• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1383-1392
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• 2019

Adversarial attack, which geneartes adversarial data to make target model misclassify the input data, is able to confuse real life applications of classification models and cause severe damage to the classification system. An Black-box adversarial attack learns a substitute model, which have similar decision boundary to the target model, and then generates adversarial data with the substitute model. Jacobian-based data augmentation is used to synthesize the training data to learn substitutes, but has a drawback that the data synthesized by the augmentation get distorted more and more as the training loop proceeds. We suggest data augmentation with 'decay factor' to alleviate this problem. The result shows that attack success rate of our method is higher(around 8.5%) than the existing method.

• 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 Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.145-150
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• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• Land and Housing Review
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• v.14 no.1
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• pp.67-82
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• 2023

Using the Artificial Neural Network model and Binary Logistic Regression model, this study investigates influence factors on the quality of life in terms of housing environment during the COVID-19 in Seoul. The results show that the lower the satisfaction level of housing policy, the lower the quality of life in the employment field and the lower the quality of residential field. On the other hand, permanent workers and self-employed respondents have experienced improvement in residential quality during the pandemic. A limitation of this study is associated with disentangling the causal relationship using the 'black box' characteristics of ANN method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.113-118
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• 2023

Recently, as more and more cyclists ride bicycles for their health and more people commute by bicycle, the number of cyclists has increased. However, as the number of users increases, many accidents occur, and the handling of bicycle accidents is unstable. It is inadequate to prepare for accidents in other ways except for safety equipment. Therefore, there is a need for a safe and convenient way for modern adults to ride. Unlike other apps, in this study, by adding a safety function, you can shoot a black box while riding, and a function to inform you that it is an accident-prone area is implemented. In addition, a function that can detect an accident using the Android built-in sensor and automatically make emergency contact is added. Cyclists can secure safety and convenience in one app without the need to use additional apps. Furthermore it develops an app system that allows you to talk about riding and share your route through the Riding Community bulletin board.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.3
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• pp.119-128
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• 2014

One of the theoretically and empirically grounded black boxes in HRD and firm performance link is employee' attitudes such as organizational commitment and job satisfaction. However, most studies were conducted with the regression analysis at the organizational level. This study used HLM(hierarchical linear modeling) analysis, which made it possible to estimate more accurate relationship between variables that were measured from two different levels. In addition, this study attempted to open an the black box(learning culture) in the relationship between HRD and employee attitudes. The result showed that the HRD have a positive effect on the organizational commitment and the job satisfaction. Also the HRD showed full mediation effect of organization commitment and the job satisfaction on the Learning culture. And the result showed that the HRD in 2007 have a positive effect on employee' attitudes in 2009. These findings concluded that systematic HRD like employee's education and training must be built and also the positive culture for employee's learning like support of management's learning organization must be improved in order to promote the organizational performance(organizational commitment, job satisfaction) in company.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.115-121
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• 2015

In this paper, we proposed an iterative data-flow optimal scheduling algorithm based on genetic algorithm for high-performance multiprocessor. The basic hardware model can be extended to include detailed features of the multiprocessor architecture. This is illustrated by implementing a hardware model that requires routing the data transfers over a communication network with a limited capacity. The scheduling method consists of three layers. In the top layer a genetic algorithm takes care of the optimization. It generates different permutations of operations, that are passed on to the middle layer. The global scheduling makes the main scheduling decisions based on a permutation of operations. Details of the hardware model are not considered in this layer. This is done in the bottom layer by the black-box scheduling. It completes the scheduling of an operation and ensures that the detailed hardware model is obeyed. Both scheduling method can insert cycles in the schedule to ensure that a valid schedule is always found quickly. In order to test the performance of the scheduling method, the results of benchmark of the five filters show that the scheduling method is able to find good quality schedules in reasonable time.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.937-950
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• 2004

With growing population of internet and web applications, distributed environment is considered to be the standard architecture of application. A network management systems(NMS) is necessary to control and monitor the complex network resources for providing and sharing the heft quality service. We recognize the NMS as a standard infrastructure for supporting efficient networking and a separate commercial applications. We believe every resource including software, hardware and environment for the network management should be separated from special protocols, vendors and applications. Therefore, We need a standard network management system that is efficient and consistent because of the heterogeous network features. In regards to software development, software reuse through assembling and extending the reusable elements such as patterns and components assures to realize the best productivity and quality The component based development(CBD) methodology that can assemble black box though well defined interfaces makes it possible to develop easer and quicker applications and is proved as the best software development solution involved in construction, selection and assembly of components. In this thesis, we describe the architecture for the network management and identify, define and design the components through analysis and design in the network management domain and Identified components mapped to the component architecture. We also specify the component development and design and implement the component for developing the network management. Implemented components apply to the component repository system that register, retrieve and understand the components. We analyze, design and implement the entire network management system based on configuration, connection, performance and fault management through the pre-developed components.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.13-25
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• 1998

It is necessary to develop methodologies for the application of artificial neural network into hydrologic rainfall-runoff process, although there is so much applicability by using the functions of associative memory based on recognition for the relationships between causes and effects and the excellent fitting capacity for the nonlinear phenomenon. In this study, some problems are presented in the application procedures of artificial neural networks and the simulation of runoff hydrograph experiences are reviewed with nonlinear functional approximator by artificial neural network for rainfall-runoff relationships in a watershed. which is regarded as hydrdologic black box model. The neural network models are constructed by organizing input and output patterns with the deserved rainfall and runoff data in Pyoungchang river basin under the assumption that the rainfall data is the input pattern and runoff hydrograph is the output patterns. Analyzed with the results. it is possible to simulate the runoff hydrograph with processing element of artificial neural network with any hydrologic concepts and the weight among processing elements are well-adapted as model parameters with the assumed model structure during learning process. Based upon these results. it is expected that neural network theory can be utilized as an efficient approach to simulate runoff hydrograph and identify the relationship between rainfall and runoff as hydrosystems which is necessary to develop and manage water resources.