• Smart Media Journal
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• v.12 no.9
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• pp.9-18
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• 2023

Black box cryptography is a cryptographic scheme based on a hardware encryption device, operating under the assumption that the device and the user can be trusted. However, with the increasing use of cryptographic algorithms on unreliable open platforms, the threats to black box cryptography systems have become even more significant. As a consequence, white box cryptography have been proposed to securely operate cryptographic algorithms on open platforms by hiding encryption keys during the encryption process, making it difficult for attackers to extract the keys. However, unlike traditional cryptography, white box-based encryption lacks established specifications, making challenging verify its structural security. To promote the safer utilization of white box cryptography, CHES organizes The WhibOx Contest periodically, which conducts safety analyses of various white box cryptographic techniques. Among these, the Differential Computation Analysis (DCA) attack proposed by Bos in 2016 is widely utilized in safety analyses and represents a powerful attack technique against robust white box block ciphers. Therefore, this paper analyzes the research trends in white box block ciphers and provides a summary of DCA attacks and relevant countermeasures. adhering to the format of a research paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1903-1909
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• 2011

This paper presents the development of a core module for integrating data from vehicle by the convergence technology of mobile telematics and black-box. This emerging technology can be referred to as Black-box in Mobile (BIM). For the development of BIM, sensors and cameras were realized in a driving robot. Relevant hardware implementation was achieved to verify the functionality of BIM. The transmitted signal from the driving robot was confirmed in an Android-based portable device. Existing Black-boxes were mostly developed by major transportation companies and focused only on storing data. The proposed BIM offers not only data storage but also easy-to-use real-time monitoring while in motion. In addition, the vehicle can be monitored on parking through shock sensors. This development is considered commercially viable as it is achieved via software implementation.

• Journal of the Korean Society of Safety
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• v.30 no.3
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• pp.20-25
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• 2015

It is predicted the mass retirement of the post-war generation and the lack of young people according to reduces the recruitment. Therefore, industry fields are concerned by the low level of occupational safety and health from issued problem in a variety of industries; the charge of expanding business range/multi-functional, black box of technology, difficulty of systematic training, relative decrease in the skill of workers, loss of know-how in the field of information followed restricted site information. In response to these problems, it is necessary to establish the long-termly and actively based on for the adoption of a safety and health management techniques utilizing IT, which is digital assistant(tablet PC, PDA, etc.), RFID/USN/ICT, database systems, and etc. In this study, we developed and evaluated a worker's safety sensing apparatus using smart phone. The apparatus may be useful to prevent accidents in the construction industry as well as confined space work.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.665-670
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• 2010

This paper suggests the vehicle remote control on the basis of Smart Phone. In general, most smart phone is mounted with G-sensor to control the motion. G-sensor is able to control several directions and movements of velocity along with X, Y, and Z axis. To access remote location and data system, we can also utilize Wi-Fi communication as well as bluetooth communication. In this study, we propose the scheme that is the car management application by remote control via real-time monitoring on mobile device for user convenience.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.53-58
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• 2023

With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a "black box" model, Shapley values help us to alleviate the "black box" problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses K-nearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.337-339
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• 2015

As much interest recently location based service, a study to analyze the movement patterns of the users from getting a lot of large amounts of data collected from a GPS installed in the smart device. Also it is recorded in the log file in the form of day-to-day personal computer, the development of a variety of a smart phone, a black box, and navigation. This data is collected by the user have been developed a variety of personalized services. In this paper, using the black box camera, such as the vehicle to form a social network platform that broadcasts real-time traffic utilization real-time video information.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.168-174
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• 2017

Videos recorded on surveillance cameras or by car black boxes at night have distorted images due to illumination variation. Therefore, it is difficult to analyze morphological characteristics of objects, and it is limiting to use such distorted images as evidence in traffic accidents. Image restoration is performed by amplifying the brightness of nighttime images using linearized gamma correction to increase their contrast (which destroys visual information) and by minimizing degradation factors caused by irregular traveling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1762-1769
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• 2021

The final goal of this study is to develop a system that can analyze whether a wanted vehicle is a criminal vehicle from images collected from black boxes, smartphones, CCTVs, and so on. Data collection was collected using a self-developed black box. The used data in this study has used a total of 83,753 cases such as the eight vehicle types(truck, RV, passenger car, van, SUV, bus, sports car, electric vehicle) and 434 vehicle models. As a result of vehicle recognition using YOLO v5, mAP was found to be 80%. As a result of identifying the vehicle model with ReXNet using the self-developed black box, the accuracy was found to be 99%. The result was verified by surveying field police officers. These results suggest that improving the accuracy of data labeling helps to improve vehicle recognition performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.28-29
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• 2020

최근 들어 정부의 적극적인 지원책에 힘입어 전통적인 축산농장의 환경을 스마트 축사로 개선하는 사업이 다양하게 추진되고 있다. 이에 축산농장의 스마트화를 위해 다양한 축산용 ICT 기기들이 개발되어 도입되고 있고, 클라우드기반의 인터넷환경까지 연결되고 있으나, 이러한 ICT 기기들을 사용하여 스마트 축사를 구축하고 운영하는데, 편의성 측면에서나 효율성 측면에서 어려움을 겪는 경우가 다수 발생하고 있다. 이 문제를 해결하기 위해, 축산 현장에서 사용자의 편의성 측면을 고려하여 축산현장 정보를 기록하는 스마트 블랙박스 시스템을 개발하고, 효율성을 고려하여 이 시스템을 위한 지능형 시스템 관제 플랫폼을 개발하였다. 그리고 현장상황에서 실증평가를 통해 축산 인들이 현장에서 축산 ICT 기기를 쉽고, 안전하게 운영하도록 하도록 사용자 환경을 구축하였다. 본 논문에서는 개발된 스마트 축산 ICT 블랙박스 시스템(Smart.Dx)과 IoT센서 수집용 게이트웨이(Smart.Dn), 그리고 클라우드 데이터 분석 솔루션(Smart.Center)을 기술한다. 이 연구내용은 또한 축산업에 종사하는 고령자나 스마트폰 환경에 익숙하지 않은 사용자 환경 특성을 고려하여, 유니버셜 디자인의 7대 원칙을 지원하고 있다.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.