• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.112-115
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• 2013

차량용 블랙박스는 음성과 영상 그리고 자동차의 다양한 정보를 저장하는 매체이며 이러한 정보를 바탕으로 사고의 재구성이 가능해 최근 자동차 시장에서 주목을 받고 있다. 또한 대중교통 중심으로 블랙박스의 장착이 확산되면서 수년 내에 시장은 더욱 커질 전망이다. 본 연구는 차량용 블랙박스 구매결정에 영향을 미치는 요인의 대한 선호도를 분석하였다. 블랙박스 구매결정 요인을 안전성, 기능성, 차별성, 경제성으로 구분하였다. 실증적 분석을 위해 블랙박스 관련회사 직원을 대상으로 설문조사를 실시하여 수집된 설문지를 분석하였다. 분석결과를 바탕으로 블랙박스 구매결정과 관련된 실무적 의의를 논의하였다.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.81-90
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• 2022

There are several ways to improve software performance by using existing software. So, the developments of some programs are the most promising ways. However, traditional part programming studies usually assume that the components are recycled "as is". Existing models of component objects only provide limited support for partial adjustments, namely white box technologies ( copy-paste & inheritance) and the black-box methods (such as mixing and encapsulation). These technologies have problems related to recovery, efficiency, implementation of indirect costs, or their own problems. This paper suggests as JALTREE, The Black Box adaptation technology, which allows us for the implementation of previous components, but we need configurable the interface types, for measuring the adaptability. In this article we discussed the types of adjustments including component interfaces and component composition. An example of customizing JALTREE and component can be illustrated in several examples

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.369-372
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• 2007

about commercial business of car black box, mirror vision for car black box deal with analysis of Existing Car Black boxes in market to provide the objective information associated with surrounding scene of car instead of witness, we experimented on suitable structure of all direction to cover surrounding of car considering dead zone where can't see at short distance and realized simple structure of gathering scene using mirror and lens and by saving the number of camera and MUX of pre-circut

• 한국시뮬레이션학회논문지
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• 제3권2호
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• pp.69-76
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• 1994

Traditionally, simulation-based learning games which are known as flight-simulators have been constructed as a black-box game. Within a black-box game, game-players can view and modify only a part of model parameters. Game-players cannot change the structure of a simulation model. In a black-box game, game-players cannot understand and learn the system structure which is responsible for the system behavior. In this paper, the multi-tasking at the level of operating systems is exploited to enhance the transparency of simulation-based learning game. The white-box game or transparent-box game allows game-players ot view and modify the model structure. The multi-tasking solution for white-box learning game is implemented with Smalltalk language on MS-/windows operating system.

• 대한임베디드공학회논문지
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• 제16권4호
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• pp.145-151
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• 2021

In this paper, we propose a novel visualization technique to explain the predictions of deep neural networks. We use knowledge distillation (KD) to identify the interior of a black-box model for which we know only inputs and outputs. The information of the black box model will be transferred to a white box model that we aim to create through the KD. The white box model will learn the representation of the black-box model. Second, the white-box model generates attention maps for each of its layers using Grad-CAM. Then we combine the attention maps of different layers using the pixel-wise summation to generate a final saliency map that contains information from all layers of the model. The experiments show that the proposed technique found important layers and explained which part of the input is important. Saliency maps generated by the proposed technique performed better than those of Grad-CAM in deletion game.

• Structural Monitoring and Maintenance
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• 제4권4호
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• pp.365-379
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• 2017

This paper presents a comparison of the black-box and the physics based derived gray-box models for subspace identification for structures subjected to support-excitation. The study compares the damage detection capabilities of both these methods for linear time invariant (LTI) systems as well as linear time-varying (LTV) systems by extending the gray-box model for time-varying systems using short-time windows. The numerically simulated IASC-ASCE Phase-I benchmark building has been used to compare the two methods for different damage scenarios. The efficacy of the two methods for the identification of stiffness parameters has been studied in the presence of different levels of sensor noise to simulate on-field conditions. The proposed extension of the gray-box model for LTV systems has been shown to outperform the black-box model in capturing the variation in stiffness parameters for the benchmark building.

• 디지털산업정보학회논문지
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• 제20권1호
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• pp.31-45
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• 2024

The purpose of this study is to explain employment factors in the software field. For it, the Graduates Occupational Mobility Survey by the Korea employment information service is used. This paper proposes employment models in the software field using machine learning. Then, it explains employment factors of the models using explainable artificial intelligence. The models focus on both university graduates and vocational college graduates. Our works explain and interpret both black box model and glass box model. The SHAP and EBM explanation are used to interpret black box model and glass box model, respectively. The results describes that positive employment impact factors are major, vocational education and training, employment preparation setting semester, and intern experience in the employment models. This study provides a job preparation guide to universitiy and vocational college students that want to work in software field.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.483-486
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• 2011

최근 크고 작은 차량사고로 인한 인명, 재산 손실 때문에 비행기에서 사용되고 있는 것과 유사한 기능을 가진 블랙박스를 차량에 장착시켜 운전자들은 차량사고의 원인을 분석하기 위해 차량용 블랙 박스를 사용하고 있다. 기존의 블랙박스들에서 사용된 운영체제와는 달리 안드로이드 OS는 다른 OS들에 비해 이식성이 좋으며 개방 플랫폼을 지원해서 개발 시 추가적인 비용이 들지 않으며 검증된 많은 라이브러리들을 포함하고 있어 별도의 외부 라이브러리를 사용할 필요가 없다는 장점이 있다. 또한 기존의 블랙박스들은 사고 발생 시 자동으로 사고 알림 신고를 보내지 못하는 문제점이 있다. 본 논문에서는 기존의 블랙박스에서 사용된 OS와는 다른 장점이 있는 안드로이드 기반의 테스트 보드에 GPS 모듈과 가속도 센서 정보를 이용하고 스마트 폰에서 사고 감지 시 지정된 번호로 메시지를 전송하는 기능을 가진 블랙박스를 구현하였다.

• 한국광학회지
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• 제12권3호
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• pp.205-211
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• 2001

본 논문에서는 최근 주목받고 있는 장파장 EDFA(Erbium-Doped Fiber Amplifer)에서의 성능 분석, 특히 전력변환률(Power Conversion Efficiency)의 예측을 위한 밀도반전이 고정된 상태에서의 black box 모델을 유도하고 이득이 평탄화된 장파장 EDFA의 요구되는 펌프파워측면에서 수치해석과 실험을 통하여 유도된 모델의 유용성을 증명하였다. 모델을 통한 펌프파워의 예측치는 실험오차 범위내에서 1.9%이내의 정확성을 보였다. 밀도반전이 고정된 상태에서의 black box 모델은 일반적으로 980nm, 1480m, 펌핑의 경우 모두 적용이 가능하며 EDF의 3가지 이상의 길이에 대하여 입력 펌프파워와 입력/출력 신호파워의 관계를 측정하여 밀도반전이 일정한 상태에서 다른 동작점의 성능을 예측할 수 있었다. 또한 단파장 EDFA에서는 손실의 효과가 크게 나타나지 않아 해석적인 해와 일치하는 결과가 알려져 있었으나 이에 비해 장파장 EDFA에서는 여러 가지 손실의 효과가 더욱 크게 작용하게 되며 특히 paired ion의 효과로 인한 손실로써 비선형적인 관계식이 유도됨을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권1호
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• pp.27-35
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• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.