• Journal of Conservation Science
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• v.33 no.1
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• pp.17-24
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• 2017

Studies were conducted on manufacturing techniques by applying microscopy and conservation treatments on the annals of Joseon dynasty storage box at the National Museum of Korea. The results revealed that lime tree wood(Tilla spp.) was used to make the annals of Joseon dynasty storage box. Lacquering techniques were used to coat the box with a layer of lacquer and bone ash and then cover it with traditional Korean paper. After being covered with traditional Korean paper, more layers were applied in the following sequence: mud ashes, black lacquer, pure lacquer, and black lacquer. Before conservation treatments, some components and lacquer layers were missing in addition wood joint were loose. Therefore, conservation and restoration should be conducted based on the identified wood and by observing the shape of the remaining components.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.49-54
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• 2024

Data imbalance is a common issue encountered in classification problems, stemming from a significant disparity in the number of samples between classes within the dataset. Such data imbalance typically leads to problems in classification models, including overfitting, underfitting, and misinterpretation of performance metrics. Methods to address this issue include resampling, augmentation, regularization techniques, and adjustment of loss functions. In this paper, we focus on loss function adjustment, particularly comparing the performance of various configurations of loss functions (Cross Entropy, Balanced Cross Entropy, two settings of Focal Loss: 𝛼 = 1 and 𝛼 = Balanced, Asymmetric Loss) on Multi-Class black-box video data with imbalance issues. The comparison is conducted using the I3D, and R3D_18 models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.1
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• pp.142-152
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• 2016

GPS is not able to be used for indoor positioning and currently most of techniques emerging to overcome the limit of GPS utilize private wireless networks. However, these methods require high costs for installation and maintenance, and they are inappropriate to be used in the place where precise positioning is needed as in indoor parking lots. This paper proposes a vehicular indoor positioning method based on QR-code recognition. The method gets an absolute coordinate through QR-code scanning, and obtain the location (an relative coordinate) of a black-box camera using the tilt and roll angle correction through affine transformation, scale transformation, and trigonometric function. Using these information of an absolute coordinate and an relative one, the precise position of a car is estimated. As a result, average error of 13.79cm is achieved and it corresponds to just 27.6% error rate in contrast to 50cm error of the recent technique based on wireless networks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1081-1090
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• 2022

The face recognition model is used for identity recognition of smartphones, providing convenience to many users. As a result, the security review of the DNN model is becoming important, with adversarial attacks present as a well-known vulnerability of the DNN model. Adversarial attacks have evolved to decision-based attack techniques that use only the recognition results of deep learning models to perform attacks. However, existing decision-based attack technique[14] have a problem that requires a large number of queries when generating adversarial examples. In particular, it takes a large number of queries to approximate the gradient. Therefore, in this paper, we propose a method of generating adversarial examples using orthogonal space sampling and dimensionality reduction sampling to avoid wasting queries that are consumed to approximate the gradient of existing decision-based attack technique[14]. Experiments show that our method can reduce the perturbation size of adversarial examples by about 2.4 compared to existing attack technique[14] and increase the attack success rate by 14% compared to existing attack technique[14]. Experimental results demonstrate that the adversarial example generation method proposed in this paper has superior attack performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.133-146
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• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of Software Engineering Society
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• v.13 no.3
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• pp.31-44
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• 2000

소프트웨어를 개발하는데 미리 구현된 블록을 사용하여 소프트웨어 개발비용과 시간을 단축할 수 있다. 이와 같이 미리 구현된 블록을 컴포넌트(Component)라고 하며 컴포넌트를 실행 단위로 개발자에게 인터페이스만을 제공하여 내부 상세한 부분을 숨기므로 쉽고 바르게 대행 어플리케이션을 개발할 수 있다. 개발자는 완전히 내부를 볼 수 없는 블랙 박스(Black Box) 형태의 컴포넌트를 사용한다. 그러나 개발자들은 개발 도메인의 특성에 맞게 속성 및 워크플로우(Workflow)의 변경을 원하기 때문에 커스터마이즈(Customize)할 수 있는 방법이 있어야 한다. 기존의 커스터마이즈 기법은 컴포넌트의 속성을 변경하는 것에 국한되어 있다. 본 논문에서는 비즈니스 측면에서 속성 뿐만이 아니라 컴포넌트 내부의 워크플로우도 변경할 수 있는 기법을 제시한다. 기존에 워크플로우를 변경한다는 것은 컴포넌트 내부를 개발자가 이해하고 코드 수준에서 수정해야 하는 화이트 박스(White Box)이지만, 본 논문에서는 워크플로우의 변경을 화이트 박스가 아니라 블랙 박스 형태로 컴포넌트 인터페이스 만을 이용해 커스터마이즈 할 수 있다. 본 논문에서 제시하는 컴포넌트 커스터마이즈 기법은 특정 비즈니스 측면에서 도메인에 종속적인 특성을 가지며 컴포넌트를 좀더 범용적으로 사용할 수 있는 향상된 커스터마이즈 기법을 제시한다.

• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.104-112
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• 2001

객체지향 프로그램에서 클래스는 수정한 후 재사용하는 화이트박스 재사용과 수정없이 재사용하는 블랙박스 재사용으로 나눌 수 있다. 컴포넌트 기반 소프트웨어 개발 방법론에서의 컴포넌트는 블랙박스 재사용 형태를 띤다. 클래스와 컴포넌트는 절차적인 특성과 객체지향적인 특성을 모두 가지고 있으므로 이를 고려하여 재사용성을 측정해야 한다. 이 논문에서는 클래스와 컴포넌트의 재사용성 측정 모델과 측정 기준을 제안한다. 제안된 모델을 사용하여 측정된 클래스는 화이트박스 재사용이 유리한지 블랙박스 재사용이 유리한지를 판단할 수 있다. 아울러 총평가점수를 산정하여 비교하므로 어느 클래스가 재사용성이 높은지를 알 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.747-753
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• 2018

In the license-plate recognition systems for automobile black Image, the license-plate image frequently has a shadow due to outdoor environments which are frequently changing. Such a shadow makes unpredictable errors in the segmentation process of individual characters and numbers of the license plate image, and reduces the overall recognition rate. In this paper, to improve the recognition rate in these circumstance, a license-plate image binarization algorithm is proposed removing the shadow effectively. The propose algorithm splits the license-plate image into the regions with the shadow and without. To find out the boundary of two regions, the algorithm estimates the curve for shadow boundary using the least-squares method. The simulation is performed for the license-plate image having its shadow, and the results show much higher recognition rate than the previous algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.187-190
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• 2015

주행 중 차선 이탈 경고 시스템은 사고 발생 예방 차원에서 매우 높은 효과가 인정되어서 차선이탈 경고 장치(LDWS) 제품들이 출시되고 있다. 본 논문은 블랙박스의 영상을 이용하여 차선 검출에 정확도를 향상하기 위한 알고리즘을 연구한 것으로 특히 차량에 장착되어 있는 블랙박스 영상을 영상 변환 없이, 실시간 소프트웨어 만 으로 처리할 수 있는 알고리즘을 연구한다. 차선인식을 위한 최적의 영상 ROI를 결정하고, 차선 인식 정확도를 향상하기 위한 전 처리 과정을 적용하고, 동영상의 연속성을 잘못된 차선인식에 대한 보정, 인식이 되지 않는 차선에 대한 후보 차선 추천 알고리즘과 시점 변환에 의한 야간, 곡선 도로에 대한 오인식율을 최소화 하는 방법을 제안한다. 도로주행의 다양한 환경에 대한 실험을 진행했으며, 각각의 방법 적용에 의한 오인식율의 감소와 많은 인식 알고리즘 적용에 의한 처리 속도 저하를 개선하기 위한 연구를 진행했으며, 본 논문은 블랙박스 영상을 이용하여 주행 차선 인식을 위한 최적 알고리즘을 제안한다.