• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.2
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• pp.97-105
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• 2020

In order to set up radar cross section(RCS) reduction factors for a target, the scattering point position of the target should be identified through inverse synthetic aperture radar(ISAR) image analysis. For this purpose, ISAR image focusing is important. Maritime ship is non-linear maneuvering in the sea, however, which blur the ISAR image. To solve this problem, translational and rotational motion compensation are essential to form focused ISAR image. In this paper, hourglass and ISAR image analysis are performed on the collected data in the sea instead of using the prediction software tool, which takes much time and cost to make computer-aided design(CAD) model of the ship.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.151-161
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• 2011

PGA(Phase Gradient Autofocus) is a representative autofocus technique to improve the SAR(Synthetic Aperture Radar) image quality. PGA can estimate high order phase errors and have good robustness in noisy environments. However, PGA is not suitable to apply to the stripmap mode data directly because it is based on the spotlight mode operation. In this paper, the PGA implementation technique for stripmap mode data and the method of ROI(Region of Interest) selection that affects severely on PGA performance have been proposed. The proposed technique was verified by the point target simulation first, and was applied to the real SAR signal data acquired by the flight test. Finally, the significant improvements in focusing quality were shown in the processed SAR images using the proposed method.

• Science of Emotion and Sensibility
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• v.25 no.2
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• pp.55-70
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• 2022

In the 21^st century, the demand for eco-friendly leather, such as eco-leather and vegan leather, is steadily increasing. This study examines the influence of eco-friendliness on consumers' purchasing intentions and the possibility of eco-friendly changes in the fashion accessory market, which is dominated by leather material and leather substitutes. This study administered a questionnaire survey to 227 males and females between 20 and 60 years of age in Korea. With a 5-point Likert scale, data were collected on evaluation criteria when purchasing shoes and bags and purchasing intention of various leather substitute materials according to the democratic variables. The eco-friendliness attitude was divided into eco-consciousness and green behavior. As the eco-friendly attitude increased, most purchasing standards increased, but the purchasing criteria, such as trends, brands, and prices, did not correlate with the eco-friendly attitude. The eco-consciousness of a consumer had a high correlation with the design evaluation criteria, while the green behavior of the consumer aligned with durability and comfort criteria when purchasing a bag. There was a preference for recycled leather, vegetable leather, synthetic leather, and chemical leather, and the fabric type was ranked as natural fiber, biodegradable fiber, and synthetic fiber. Consumers with both green behavior and eco-consciousness are more likely to purchase biodegradable textiles and vegetable leather for the material of shoes and bags.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.776-788
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• 2020

In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.285-293
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• 2024

This study explores the use of Sentinel-1 Synthetic Aperture Radar (SAR), processed through Google Earth Engine (GEE), to monitor changes in the areas of Antarctic ice shelves. Focusing on the Campbell Glacier Tongue (CGT) and Drygalski Ice Tongue (DIT),the research utilizes GEE's cloud computing capabilities to handle and analyze large datasets. The study employs Otsu's method for image binarization to distinguish ice shelves from the ocean and mitigates detection errors by averaging monthly images and extracting main regions. Results indicate that the CGT area decreased by approximately 26% from January 2016 to January 2024, primarily due to calving events,while DIT showed a slight increase overall,with notable reduction in recent years. Validation against Sentinel-2 optical images demonstrates high accuracy,underscoring the effectiveness of SAR and GEE for continuous, long-term monitoring of Antarctic ice shelves.

• Geophysics and Geophysical Exploration
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• v.21 no.3
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• pp.139-149
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• 2018

The resistivity monitoring is a practical method to resolve changes in resistivity of underground structures over time. With the advance of sophisticated automatic data acquisition system and rapid data communication technology, resistivity monitoring has been widely applied to understand spatio-temporal changes of subsurface. In this study, a new 4D inversion algorithm is developed, which can effectively emphasize significant changes of underground resistivity with time. To overcome the overly smoothing problem in 4D inversion, the Lagrangian multipliers in the space-domain and time-domain are determined automatically so that the proportion of the model constraints to the misfit roughness remains constant throughout entire inversion process. Furthermore, a focusing model constraint is added to emphasize significant spatio-temporal changes. The performance of the developed algorithm is demonstrated by the numerical experiments using the synthetic data set for a time-lapse model.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.521-537
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• 2000

Ultrasonic imaging is the most widely used modality among modern imaging device for medical diagnosis and the system performance has been improved dramatically since early 90's due to the rapid advances in DSP performance and VLSI technology that made it possible to employ more sophisticated algorithms. This paper describes "main stream" digital signal processing functions along with the associated implementation considerations in modern medical ultrasound imaging systems. Topics covered include signal processing methods for resolution improvement, ultrasound imaging system architectures, roles and necessity of the applications of DSP and VLSI technology in the development of the medical ultrasound imaging systems, and array signal processing techniques for ultrasound focusing.

• Korean Institute of Interior Design Journal
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• no.28
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• pp.17-24
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• 2001

This study aims at the spatial analysis in semiotic architecture focusing on the early works of M. Graves and P. Eisenman, the representative architects of semiotic architecture. This study on the semiotics of architectural space can be started with literaeur's approaches which recognizes it as the language. Thus, to applicate semiotics in architectural space means a methodology reaching for essential architecture. The conclusions of the study as per the above mentioned aims and intentions are as follows: The viewpoint of spatial analysis in M. Graves's semiotic architecture is defined the semantic metaphorical space of denotative context : That of P. Eisenman's described the synthetic deep of connotative self-restraint.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.283-285
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• 2010

도시 계획의 중요성이 부각되면서 다양한 도시 통합 모델의 개발이 이루어져왔으나 기존 모델들은 거시적 측면의 토지이용의 변화만 다루는 한계점이 있다. 본 논문은 토지이용 변화뿐만 아니라 다양한 사회 경제 지표를 반영하여 미시적인 분석이 가능한 UrbanSim 모델을 사용하여 사례연구를 통한 국내 도입 가능성과 시사점을 도출하고자 하였다. 이를 위해 수치지적도, 건축물 대장, 개별 공시지가 등 다양한 시공간 데이터를 이용하여 $150{\times}150m$ 그리드 셀 기반의 입력 데이터베이스를 구축하고 UrbanSim의 Land Price Model에 적용하였다. 향후 보다 현실적인 모델 수행을 위한 다중 스케일 및 Synthetic 데이터 구축 방안과 접근성 측면의 교통 통합 모델로 확장이 요구된다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.45-52
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• 2015

Although researchers have proposed various recommendation systems, most recommendation approaches are for single users and there are only a small number of recommendation approaches for groups. However, TV programs or movies are most often viewed by groups rather than by single users. Most recommendation approaches for groups assume that single users' profiles are known and that group profiles consist of the single users' profiles. However, because it is difficult to obtain group profiles, researchers have only used synthetic or limited datasets. In this paper, we report on various group recommendation approaches to a real large-scale dataset in a TV domain, and evaluate the various group recommendation approaches. In addition, we provide some guidelines for group recommendation systems, focusing on home group users in a TV domain.