Reusing Simulation Models for Weapons Effectiveness Analysis

Kangsun Lee

and Taesup Kim

Department of Computer Engineering, MyongJi University, MyongJiRo 116, YongIn, Kyunggi-Do, South Korea

Department of Business IT architecture, KT ds, 206 Jeongja, Seongnam, Kyunggi-Do, South Korea

Keywords: Reuse Repository, Ontology, Simulation-based Weapons Effectiveness Analysis.

Abstract: Simulation-based weapons effectiveness analysis involves complex modeling tasks to represent weapons,

natural environment and operational environment. An integrated M&S (Modeling and Simulation)

environment provides useful tools and services to partly automate the modeling tasks. Along with the M&S

environment, a model repository can help model developers to ease the required tasks by sharing predefined

and already validated models, generated from inside and outside the M&S environment. In this paper, we

introduce our M&S environment, OpenSIM (Open Simulation Engine for Interoperable Models), and

illustrate how the model repository in OpenSIM can enable users to reuse models for weapons effectiveness

analysis. OpenSIM manages weapon ontology and thesaurus dictionaries to assess structural and contextual

similarity between weapon models. We present semantic information and similarity measures of OpenSIM

and illustrate how the model repository of OpenSIM helps users locate reusable weapon models.

1 INTRODUCTION

As modern weapon systems require significant

money to develop, evaluating their effectiveness

becomes necessary before the actual development

(Department of Defense, 2001) is taken place.

Evaluating weapons effectiveness is a hard task,

since we have to consider not only weapon systems

themselves but also various war factors including

natural environment (i.e. sea, ground, air),

operational environment (i.e. anti-air warfare, anti-

surface warfare), and external systems (i.e.

command and control systems) (Hong, 2011). These

factors are hard to control in real world. Therefore,

simulation technology is believed to be a realistic

solution for analyzing and predicting weapons

effectiveness (Wang, 2010).

Simulation-based weapons effectiveness analysis

involves complex modeling tasks to represent

weapons dynamics and engagement environments.

An integrated M&S (Modeling and Simulation)

environment provides useful tools and services to

partly automate the modeling tasks, and helps users

to save development cost and time (Cho, 2007).

Along with the M&S environment, a model

repository can also help model developers to ease

the required tasks by sharing predefined and already

validated models (Benali, 2010).

Many research works have been proposed to

construct and manage model repositories for various

M&S applications. Although the existing

repositories have been partly successful in providing

efficient services, such as registering and retrieving

models, they still lack flexible matching services

regardless of structural discrepancies between

models (Yilmaz, 2011). Most of the existing

repositories employ key-word based search

techniques to find reusable candidates. However,

they are not powerful in taking into account the

contextual similarity between similar weapon

models. Also, utilizing the reuse repositories may be

limited, if users are unable to access the stored

models from anywhere on various execution

environments. Weapon models are usually

developed by many experts from various disciplines,

possibly dispersed over the network. Therefore, a

distributed repository can greatly facilitate

collaborative modeling among the experts.

In this paper, we introduce a distributed model

repository to support reuse of models for simulation-

based weapons effectiveness analysis. Our

repository has been implemented in an integrated

M&S environment, OpenSIM (Open Simulation

Engine for Interoperable Models) which is under

development by our research team (Lee, 2011).

OpenSIM manages a reuse repository in the cloud

114

Lee K. and Kim T..

Reusing Simulation Models for Weapons Effectiveness Analysis.

DOI: 10.5220/0004055901140119

In Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2012),

pages 114-119

ISBN: 978-989-8565-20-4

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

computing environment to store large amount of

data for representing weapon models. Utilization of

the reuse repository can be greatly improved, since

users can access the weapon models from anywhere

and on various execution and operating systems with

the help of cloud data storage and services.

OpenSIM describes models with three dimensions –

structure, attributes, and behaviors. Weapon

ontology has been constructed to organize similar

models and assess structural similarity between

models. Thesaurus dictionaries are also managed to

resolve textual discrepancies appeared in the names

of attributes and operations in weapon models.

Similarity metrics are defined to quantify the

structural and contextual similarity between models

and to guide the semantic search process. With the

help of the model repository and tools/services of

OpenSIM, model developers can efficiently conduct

their weapons effectiveness analysis by improving

reusability of weapon models.

This paper is organized as follows. Section 2

introduces the architecture of model repository in

OpenSIM. Section 3 presents the reuse framework in

OpenSIM to enable semantic search for reusable

models. Weapon ontology, thesaurus dictionaries

and the semantic search algorithm are explained in

Section 3. Section 4 presents implementation results

of our reusing framework. We conclude in Section 5

with future research works to achieve.

2 MODEL REPOSITORY IN

OPENSIM

OpenSIM is an integrated simulation environment to

help model developers perform weapons

effectiveness analysis. OpenSIM provides a suite of

tools and services for developing, executing and

analyzing simulations of weapon systems. In order

to facilitate the modeling process in OpenSIM, we

provide a repository that stores reusable weapon

models as shown in Figure 1. Clients can access the

reuse repository from anywhere with the help of

Storage Manager in OpenSIM. Storage services help

clients to register their models and search reusable

models according to their simulation objectives.

With the help of Resource Registrant, each model is

registered to the model repository with syntax (e.g.

interface information) and semantic (e.g. weapon

category) information. All these information are

zipped with an index bitmap to save space. In order

to support semantic search for weapon models,

queries are analyzed in Query Analyzer.

Morphological analysis is performed on the weapon

models to resolve textual discrepancies in the names

of attributes and operations. The Searcher ranks

candidates with the help of Ranking Module, and

recommends reusable models to the client. Detailed

explanation on the semantic information and the

search algorithm will be given in Chapters 3.

Figure 1: Model Repository Architecture in OpenSIM.

3 REUSE MECHANISM

Many weapon models have been developed by

various organizations. Although they exhibit

different forms in development programming

languages and platforms, they are similar in their

structures, attributes and operations. New weapon

systems are usually developed in order to improve

parts of the old ones. Therefore, there are very high

chances to reuse the existing weapon models for

analyzing the effectiveness of new weapons. In

order to maximize reusability, OpenSIM

recommends a model that has maximum similarity

in structure, attributes, and operations. Structure

similarity is assessed based on our weapon ontology,

while attributes and operations similarity are

assessed by resolving textual discrepancies based on

our weapon thesaurus dictionary. Section 3.1 – 3.3

present the details.

3.1 Weapon Ontology

Ontology formally represents knowledge as a set of

concepts within a domain, and the relationships

between those concepts (Silver, 2010). We construct

weapon ontology to organize weapon models.

Figure 2 shows a part of weapon ontology to

organize guided missiles. Guided missile models are

represented and related to others according to

military organizations (i.e. Air force, Army, Navy),

engagement types (i.e. air-to-air, air-to-ground,

ground-to-air, etc.), range (i.e. short, middle, long),

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115

guidance method (i.e. radar, laser, optic, GPS, etc.),

development details (i.e. platform, programming

language), and resolution (i.e. high, medium, low).

Figure 2: Weapon Ontology for Guided Missiles.

An index bitmap is associated with a weapon model,

in order to represent the ontology information space-

efficiently. All the structural information, including

military organization, warfare type, range, fidelity,

programming language, and guidance systems, are

represented with 25 bits as shown in Figure 3. These

bitmap indexes provide significant performance

advantages over traditional value-list indexes for

complex queries, such as searching for reusable

models, according to Oneil (Oneil, 1997). Structural

similarity can be assessed by logical operations (e.g.

and (&)) between bitmaps.

3.2 Weapon Thesaurus

Models may have attributes and operations with

different names, even though they have similar

meanings. We construct a weapon thesaurus

dictionary in order to group models that have similar

meanings in attributes and operations. Figure 4

shows a part of our thesaurus dictionary for guided

missiles. For example, hit rate, hit_rate,

hit ratio, hit and accuracy may have the

same meaning, even though their textual

appearances are different.

25 bits

Military

(3bit)

Warfare

(3bit)

Range

(3bit)

Fidelity

(3bit)

Guidance

(10bit)

Field Name

Example

Military

Army(001), Navy(010),

AirForce(100)

Warfare

Air-to-Air(001),

Ground-to-Air(010)

Range

Short(001), Medium(010),

Long(100)

Fidelity

High(001), Medium(010),

Low(100)

Programming

Language

C++(001), Java(010)

Guidance Type

Radar(0000000001),

Infrared light(0000000100)

Figure 3: Structure Index Map (in part).

By looking up the names of attributes and

operations in the thesaurus dictionary, we can assess

how a model is similar to other models regardless of

their textual appearances.

Wingspan

Altitude

Accuracy

Length

wingspan

altitude

accuracy

length

currentx

wingspread

flightaltitude

accuracyrate

scope

current_x

wingwidth

flight_altitude

accuracy_rate

spread

coordinate

wing_width

flightlevel

hit

lineardimension

coordinate_x

wingbreadth

flight_level

hitrate

lenear_dimension

Speed

Diameter

Length

Altitude

Ceiling

speed

caliber

spread

flightlevel

serviceceiling

pace

lineardimension

flight_altitude

practicalceiling

velocity

Figure 4: Weapon Thesaurus Dictionary for Guided

Missiles (in part).

3.3 Semantic Search Process

In order to locate reusable models, our search

algorithm calculates similarity in three dimensions –

structure, attributes, behaviours (or operations).

Given a user model X, structural similarity with

other model, Y, is calculated by the following

equation:

S_Similarity(X,Y) =

Σ (Index (X

) & Index (Y

))*w

∀i=1..25

where, Index(X

) is the i

bit in the index bitmap

of model X, and w

is a weight factor for comparing

the i

bit, and & is logical and operation.

Two models can be considered as similar if they

have many attributes in common. In order to resolve

morphological discrepancies between attributes, we

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first look up the thesaurus dictionary to replace an

attribute name with the representative name in the

thesaurus dictionary. Then, attributes similarity,

A_Similarity(X,Y), between model X and

model Y can be calculated by cross-checking all

attributes of model X and model Y, and counting

the number of attributes that belong to both of the

models. O_similarity(X,Y), the measure for

assessing similarity on operations between model X

and model Y, can be determined with the same

process as in A_Similarity.

Finally, the search algorithm determines the

overall similarity, Similarity, by calculating the

weighted sum of S_Similarity,

A_Similarity, and O_Similarity. High

Similarity valued models are recommended to

users as reusable candidates.

4 IMPLEMENTATION

A distributed M&S resource storage has been

constructed in OpenSIM based on Apache Hadoop

and HDFS (Hadoop Distributed File System)

(Apache, 2011). Apache Hadoop is a software

framework that supports the distributed processing

of large data sets across clusters of computers.

Applications are able to work with thousands of

nodes and petabytes of data within the Hadoop

framework. HDFS is a distributed, scalable, and

portable file system written in Java for the Hadoop

framework. HDFS provides services to replicate data

across multiple hosts for high reliability, to

rebalance data by moving copies around, and to keep

the replication of data high. Detailed explanation on

cloud data storage and services are found in the

works of Franklin (Franklin, 2005) and Chuck

(Chuck, 2010).

Weapon models and their associated data

resources are stored across three machines with the

help of HDFS. Clients can transparently access

weapon models stored in the OpenSIM repository

through TCP/IP communication, regardless of their

execution environments and operating systems.

Section 4.1 – 4.3 illustrate how we can reuse weapon

models in OpenSIM repository through an example

of Mistral missile.

4.1 Registration

Users register their weapon models to the repository

in OpenSIM. Figure 5 shows a registration tool in

OpenSIM.

Upon completing modelling tasks on OpenSIM,

users register their models to the repository.

Figure 5: Registration Process in OpenSIM.

Attributes, Operations, and general information

(e.g. model name, creator, publisher, implementation

details (e.g. programming language, operating

system), component type (e.g. C++, DLL, etc.),

ownership) can be selectively published for users

discretion. For example, attributes such as

(x,y,z) position of a missile, and operation

names, such as fireTarget, are published by

users. All published data are then automatically

represented in XML and RDF data files as shown in

Figure 6. An index bitmap is also associated with the

registered model based on the weapon ontology

discussed in section 3.1.

Suppose the Mistral model has been developed

by army to analyze the effectiveness of short-range,

infrared ray missile in the anti-air warfare. Also,

suppose this model has been developed by C++ with

detailed dynamics and attributes (high resolution).

By matching this information with the weapon

ontology in Figure 2, the index bitmap of this model

is defined as 0010010010010010000000100.

This index bitmap is specified in the last row of the

RDF data file in Figure 6.

4.2 Semantic Search

Semantic search in OpenSIM is performed

throughout the three phases – structure matching,

attribute matching and operation matching phase.

Suppose a modeller wants to reuse available missile

models that have been developed by army for

analyzing weapons effectiveness in anti-air warfare.

Suppose the modeller also wants to reuse short-

distance and infrared ray guided missiles. Figure 7

shows a wizard provided in OpenSIM to perform the

Reusing Simulation Models for Weapons Effectiveness Analysis

117

structure matching.

<?xml version="1.0" encoding="EUC-KR"?>

<launchWeightName>dischargeWeight</launchWeightName>

<speedName>velocity</speedName>

</attribute>

<operationName>fireTarget</operationName>

<operationDescriptione>Calculates (x,y,z) to encounter a targer

</operationDescriptione>

</operation>

</MissileData>

(a) XML data file for Mistral weapon model – in part

<?xml version="1.0" encoding="EUC-KR"?>

<RDF

xmlns="http://www.w3.org/TR/WD-rdf-syntax#"

xmlns:dc="http://selab.mju.ac.kr#"

xmlns:inducement="http://selab.mju.ac.kr/inducement/"

<dc:Title>Mistral</dc:Title>

<dc:Creator>Taesup</dc:Creator>

<dc:Type>Missile</dc:Type>

<dc:Contributor>SELAB</dc:Contributor>

<dc:Publisher>SELAB</dc:Publisher>

<dc:Date>2011/11/21</dc:Date>

<dc:Format>C++_DLL</dc:Format>

<dc:Subject>anti-air weapon</dc:Subject>

<dc:Name>Mistral</dc:Name>

<dc:Version>1.0</dc:Version>

<dc: Fidelity>HIGH</dc:MultiFidelity>

<dc:Country>USA</dc:Country>

<dc:Company>MBDA</dc:Company>

<dc:Military> Army</dc:Army>

<dc:Use>Anti-Air</dc:Use>

<dc:Distance>Short</dc:Distance>

<dc:Guidancet>

<inducement:Laser>Used</inducement:Laser>

<inducement:Radar>Unused</inducement:Radar>

<inducement:Infrared>Unused</inducement:Infrared>

<inducement:Order>Unused</inducement:Order>

<inducement:Optics>Unused</inducement:Optics>

<inducement:Inertia>Unused</inducement:Inertia>

</Description>

</dc:Guidancet>

<dc:maxSpeed>2.60</dc:maxSpeed>

<dc:maxRange>5.30</dc:maxRange>

<dc:BitMap>001 001 001 0000000100 001 001</dc:BitMap>

</Description>

</RDF>

(b) RDF data file of Mistral Model – in part

Figure 6: Data files for registering weapon models.

Upon selecting the search criteria, such as military

organization, and warfare type, SQL query is

automatically generated in OpenSIM with the

corresponding index bitmap. Structure matching is

performed by the steps specified in section 3.3.

Table 1 summarizes structure matching results in

descending order. The three models with highest

structure matching score are selected for further

attributes and operations matching. Based on the

missile thesaurus dictionary, attributes with similar

meanings are grouped. In this example, KP-SAM,

Mistral, and 9K38 are selected from the structure

matching, and their attributes are cross-checked after

resolving their textual differences with the help of

the missile thesaurus dictionary. Figure 8 shows the

OpenSIM tool to show the results from the attributes

and operations matching. Based on the modelling

and simulation objectives, users can choose reusable

models that have desired attributes. Operation names

are also checked based on the thesaurus dictionary,

and operations with the similar meaning are grouped

for users to select reusable models. Detailed

descriptions on the operations are also available to

help users choose reusable models

Figure 7: OpenSIM wizard for Structure Matching.

Table1: Structure Matching Results – in part (Top 10)

Missile

Index bitmap

Matching Score

Sin-Goong

(KP-SAM)

001 001 001 001 010 0000000100

Mistral

001 001 001 001 001 0000000100

9K38 Eaglar

001 001 001 010 001 0000000100

FGM-148

001 001 001 001 100 0001000000

BGM-71 TOW

001 000 001 010 100 0000010000

Chun-Ma

(KSAM-1)

001 001 001 010 010 0000000010

KM-SAM

001 001 000 100 100 0000101010

9K115-2

001 000 001 100 010 0000010000

HyunMu-2

001 010 100 001 100 0000100000

HyunMu-1

001 010 100 001 001 0000100000

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Figure 8: Attributes and Operations Matching Results.

5 CONCLUSIONS

In this paper, we introduced a cloud repository in

OpenSIM for improving reusability of weapon

models. OpenSIM is an integrated modelling and

simulation environment to help users perform

weapons effectiveness analysis. OpenSIM provides

a set of tools and services to register and discover

reusable models. Weapon ontology has been

constructed to assist structure matching. Weapon

thesaurus dictionaries are also provided in OpenSIM

to resolve textual discrepancies appeared in the

names of attributes and operations. Reusable models

are recommended based on structure similarity,

attribute similarity and operation similarity. The

OpenSIM reuse repository is distributed over the

cloud computing environment. Weapon engineers

and simulation experts can cooperate easily by

accessing reusable weapon models in any places on

various operating and execution environment.

Although OpenSIM can successfully supports

semantic discovery of reusable models by

considering structure, attributes and operations

similarity, search results can be further improved by

considering more semantic information. So far, we

have dealt with Is-A and Has-A relationship between

models. However, various model relationships, such

as, part-of, relates-to, can be added for accurate

semantic discovery. We also would like to research

various metrics to properly quantify similarity

between models.

ACKNOWLEDGEMENTS

This work was supported by Defense Acquisition

Program Administration and Agency for Defense

Development under the contract UD080042AD,

Republic of Korea.

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