For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE)
and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes
increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for
decision support systems. In this paper, we overview some information fusion methods in decision support and address
the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion
model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an
information fusion system. Decision support implies that information methods augment user decision making as opposed
to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that
facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain,
and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy,
confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality
(time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and
robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

/pdf/information-fusion-measures-of-effectiveness-moe-for-2djlrw5t6d.pdf

Information Fusion Measures of Effectiveness (MOE) for Decision Support

Simulation and optimization have been successfully combined to solve real-world decision making problems. However, there is no formal structure to define the integration between simulation and optimization. This deters the development of simulation-based optimization methods that have a proper balance between the desired features (i.e. generality, efficiency, high-dimensionality and transparency). This research provides two contributions to the problem above by providing: 1) the design of the framework that facilitates the fulfillment of the aforementioned features; 2) the implementation of the framework in Java. The proposed framework is developed based on Zeigler's modeling and simulation framework and the phases of an optimization study in operations research. The test and evaluation show that the desired features are successfully satisfied.

The simulation-based multi-objective evolutionary optimization (SIMEON) framework.

Artificial systems that generate contingency-based teleological behaviors in real-time, are difficult to model. This chapter describes a modeling and simulation (M&S) framework designed specifically to reduce this difficulty. The described Knowledge-based Contingency-driven Generative Systems (KCGS) framework combines aspects of SES theory, DEVS-based general systems theory, net-centric heterogeneous simulation, knowledge engineering, cognitive modeling, and domain-specific language development using meta-modeling. The chapter outlines the theoretical and technical foundations of the KCGS framework as realized in the Cognitive Systems Specification Framework (CS2F), a subset of KCGS. Two executable models are described to illustrate how models of autonomous, goalpursuing cognitive systems can be modeled and simulated in the framework. The technical content and agent descriptions in the chapter illustrate how the M&S of the artificial depends critically on ontology, epistemology, and teleology in the KCGS framework.

A Framework for Modeling and Simulation of the Artificial

In recent years, because of the development of marine military science technology, there is a growing interest in the unmanned systems throughout the world Also, the demand of Unmanned Surface Vehicles (USVs) which can be autonomously operated without the operator intervention is increasing dramatically The growing interests lie in the facts that those USVs can be manufactured at much lower costs, and can be operated without the human fatigue, while can be sent to the hostile or quite dangerous areas that are inherently unhealthy for human operators The utilization and the deployment of such vessels will continue to grow in the future In this paper, along with the technological development of unmanned surface vehicles, we investigate and analyze the cases of already developed platforms and identify the trends of the technological advances Additionally, we suggest the future directions of development

/pdf/analysis-of-design-directions-for-unmanned-surface-vehicles-4oc34r9jyh.pdf

Analysis of Design Directions for Unmanned Surface Vehicles (USVs)

/pdf/the-simulation-based-multi-objective-evolutionary-5fak1se6k4.pdf

The simulation-based multi-objective evolutionary optimization (SIMEON) framework

With the advent of Network-Centric Warfare (NCW) concepts, Command and Control (C2) systems need efficient methods to produce, exchange, and consume diverse kinds of information within the networks. The System Entity Structure (SES) is an ontology framework that can facilitate information exchange in a network environment. From the perspective of the SES information exchange framework, the Battle Management Language (BML) serves to specify the information desired by a consumer in an unambiguous way.This paper formulates information exchange in the SES ontology via BML and defines novel pruning and transformation processes of the SES to extract and fuse data into higher-level representations.This supports increased interoperability between human users and other sensor systems.To demonstrate the effectiveness of the proposed information exchange framework for data fusion systems, we illustrate the approach in an air defense operation scenario using Discrete Event System Specification (DEVS) modeling and simulation.The demonstration shows that the information requested by a commander is delivered in the right way at the right time so that it can support agile decision making against threats.

System Entity Structure Ontological Data Fusion Process Integrated with C2 Systems

Discrete event system specifications (DEVS) is a mathematical formalism based on system theoretic principles, which has evolved with state-of-technologies implementation over the past few decades. In this paper, we discuss a DEVS framework to solve parameter optimization problems. As a case study, we consider the Link-11 gateway that has been developed by the Joint Interoperability Test Command (JITC) is to provide interoperability between Link-11 network and TCP/IP network. The performance of Link-11 gateway is highly sensitive to the sampling rate of soundcards since the frame time of Link-11 signal is very short. Unfortunately, the sampling rate is not as accurate as it is specified by the manufacture of soundcards. A solution is to search for an optimized parameter that can be used to adjust the sampling rate. We apply an optimization technique to search for optimal sampling rate in modeling and simulation environment, DEVSJAVA. The DEVS-based framework can facilitate efficient global optimal parameter search capability and reduce execution time benefiting from its parallel and variable structure implementation.

/pdf/a-devs-based-framework-for-simulation-optimization-case-1jq2q1iem3.pdf

A DEVS-based framework for simulation optimization: Case study of Link-11 gateway parameter tuning

Data Fusion (DF) process is in the interest of the military community since it provides the opportunity to achieve information superiority. The System Entity Structure (SES) is an ontology framework that can facilitate information exchange and represent knowledge in a network-centric environment. We explore an idea of adopting the SES ontology to fuse raw data into higher-level information in DF systems for Command and Control (C2). Pruning and transformation processes of the SES ontology generate various levels of information in accordance with C2 systems' needs, which support decision-making process in an automated way. C2 systems receive customized information through the SES Information Exchange Framework (IEF). Simulation results demonstrate the ontological DF process, and evaluate its performance in the application to categorization of air object flight patterns into high level user oriented classes.

/pdf/ses-based-ontological-process-for-high-level-information-559g7m6wqt.pdf

SES-based ontological process for high level information fusion

This paper explores the Information Exchange Framework (IEF) concept of distributed data fusion sensor networks in Network-centric environment. It is used to build up integrative battlefield pictures through the Battle Management Language (BML) and System Entity Structure (SES) ontology for C2 systems. The C2 process requires multi-level information to assess the current situation in a sound manner. Superiority of information is critical factor to win battles. The SES is an ontology framework that can facilitate information exchange in a network environment. From the perspective of the SES framework, BML serves to express pragmatic frames, since it can specify the information desired by a consumer in an unambiguous way. We explain the idea of information exchange in the SES ontology via BML and demonstrate pruning and transformation processes of SES with proof-of-concept examples. Keywords-component; SES; Ontology; Information Exchange Framework; BML; Pragmatic Frame; Data Fusion

/pdf/bml-enabled-information-exchange-framework-in-ses-ontology-4y2sbrmie1.pdf

BML Enabled Information Exchange Framework in SES Ontology for C2

이 연구의 목적은 6･25전쟁 이전 이승만정부의 해양위협인식과 그에 따른 해군정책의 양상과 한계를 재조명해보는 데 있다. 이승만정부는 북･서쪽 해양 방면에서 잠수함을 포함한 소련 해군의 적대적 활동, 국공내전에서 승리한 중국공산당의 황해 제해권 확대, 조선인민군의 해양침투 및 전면 공격 가능성 등을 위협으로 인식하고 있었다. 남･동쪽 해양 방면으로부터는 일본 어선의 출몰과 일본 재무장의 위협이 있다고 보았다. 이승만정부는 이러한 양면위협인식에 근거해서 첫째, 미국 군사원조의 획득과 해군기지 사용권 제공, 둘째, 자주적 해군 증강, 셋째, 해군부 독립 등을 통한 육･해군 병립발전을 추진했다. 이러한 해군정책은 상이했던 미국의 전략, 미약했던 한국의 경제력, 육군 우선의 건군정책 등으로 한계에 봉착했다. 그럼에도 불구하고 6･25전쟁 발발 직후 백두산함의 활약 등과 같은 성과도 있었다. 

Maritime Threat Perception and Naval Policy of the Syngman Rhee Government before the 6/25 War in Korea, 1948-1950

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