acquisition and manufacturing constraints. In the first stage, multiple criteria analysis is employed to build a portfolio value hierarchal structure from two main perspectives: a five-level set of technology pushing measures and a two-level set of requirement pulling measures. The developmental maturity level of a technology, its functionality, systems, capability, and portfolio are all captured by technology pushing measures, based on an additive multiple criteria model. The requirement satisfaction level of a system or portfolio is obtained by requirement pulling measures based on linear programming optimization and the first-ignorance model, which is founded on expert opinion, using pairwise comparisons. In the second stage, by means of manufacturing cost consumption and capability requirement constraint assumptions, the dominance structure of the portfolios is designed and all the technology and requirement portfolios are generated as a set of Pareto-optimal solutions, through multi-objective integer programming. An illustrative case study is presented to validate the efficiency of the proposed model, and the computational results are further analysed and discussed.

Executable System-of-Systems architecting based on DoDAF meta-model

Methods used to gain executable architecture are all based on transforming view models to executable models, which can not take the advantage of “data-centric” approach adopted by DoDAF2.0. A novel executable architecting approach transforming DoDAF Meta-model (DM2) to executable model directly is proposed, which can overcame the limitation of traditional approaches that information consistency and data completeness may be damaged. An example to analyze Command and Control (C2) capability in Air Defense System-of-Systems (ADSoS) is provided in detail to illustrate the process to transform DM2 to Coloured Petri Net (CPN). Besides, the input and output data is also given in detail along with the process illustration.

A data-centric executable modeling approach for system-of-systems architecture

A data-centric executable modeling approach is proposed for system-of-Systems (SoS) architecture by taking full advantage of the Department of Defense Architecture Framework (DoDAF) Meta-model (DM2), which provides more flexibility and adaptability to the automated construction of executable models directly from the architectural data. Firstly, the architectural data meta-model is established to guide architectural data modeling of core data elements and associations in DM2 as the common and consistent data dictionary for architecture modeling, and the executable formalism meta-model is designed to formally define executable models. Then, the mapping rules between both meta-models are defined as the common transformation specification regardless of what modeling language or methodology is employed in developing architectural descriptions. Finally, XML (eXtensible Markup Language) technologies are discussed to facilitate the automated transformation of executable models from architectural instance data. Colored Petri Net (CPN) is used as an illustrative executable formalism in the discussion of the proposed approach.

Evolution monitoring for innovation sources using patent cluster analysis

According to the increasing importance of advanced technologies for economy growth and the incremental complexity of research and development management, a novel methodology is proposed in this paper to monitor the evolution trace of innovation sources. This approach focuses on the knowledge-transfer among technologies using patent cluster analysis. More specifically, a citation network model, consisting of patents in “Coherent Light Generators” classification, is established with the data collected from the United States Patent and Trademark Office. In addition, dynamical topological structure is investigated to probe into the overview properties and identify key milestones for the expanding citation network from 1976 to 2014. Next, a novel framework for patent clustering is developed to find out knowledge chunks of which internal knowledge-flows are dense while cut edges are sparse. Community detection algorithms are compared with different assessment indices based on citation network and the selected solution is improved using optimization objectives of cluster analysis. Then, the dynamical structure of the detected knowledge chunks is investigated and the evolution of innovation sources, identified by k-core decomposition, is monitored to unveil the technology development trace. Finally, analysis results are discussed and related conclusions are summarized. This article improves approaches for patent cluster analysis and develops a new follow-up investigation methodology for detected knowledge chunks. It is discovered there are not only scale increases, but also the integration for knowledge chunks during the focal period. Identifying the knowledge chunks which obtain rapid growth in both cluster scale and innovation source is useful to detect technology development opportunities.

Belief preference in graph model for conflict resolution with two decision-makers

In order to deal with the subjectivity, epistemic uncertainty and incompleteness of input data, a novel methodology is proposed for interactive decision making between two players based on the graph model for conflict resolution (GMCR) with belief preference. First, a new GMCR model is calibrated, in which belief preferences are generated via evaluating the utilities of feasible states. Second, new definitions of unilateral improvements and four stability concepts are presented for conflict analysis. Finally, an interactive decision case on a weapon system of systems (WSoS) architecting is studied to demonstrate the feasibility of the proposed methodology.

Automatic sleep and wake classifier with heart rate and pulse oximetry: Derived dynamic time warping features and logistic model

This paper presents a novel sleep/wake classification method based on heart rate and pulse oximetry, using logistic model with derived dynamic time warping and correlation features introduced, which were used to classify sleep stages. 100 sleep recordings obtained from the Sleep Heart Health Study dataset, which are available on websites, were used to validate the proposed method. Using the features extracted by our research, classification performance of a LD classifier and feedforward neural classifier were compared to the proposed logistic classifier. The classification accuracy and AUC of the logistic classifier was found to be better (83.8%, 0.924) than those of the two other classifiers (80.1%, 0.732 for LD and 64.0%, 0.801 for neural classifier). The result demonstrated that the proposed logistic classifier using the derived dynamic time warping and correlation features extracted from heart rate and pulse oximetry signals can classify sleep stages efficiently and effectively, which can provide a novel way to carry out automatic sleep stage classification with a wearable device.