• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.6
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• pp.80-88
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• 2008

In this paper, the block cipher algorithms, 3-DES(Triple Data Encryption Standard), AES(Advanced Encryption Standard), SEED, HASH(SHA-1), which are domestic and international standards, have been implemented as an integrated cryptographic engine for smart card applications. For small area and low power design which are essential requirements for portable devices, arithmetic resources are shared for iteration steps in each algorithm, and a two-level clock gating technique was used to reduce the dynamic power consumption. The integrated cryptographic engine was verified with ALTERA Excalbur EPXA10F1020C device, requiring 7,729 LEs(Logic Elements) and 512 Bytes ROM, and its maximum clock speed was 24.83 MHz. When designed by using Samsung 0.18 um STD130 standard cell library, the engine consisted of 44,452 gates and had up to 50 MHz operation clock speed. It was estimated to consume 2.96 mW, 3.03 mW, 2.63 mW, 7.06 mW power at 3-DES, AES, SEED, SHA-1 modes respectively when operating at 25 MHz clock. We found that it has better area-power optimized structure than other existing designs for smart cards and various embedded security systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.29-32
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• 2005

3차원 비디오는 차세대 정보 통신 서비스 분야의 하나로, 사용자에게 시각적으로 고차원적인 서비스를 제공하는 것을 목적으로 한다. 이 가운데 다시점 비디오는 같은 시간, 여러 시점에서 영상 정보를 획득하여 사용자에게 원하는 시점의 영상 정보를 제공하는 3차원 비디오이며, 현재 방송 관련 연구 기관에서 차세대 실감방송 멀티미디어 서비스 개발을 목적으로 하는 연구가 활발히 진행되고 있다. 최근 MPEG 표준화 그룹에서는 다시점 비디오 부호화 (multi-view video coding, MVC) 방법에 관한 표준화 작업이 진행 중이며, 최신 비디오 압축 표준인 H.264를 이용한 여러 가지 방법들이 제안되었다. 현재 MVC 표준화 작업의 평가 기준이 되는 방법은 각 시점을 H.264로 부호화하는 방법인데, 이는 다시점 비디오 영상의 중요한 특성인 인접시점들 사이의 공간적 상관도를 전혀 고려하지 않았다. 본 논문에서는 시점 보간법을 이용하여 얻어진 중간 영상과 원영상과의 차분 영상을 부호화하는 알고리즘을 제안하고자 한다. 여기서 시점 보간법이란 좌우 두 시점 영상으로부터 변이값을 얻은 다음, 이를 이용하여 중간 시점 영상을 합성하는 방법을 말한다. 예를 들면,다시점 비디오의 홀수 번째 시점의 영상은 기존의 방법을 따르고, 짝수 번째 시점의 영상은 이미 부호화된 홀수 번째 시점의 영상을 이용하여 보간적으로 예측하여 원래 영상과 차분 영상을 구하여 부호화한다. 차분 영상은 영상의 복잡도가 많이 감소되어 원영상에 비해 보다 나은 부호화 효율을 보인다. 그러나 합성 영상이 각 장면마다 독립적으로 생성되므로 원영상에 비해 차분 영상의 시간적인 상관도가 줄어들어 I장면의 경우 부호화 효율이 크게 향상되었으나, 시간적인 상관도를 이용하는 P장면과 B장면에서는 오히려 좋지 않은 결과를 보였다. 통계는 전 국민에 대한 패널자료이기 때문에 통계적 활용의 범위가 방대하다. 특히 개인, 가구, 사업체 등 사회 활동의 주체들이 어떻게 변화하는지를 추적할 수 있는 자료를 생산함으로써 다양한 인과적 통계분석을 할 수 있다. 행정자료를 활용한 인구센서스의 이러한 특징은 국가의 교육정책, 노동정책, 복지정책 등 다양한 정책을 정확한 자료를 근거로 수립할 수 있는 기반을 제공한다(Gaasemyr, 1999). 이와 더불어 행정자료 기반의 인구센서스는 비용이 적게 드는 장점이 있다. 예를 들어 덴마크나 핀란드에서는 조사로 자료를 생산하던 때의 1/20 정도 비용으로 행정자료로 인구센서스의 모든 자료를 생산하고 있다. 특히, 최근 모든 행정자료들이 정보통신기술에 의해 데이터베이스 형태로 바뀌고, 인터넷을 근간으로 한 컴퓨터네트워크가 발달함에 따라 각 부처별로 행정을 위해 축적한 자료를 정보통신기술로 연계${cdot}$통합하면 막대한 조사비용을 들이지 않더라도 인구센서스자료를 적은 비용으로 생산할 수 있는 근간이 마련되었다. 이렇듯 행정자료 기반의 인구센서스가 많은 장점을 가졌지만, 그렇다고 모든 국가가 당장 행정자료로 인구센서스를 대체할 수 있는 것은 아니다. 행정자료로 인구센서스통계를 생산하기 위해서는 각 행정부서별로 사용하는 행정자료들을 연계${cdot}$통합할 수 있도록 국가사회전반에 걸쳐 행정 체제가 갖추어져야 하기 때문이다. 특히 모든 국민 개개인에 관한 기본정보, 개인들이 거주하며 생활하는 단위인 개별 주거단위에 관한 정보가 행정부에 등록되어 있고, 잘 정비되어 있어야 하며, 정보의 형태 또한 서로 연계가 가능하도록 표준화되어있어야 한다. 이와 더불어, 현재 인구센서스에서 표본조사를 통해 부가적으로 생산하는 경제활동통계를 생산하기 위해서는 개인이

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.4
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• pp.285-293
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• 2020

In this paper, to improve Synthetic Aperture Radar (SAR) image quality, the effect of time synchronization error in the EGI/IMU (Embedded GPS/INS, Inertial Measurement Unit) integrated system is analyzed and state augmentation is applied to compensate it. EGI/IMU integrated system is widely used as a SAR motion measurement algorithm, which consists of EGI mounted to obtain the trajectory and IMU mounted on the SAR antenna. In an EGI/IMU integrated system, a time synchronization error occurs when the clocks of the sensors are not synchronized. Analysis of the effect of time synchronization error on navigation solutions and SAR images confirmed that the time synchronization error deteriorates SAR image quality. The state augmentation is applied to compensate for this and as a result, the SAR image quality does not decrease. In addition, by analyzing the performance and the observability of the time synchronization error according to the maneuver, it was confirmed that the time-variant maneuver such as rotational motion is necessary to estimate the time synchronization error adequately. In order to reduce the influence of the time synchronization error on the SAR image, the time synchronization error must be compensated by performing maneuver changing over time such as a rotation before SAR operation.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.25-34
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• 2019

VTON (Virtual try-on) is a key technology that can activate the online commerce of fashion items. However, the early 3D graphics-based methods require the 3D information of the clothing or the human body, which is difficult to secure realistically. In order to overcome this problem, Image-based deep-learning algorithms such as VITON (Virtual image try-on) and CP-VTON (Characteristic preserving-virtual try-on) has been published, but only a sampled results on performance is presented. In order to examine the strength and weakness for their commercialization, the performance analysis is needed according to the complexity of the clothes, the object posture and body shape, and the degree of occlusion of the clothes. In this paper, IoU and SSIM were evaluated for the performance of transformation and synthesis stages, together with non-DL SCM based method. As a result, CP-VTON shows the best performance, but its performance varies significantly according to posture and complexity of clothes. The reasons for this were attributed to the limitations of secondary geometric deformation and the limitations of the synthesis technology through GAN.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.4
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• pp.55-61
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• 2024

Recently, the number of aging concrete structures is steadily increasing. This is because many of these structures are reaching their expected lifespan. Such structures require accurate inspections and persistent maintenance. Otherwise, their original functions and performance may degrade, potentially leading to safety accidents. Therefore, research on objective inspection technologies using deep learning and computer vision is actively being conducted. High-resolution images can accurately observe not only micro cracks but also spalling and exposed rebar, and deep learning enables automated detection. High detection performance in deep learning is only guaranteed with diverse and numerous training datasets. However, surface damage to concrete is not commonly captured in images, resulting in a lack of training data. To overcome this limitation, this study proposed a method for generating concrete surface damage images, including cracks, spalling, and exposed rebar, using stable diffusion. This method synthesizes new damage images by paired text and image data. For this purpose, a training dataset of 678 images was secured, and fine-tuning was performed through low-rank adaptation. The quality of the generated images was compared according to three base models of stable diffusion. As a result, a method to synthesize the most diverse and high-quality concrete damage images was developed. This research is expected to address the issue of data scarcity and contribute to improving the accuracy of deep learning-based damage detection algorithms in the future.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.1-7
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• 2024

In this paper, we proposes a Convolutional Neural Networks(CNN) equipped with Batch Normalization(BN) for handwritten digit recognition training the MNIST dataset. Aiming to surpass the performance of LeNet-5 by LeCun et al., a 6-layer neural network was designed. The proposed model processes 28×28 pixel images through convolution, Max Pooling, and Fully connected layers, with the batch normalization to improve learning stability and performance. The experiment utilized 60,000 training images and 10,000 test images, applying the Momentum optimization algorithm. The model configuration used 30 filters with a 5×5 filter size, padding 0, stride 1, and ReLU as activation function. The training process was set with a mini-batch size of 100, 20 epochs in total, and a learning rate of 0.1. As a result, the proposed model achieved a test accuracy of 99.22%, surpassing LeNet-5's 99.05%, and recorded an F1-score of 0.9919, demonstrating the model's performance. Moreover, the 6-layer model proposed in this paper emphasizes model efficiency with a simpler structure compared to LeCun et al.'s LeNet-5 (7-layer model) and the model proposed by Ji, Chun and Kim (10-layer model). The results of this study show potential for application in real industrial applications such as AI vision inspection systems. It is expected to be effectively applied in smart factories, particularly in determining the defective status of parts.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.6
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• pp.633-641
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• 2015

Several complex processes that are employed in industries, such as shipping, power plants, and the petrochemical industry, involve time-varying behavior as well as strong nonlinear behavior during operation. The fixed-parameter proportional-integral-derivative (PID) controllers have difficulty in dealing with control problems that occur in such processes. In this paper, we propose a method of designing a nonlinear PID controller for industrial processes that exhibit a large number of nonlinearities and time-varying behavior. The gains of the nonlinear PID controller are characterized by a simple nonlinear function of the error and/or error rate depending on the process set-point and output. We tune the user-defined parameters using a genetic algorithm by minimizing the integral of time absolute error (ITAE) index. We verify the effectiveness of the proposed method by performing a comparison of the proposed method and two other nonlinear and adaptive methods that are employed for reference tracking, disturbance-rejection performances, and robustness to parameter changes on a continuously stirred tank reactor.