• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10d
• /
• pp.556-558
• /
• 2002

항공사진에 포함된 기점 마크의 방사 및 기하 특성을 이용하여 마크의 중심 위치를 자동으로 인식하기 위한 방안을 제안한다. 마크를 포함하는 배경 영역의 방사 특성에 기반을 푼 전략에 근거하여 입력된 영상을 이치화한 다음 형태 연산자를 적용시켜 기전 마크가 있는 후보 영역을 추출한다. 기하 특성에 기반을 둔 전략에 근거하여 ▽$^2$G 필터링과 대칭성 강조 필터링을 적용시킨 후, 대칭이 가장 강하게 나타나는 위치인 마크의 중심 위치를 구한다. 66매의 기점 마크 영상에 대한 평가 결과 중심 위치가 1 화소의 정확도까지 얻어질 수 있다는 것을 확인할 수 있었다.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.11
• /
• pp.625-631
• /
• 2000

In designing fuzzy model, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious-and-error process. This paper presents an approach to automatic design of optimal fuzzy rule bases for modeling using evolutionary programming. Evolutionary programming evolves simultaneously the structure and the parameter of fuzzy rule base a given task. To check the effectiveness of the suggested approach, 3 examples for modeling are examined, and the performance of the identified models are demonstrated.

• Proceedings of the IEEK Conference
• /
• 2002.06d
• /
• pp.283-286
• /
• 2002

Most of the Automatic Fingerprint Identification Systems define the ridge endings and bifurcation points as the minutia for matching. Therefore, the precise extraction of the minutia is critical in raising the efficiency and reliability of the system. The fingerprint images produced through the preprocessing may have the false minutia happened over the process and they can be the factors to decrease the system efficiency This paper suggests the algorithm, which removes the false minutia after extracting the candidate minutia from the thinned binary images of fingerprint images.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2014.10a
• /
• pp.176-178
• /
• 2014

해상 교통 관제 시스템을 구축하기 위한 센서 장치 중, 자동식별장치(AIS, Automatic Identification System)는 핵심적인 역할을 수행하는 센서 장치다. AIS가 제공하는 메시지 중 국제해사기구(IMO, International Maritime Organization) Circ.289 권고안의 6번 및 8번 메시지를 통한 ASM (Application Specific Message)은 선박위치송출이라는 AIS 고유 목적을 넘어 AIS 활용 범위를 확대시킨 좋은 예이다. 이에 본 논문에서는 AIS-ASM 서비스를 분석하여 해상 안전 분야에 적용할 수 있는 방안을 제시하고, 더 나아가 일부 AIS-ASM 서비스의 개선 방안을 소개하고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1181-1186
• /
• 1993

In the paper, a new design method of rule-based fuzzy modeling is proposed for model identification of nonlinear systems. The structure indentification is carried out, utilizing fuzzy c-means clustering. Fuzzy-neural networks composed back-propagation algorithm and linear fuzzy inference method, are used to identify parameters of the premise and consequence parts. To obtain optimal linguistic fuzzy implication rules, the learning rates and momentum coefficients are tuned automatically using a modified complex method.

• Journal of Korea Society of Industrial Information Systems
• /
• v.2 no.1
• /
• pp.73-87
• /
• 1997

Multilayer neural network using a modified beackpropagation learning algorithm was introduced to achieve automatic identification of different types of aircraft in a variety of 3-D orientations. A 3-D shape of an aircraft can be described by a library of 2-D images corresponding to the projected views of an aircraft. From each 2-D binary aircraft image we extracted 2-D invariant (L, Φ) feature vector to be used for training neural network aircraft classifier. Simulations concerning the neural network classification rate was compared using nearest-neighbor classfier (NNC) which has been widely served as a performance benchmark. And we also introduced reliability measure of the designed neural network classifier.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2003.11a
• /
• pp.147-151
• /
• 2003

Yield enhancement in semiconductor fabrication is important. Even though DRAM yield loss may be attributed to many problems, the existence of defects on the wafer is one of the main causes. When the defects on the wafer form patterns, it is usually an indication for the identification of equipment problems or process variations. In this paper describes the techniques to automatically classify a failure pattern using a fail bit map.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.4
• /
• pp.407-413
• /
• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• Journal of Korean Society of Forest Science
• /
• v.110 no.2
• /
• pp.155-164
• /
• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.46 no.2
• /
• pp.31-37
• /
• 2009

This paper presents an efficient frame synchronization strategy with the identification of modulation type for Digital Video Broadcasting-Satellite second generation (DVB-S2). To detect the Start Of Frame (SOF) and identify a modulation mode at low SNR, we propose a new correlator structure and a low complexity Automatic Gain Controller (AGC). The proposed frame synchronization architecture can reduce about 93% multipliers and 89% adders compared with the direct implementation of the Differential - Generalized Post Detection Integration (D-GPDI) algorithm which is very complex and the proposed a low complexity AGC consists of only 5 multipliers and 3 adders. The proposed architecture has been thoroughly verified on the Xilinx Virtex II FPGA board.