Computer Simulation Lab

The modern naval air defense of a fleet is a critical task dictating the equipment, the operation, and the management of the fleet. Military modelers consider that an improved weapon system in naval air defense (i.e. the AEGIS system) is the most critical enabler of defense at the engagement level. However, at the mission execution level, naval air defense is a cooperative endeavor of humans and weapon systems. The weapon system and the command and control (C2) structure of a fleet engage in the situation through human reporting-in and commands, as well as weapon deployments. Hence, this paper models the combination of the human and the weapon systems in naval air defense by covering the C2 hierarchy of the fleet, as well as the weapon systems of warships. After developing this mission-level model, we perform battle experiments with varying parameters in the human and weapon aspects. These battle experiments inform us of the impact of the changes in the human and the weapon systems. For example, the speed of incoming missiles is a critical parameter for a fleet’s survival; yet the decision-making speed is another outstanding parameter, which illustrates that there is more to improve than the weapon system when considering the mission level. This modeling and these experiments provide an example, suggesting a method of combining the human C2 and the weapon systems at the mission level in the military domain.

Discrete Event Systems Specification (DEVS) formalism supports the specification of discrete event models in a hierarchical and modular manner. Efforts have been made to develop the simulation environments for the modeling and simulation (M&S) of systems using DEVS formalism, particularly in defense M&S domains. This paper introduces the DEVSim++ toolset and its applications. The Object-Analysis Index (OAI) matrix is a tabular form of objects and analysis indices for requirements analysis. DEVSim++ is a realization of DEVS formalism in C++ for M&S. VeriTool is a DEVS model verification tool. DEVSimHLA is a library to support High-level Architecture (HLA) in DEVSim++. Other tools, including KComLib, FOM2CPPClass, and KHLAAdaptor, are used to develop a smart adaptor that allows for the interoperation of simulators of any kind. PlugSim is a distributed simulation framework using plug-in methods. These tools are utilized in every stage of the M&S development process, as well as in every application of the M&S missions to the military domain. Accordingly, the applications implemented by the toolset are used in the training, analytic, and acquisition missions of the Republic of Korea military branches. We expect the DEVS applications to become more prolific as M&S demands grow, and our toolset is already proven as complete and efficient in the domain of defense M&S.

This paper presents a modeling method to demonstrate engagement-level military simulation which includes few combat objects, or entities. To this end, the paper, on the basis of the discrete event system specification (DEVS) formalism, centers on two ideas: (1) a combat entity’s model structure at the composition level; and (2) behavioral delineation of the entity’s elementary component. In detail, we classify the combat entity model into platform and weapon models and create six groups of the model categorized by two dimensions: three activities and two abstractions. And the elementary component in the group interprets an engagement scenario as a flow of executable tasks, which are expressed by DEVS semantics. The stated structures and semantics provide intuitive appeal, reducing the effort required to read and understand the model’s behavior. From the combat experiments, we can gain interesting experimental results regarding engagement situations employing underwater weapons and their tactical operations. Finally, we expect that this work will serve an immediate application suited to various engagement situations.

The discrimination of similar patterns is important because they are the major sources of the classification error. This paper proposes a novel method to improve the discrimination ability of convolutional neural networks (CNNs) by hybrid learning. The proposed method embeds a collection of discriminators as well as a recognizer in a shared CNN. By visualizing contrastive class saliency, we show that learning with embedded discriminators leads the shared CNN to detect and catch the differences among similar classes. Also proposed is a hybrid learning algorithm that learns recognition and discrimination together. The proposed method learns recognition focusing on the differences among similar classes, and thereby improves the discrimination ability of the CNN. Unlike conventional discrimination methods, the proposed method does not require predefined sets of similar classes or additional step to integrate its result with that of the recognizer. In experiments on two handwritten Hangul databases SERI95a and PE92, the proposed method reduced classification error from 2.56 to 2.33, and from 4.04 to 3.66 % respectively. These improvement lead to relative error reduction rates of 8.97 % on SERI95a, and 9.42 % on PE92. Our best results update the state-of-the-art performance which were 4.04 % on SERI95a and 7.08 % on PE92.

Speeding up the register-transfer level (RTL) simulation of network-on-chip (NoC) is essential for design optimization under various use scenarios and parameters. One of the promising approaches for RTL NoC speedup is high-level modeling. Conventional high-level modeling approaches lead to an accuracy problem or modeling efforts that are caused by the absence of modeling framework or requiring in-depth knowledge of specific behaviors of target NoCs. To support cycle-accurate and formal high-level modeling framework, we propose a cellular automata (CA) modeling framework for RTL NoC. The CA abstracts detailed RTL NoC dynamics into the proposed high-level state transitions, which support flit transmission among CA components through dynamically changing flit paths based on the target RTL routing and arbitration algorithms. To prevent the meaningless execution of stable CA, the CA are designed to be triggered by state-change events. The proposed simulation engine asynchronously invokes CA to update their states and perform actions of flit transmissions or flit-path changes based on the state-decision result. To reduce the modeling difficulty, we provide a test environment that generates the state-transition rules for CA after monitoring the relationships between high-level states and leading actions under randomly injected packets during target RTL NoC simulations. Experiments demonstrate cycle-level functional homogeneity between RTL and the abstracted CA NoC models and significant simulation speedup.

논문에서는 네트워크 통신에서 혼잡으로 인한 패킷의 손실을 최소화하기 위하여 새로운 버퍼 모델인 적응적인 이중 버퍼 모델을 제안한다. 이는 제약된 메모리 환경에서 송수신 버퍼가 서로의 여유 공간을 공유하여 패킷의 손실을 최대한 줄일 수 있는 버퍼 모델이다. 또한 리스트와 비슷한 성능을 지니는 본 버퍼 모델은 자유 리스트를 사용한 버퍼와 달리 메모리 누수로 인한 버블(bubbles) 현상을 방지하므로 제한된 환경의 네트워크 버퍼에 적용할 수 있으며 배열을 사용하는 경우와 비교 할 때 최대 100% 성능 향상을 기대할 수 있다.