• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.952-954
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• 2013

모델 기반 평가 방법은 블랙박스 테스트의 한 종류로 평가 항목의 생성과 평가 항목의 실행이 모델 기반 평가 도구를 통하여 자동으로 이루어지는 평가 방법이다. 자동차 지능형 헤드램프의 AFLS/ADB를 대상으로 무작위 평가 생성 기법과 T-method 평가 생성 기법을 이용 하여 평가 항목을 생성하고 비교해 보았으며 Vector CANoe를 사용하여 시뮬레이션을 구성하고 CAPL을 이용하여 스크립트를 작성하고 평가하여 나온 평가 보고서를 확인 하였다.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.91-99
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• 2018

Discrete event simulation environments often need to be modified because additional questions to systems may become apparent while observing the simulation results repeatedly. It leads to increasing development budget and depreciating the effectiveness of the environment. To avoid the modifications and to generate the altered results, this paper applies an Event Control Model (ECM) with control functions that modulate, delete and generate the events at the simulation time. In addition, this paper suggests an easier approach for domain-users, who do not want to program at source code level, by using ECM templates. The simulators with the ECMs can have better extensibility because it becomes more adaptable to possibly unanticipated changes. It prevents increasing development costs due to modifications or development of new models by M&S experts, and it provides a new alternative step to domain users. To support the effectiveness of this approach, this paper describes a relevant example, which is composed of an initial simulation model based on our empirical studies. It will show that there exist the uncountable benefits because the existing simulator is reused by domain users without new projects.

• Journal of the Korea Computer Graphics Society
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• v.21 no.5
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• pp.11-19
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• 2015

Our research introduces a novel system for creating 3D vehicle animation. Our system is for intuitively authoring vehicle accident scenes according to videos or based on user-drawn trajectories. Our system has been implemented by combining three existing ideas. The first part is for obtaining 3D trajectory of a vehicle from black-box videos. The second part is a tracking algorithm that controls a vehicle to follow a given trajectory with small errors. The last part optimizes the vehicle control parameters so that the error between the input trajectory and simulated vehicle trajectory is minimized. We also simulate the deformation of the car due to an impact to achieve believable results in real-time.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.315-318
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• 2007

랜덤 테스트(RT)는 가능한 입력 도메인에서 임의의 입력 값을 선택하여 테스트 케이스를 생성하고 테스트를 수행하는 기본적인 블랙 박스 테스트 기법이다. 랜덤 테스트의 성능을 향상 시키기 위해서 오류 패턴을 고려한 다양한 Adaptive Random Testing (ART) 알고리즘들이 제안되어 왔다. 그 중 Distance-Based ART (D-ART), Restricted Random Testing (RRT)이 좋은 성능을 보이고 있지만, 수행시간이 너무 느리다는 단점이 있어, 이를 대체할 수 있는 여러 ART 방법들이 제안되고 있다. 그 중, Adaptive Random Testing through Iterative Partitioning (IP-ART)가 가장 좋은 성능과 빠른 수행시간을 보인다. 본 논문에서는 IP-ART 의 성능을 더욱 향상시킬 수 있는 방법을 제안하고, 시뮬레이션을 통하여 향상된 성능을 평가해 보았다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.1-8
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• 2016

We propose an adaptive histogram stretching algorithm for application to a car's personal recorder. The algorithm was used for pre-processing to detect the license plate region in an image from a personal recorder. The algorithm employs a Probability Density Function (PDF) and Cumulative Distribution Function (CDF) to analyze the distribution diagram of the images. These two functions are calculated using an image obtained by sampling at a certain pixel interval. The images were subjected to different levels of stretching, and experiments were done on the images to extract their characteristics. The results show that the proposed algorithm provides less deterioration than conventional algorithms. Moreover, contrast is enhanced according to the characteristics of the image. The algorithm could provide better performance than existing algorithms in applications for detecting search regions for license plates.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.664-670
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• 2018

In modern society, CCTV, which is the eye of surveillance, is being used to collect image data in various ways in daily life. CCTV is used not only for security, surveillance, and crime prevention but also in many fields such as automobile and black box. In this paper, we have developed a STM32F407 ARM chip based camera system for various applications. In order to develop camera system, modeling of camera system based on 3D structure was carried out in SolidWorks environment. The PCB board design was developed to extract the PCB parts from the camera system modeling files into iges files, convert them from the Altium Designer tool into 3D and 2D boards, After designing the camera system circuit and PCB, we have been studying the implementation of the stable system by using TRM (Thermal Risk Management) tool to cope with the heat simulation generated on the board.

• The KIPS Transactions:PartB
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• v.8B no.3
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• pp.231-236
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• 2001

일반적인 다층 신경망에서 가중치의 갱신 알고리즘으로 사용하는 오류 역전과 방식은 가중치 갱신 결과를 고정된(fixed) 한 개의 값으로 결정한다. 이는 여러 갱신의 가능성을 오직 한 개의 값으로 고정하기 때문에 다양한 가능성들을 모두 수용하지 못하는 면이 있다. 하지만 모든 가능성을 확률적 분포로 표현하는 갱신 알고리즘을 도입하면 이런 문제는 해결된다. 이러한 알고리즘을 사용한 베이지안 신경망 모형(Bayesian Neural Networks Models)은 주어진 입력값(Input)에 대해 블랙 박스(Black-Box)와같은 신경망 구조의 각 층(Layer)을 거친 출력값(Out put)을 계산한다. 이 때 주어진 입력 데이터에 대한 결과의 예측값은 사후분포(posterior distribution)의 기댓값(mean)에 의해 계산할 수 있다. 주어진 사전분포(prior distribution)와 학습데이터에 의한 우도함수(likelihood functions)에 의해 계산한 사후확률의 함수는 매우 복잡한 구조를 가짐으로 기댓값의 적분계산에 대한 어려움이 발생한다. 따라서 수치해석적인 방법보다는 확률적 추정에 의한 근사 방법인 몬테 칼로 시뮬레이션을 이용할 수 있다. 이러한 방법으로서 Hybrid Monte Carlo 알고리즘은 좋은 결과를 제공하여준다(Neal 1996). 본 논문에서는 Hybrid Monte Carlo 알고리즘을 적용한 신경망이 기존의 CHAID, CART 그리고 QUEST와 같은 여러 가지 분류 알고리즘에 비해서 우수한 결과를 제공하는 것을 나타내고 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.