• Journal of the Society of Naval Architects of Korea
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• v.47 no.2
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• pp.178-187
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• 2010

The performance of the waterjet system of a patrol boat has been experimentally studied. A waterjet propulsion system has many advantages comparing with a conventional screw propeller especially for high speed craft because of its good cavitation performance. This paper describes experimental procedure and analysis method of self-propulsion tests with a 1/12-scale model. Experimental results were analyzed according to ITTC 96 standard method. The full-scale effective power and delivered power of the ship were also analyzed and the full-scale speed predicted from the model test compares reasonably with the measured full-scale results of the sea trial.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.1-7
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• 2016

ADAS (Advanced Driver Assistance Systems) requires not only real-time robust lane detection, both straight and curved, but also predicting upcoming steering direction by detecting the curvature of lanes. In this paper, a curvature lane detection algorithm is proposed to enhance the accuracy and detection rate based on using inverse perspective images and Gaussian Mixture Model (GMM) to segment the lanes from the background under various illumination condition. To increase the speed and accuracy of the lane detection, this paper used template matching, RANSAC and proposed post processing method. Through experiments, it is validated that the proposed algorithm can detect both straight and curved lanes as well as predicting the upcoming direction with 92.95% of detection accuracy and 50fps speed.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.715-725
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• 2011

Objective: This study is aimed at developing Articulated Human Models(AHM) using superquadrics to improve the geometric accuracy of the body shape. Background: The previous work presents the AHM with geometrical simplification such as ellipsoids to improve analysis efficiency. However, because of the simplicity, their physical properties such as a center of mass and moment of inertia are computed with errors compared to their actual values. Method: This paper introduces a three steps method to present the AHM with superquadrics. First, a 3D whole body scan data are divided into 17 body segments according to body joints. Second, superquadric fitting is employed to minimize the Euclidean distance between body segments and superquadrics. Finally, Fee-Form Deformation is used to improve accuracy over superquadric fitting. Results: Our computational experiment shows that the superquadric models give better accuracy of dynamic analysis than that of ellipsoid ones. Conclusion: We generate the AHM composed of 17 superquadrics and 16 joints using superquadric fitting. Application: The AHM using superquadrics can be used as the base model for dynamics and ergonomics applications with better accuracy because it presents the human motion effectively.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.2
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• pp.215-223
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• 2012

A violet smoke hand grenade(KM18) is used to generate signals. The grenade is considered to fail when its smoke emission time is longer than the specified one so that its smoke concentration becomes lighter. Accelerated degradation test for the grenade was performed, and then failure in smoke emission time was reproduced from the test. Stress for the degradation test was selected as temperature/humidity from the pre-test results. Degraded data of emission time from the accelerated test were analyzed through applying a distibution-based degradation model. Then, Peck Model was applied to predict the storage life under field conditions. In addition, the predicted storage life was compared with that of ASRP(Ammunition Stockpile Reliability Program).

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.417-425
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• 2020

Multi-resolution modeling(MRM) is required when simulating objects in variable resolution and can be applied for interoperating systems, which simulate objects in fixed resolution. However, most interoperation middleware do not support MRM, so participating models must handle several issues to simulate MRM system. In this paper, we propose an interoperation system, which is composed of several different resolution models, based on the High Level Architecture and Run-Time Infrastructure(HLA/RTI). In the proposed architecture, each model participates to a HLA federation through MRM adapter application, which supports data resolution conversion and HLA services while communicating with the model. MRM adapter application can be implemented based on an MRM adapter, and an adapter application development tool is proposed to support developing the application. Using the tool, developers can easily implement data resolution conversion component plugged-in HLA adapter. A case study is implemented in the proposed MRM system, and shows that models of different resolution works successfully with dynamic resolution changes.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.1-7
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• 2014

This paper presents an analytical method for finding the optimum transmission range in mobile ad hoc networks(MANETs). The results are particularly useful for the operation of military networks, as the transmission range affects the throughput, delay, and battery consumption. Plus, the proposed method allows the optimum transmission range to be determined in advance when deploying combatants with mobile terminals. And we analyze the battery life-time and the optimum transmission range under various propagation scenarios based on Hata propagation model. The proposed method obtains the optimum transmission range in a MANET based on the operational conditions.

• Annual Conference of KIPS
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• 2019.10a
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• pp.697-700
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• 2019

무장데이터링크 시뮬레이션은 네트워크 기반 유도무기 모델링을 위하여 M&S 기술을 바탕으로 무장데이터링크의 운용성 및 정밀타격 성능을 검증하기 위한 시뮬레이션 소프트웨어다. 네트워크 기반 유도무기 모델은 원격임무통제를 위한 무장데이터링크망과 가상전장을 위한 시뮬레이션망이 연동하는 분산네트워크 환경에서 동작한다. 이때 유도탄모델 인터페이스는 다수의 프로토콜과 종속관계를 갖게 된다. 따라서 프로토콜이 수정될 때마다 유도탄모델 뿐만 아니라, 해당 인터페이스를 사용하는 다른 프로토콜도 수정되어야 한다. 또한 시뮬레이션 특성상 다양한 운용개념이 유도탄모델에 적용될 수 있다. 기존 고정표적 유도탄모델에 임무통제기능을 적용할 경우, 기존 모델이 훼손될 뿐만 아니라 기능 추가 및 삭제가 쉽지 않다는 문제가 있다. 본 논문은 서로 다른 프로토콜을 유도탄모델에 쉽게 적용하고 변경할 수 있는 프로토콜 변경용이성과 기 개발된 고정표적 유도탄모델을 변경하지 않고 무장데이터링크 운용 개념을 적용할 수 있는 유도탄모델 확장성을 위한 구조를 제안한다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.813-822
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• 2008

The present work has been developed the interpretation processor including analysis of the failure stress in pyrotechnically releasable mechanical linking device, which has the release characteristic without fragmentation and pyro-shock, using SoildWorks, COSMOS Works and ANSYS programs. The aim of the invention is to propose a pyrotechnically releasable mechanical linking device for two mechanical elements that does not suffer from such drawbacks. The pyrotechnically releasable mechanical linking device according to the invention is simple, compact and inexpensive in structure. It is simple to implement and permit the use of only a reduced quantity of pyrotechnic composition, such composition possibly being devoid of any primary explosive at all. The present work is only focused on the design of structure and the material characteristics. To analyze the fracture morphology resulted from tensile test in the different ball type bolts, the present work has been performed to estimate the failure stress of material and to make the same result from tensile test. The failure stress of SUS 630 in ductile material is approximately 1050 Mpa. The failure stress of SUS 420 in brittle material is about 1790 Mpa. Among the models used the ductile material, the model 6 is suitable a design of structure compared to that of other models. The use of this interpretation processor developed the present work could be extensively helped to estimate the failure stress of material having a complex geometry such as the ball type bolt

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2674-2697
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• 2019

Identity authentication is a crucial line of defense for network security, and passwords are still the mainstream of identity authentication. So far trawling password attacking has been extensively studied, but the research related with personal information is always sporadic. Probabilistic context-free grammar (PCFG) and Markov chain-based models perform greatly well in trawling guessing. In this paper we propose a systematic targeted attacking model based on structure partition and string reorganization by migrating the above two models to targeted attacking, denoted as TG-SPSR. In structure partition phase, besides dividing passwords to basic structure similar to PCFG, we additionally define a trajectory-based keyboard pattern in the basic grammar and introduce index bits to accurately characterize the position of special characters. Moreover, we also construct a BiLSTM recurrent neural network classifier to characterize the behavior of password reuse and modification after defining nine kinds of modification rules. Extensive experimental results indicate that in online attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 275%, and respectively outperforms its foremost counterparts, Personal-PCFG, TarGuess-I, by about 70% and 19%; In offline attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 90%, outperforms Personal-PCFG and TarGuess-I by 85% and 30%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4285-4299
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• 2019

Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.