• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.05a
• /
• pp.112-115
• /
• 2013

Recently, a great attention has been paid to a car black box device in the automobile markets besides it provides an accident re-construction based on the data which contains audio, video, and some meaningful driving informations. Also, it is expected that the device will get to promote around public transit and the market will greatly grow within a few years. Thus, this research conducted of preference the influencing factors in decisions purchase of auto black box. Factors influencing in decisions purchase of black box was divided safety, functionality, differentiation, economics. Questionnaire survey was conducted to those who worked in black box company. This study suggests practical and theoretical implications of factors influencing in purchase decisions based on the results.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.81-90
• /
• 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

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.369-372
• /
• 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

• Journal of the Korea Society for Simulation
• /
• v.3 no.2
• /
• pp.69-76
• /
• 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.4
• /
• pp.145-151
• /
• 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
• /
• v.4 no.4
• /
• pp.365-379
• /
• 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.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.20 no.1
• /
• pp.31-45
• /
• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.483-486
• /
• 2011

Recently, large and small vehicle accidents due to human life and property due to loss of function similar to that used on the plane with a black box mounted on the vehicle by the driver of the vehicle in order to analyze the cause of the accident vehicle you are using a black box. The black box used in the existing operating system, unlike the Android OS portability is good compared to other OS support an open platform for the development of additional costs or proven, which includes many libraries need to use any external libraries there are no advantages. In addition, the existing black box on the incident can not be sent automatically to report an accident notification has a problem. In this paper, another advantage of the OS used in a black box with an Android-based acceleration sensor on the test board GPS module and smart phones using the information, and incident detection capability to send a message to the specified number of black boxes with was implemented.

• Korean Journal of Optics and Photonics
• /
• v.12 no.3
• /
• pp.205-211
• /
• 2001

We propose a constant inversion black box model of erbium-doped fiber amplifiers (EDFAs) for exact performance predictions for EDFAs operated in the gain-flattened condition. The validity of the newly proposed model was experimentally verified by predicting the performance of EDFAs for the L band, within 1.9% required pump power discrepancy. The role of ion pairing effects on the power conversion efficiency is also discussed. ussed.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.1
• /
• pp.27-35
• /
• 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.