• 한국전자파학회논문지
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• 제18권12호
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• pp.1345-1351
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• 2007

본 논문에서는 편파화 정도(Degree of Polarization: DoP)와 동일 편파 위상차(Co-polarized Phase-Difference: CPD)를 이용한 SAR 영상 분류법을 제안한다. 우선, 측정된 stokes 산란 operator로부터 DoP와 CPD를 얻는 계산식을 유도하고, SAR 영상 분류 과정을 설명한다. 다음에는 측정에서 얻은 완전 편파 L밴드 SAR 영상 데이터에 분류법을 적용하여 그 정확성을 검증하고, 예외 경우를 검토한다. 마지막으로 제안된 분류법으로 SAR 영상을 크게 4가지 그룹인 맨땅, 낮은 식물, 높은 식물, 주거 지역(마을)으로 분류한 결과를 보인다.

• 대한조선학회 특별논문집
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• 대한조선학회 2005년도 특별논문집
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• pp.15-21
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• 2005

In this paper, the procedure of generation and application of nonlinear wave loads for structural design of large container carrier was described. Ship motion and wave load was calculated by modified strip method. Pressure acting on wetted hull surface was calculated taking into account of relative hull motion to the wave. Design wave height was determined based on the most sensitive wave length considering rule vertical wave bending moment at head sea or fellowing sea condition. And the enforced heeling angie concept which was introduced by Germanischer Lloyd (GL) classification had been used to simulate high torsional moment in way of fore hold parts similar to actual sea going condition. Using wave load generated from this dynamic load calculation, FE analyses were performed. With this result, yielding, buckling, hatch diagonal deflection and fatigue strength of hatch corners were reviewed based on the requirement of GL classification. The results of FE analysis show good compatibility with GL classification.

• 한국군사과학기술학회지
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• 제22권2호
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• pp.170-178
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• 2019

Classification of radar signals in the field of electronic warfare is a problem of discriminating threat types by analyzing enemy threat radar signals such as aircraft, radar, and missile received through electronic warfare equipment. Recent radar systems have adopted a variety of modulation schemes that are different from those used in conventional systems, and are often difficult to analyze using existing algorithms. Also, it is necessary to design a robust algorithm for the signal received in the real environment due to the environmental influence and the measurement error due to the characteristics of the hardware. In this paper, we propose a radar signal classification method which are not affected by radar signal modulation methods and noise generation by using deep learning techniques.

• Journal of information and communication convergence engineering
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• 제22권2호
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• pp.165-171
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• 2024

In this study, we present a novel approach for enhancing chest X-ray image classification (normal, Covid-19, edema, mass nodules, and pneumothorax) by combining contrastive learning and machine learning algorithms. A vast amount of unlabeled data was leveraged to learn representations so that data efficiency is improved as a means of addressing the limited availability of labeled data in X-ray images. Our approach involves training classification algorithms using the extracted features from a linear fine-tuned Momentum Contrast (MoCo) model. The MoCo architecture with a Resnet34, Resnet50, or Resnet101 backbone is trained to learn features from unlabeled data. Instead of only fine-tuning the linear classifier layer on the MoCopretrained model, we propose training nonlinear classifiers as substitutes for softmax in deep networks. The empirical results show that while the linear fine-tuned ImageNet-pretrained models achieved the highest accuracy of only 82.9% and the linear fine-tuned MoCo-pretrained models an increased highest accuracy of 84.8%, our proposed method offered a significant improvement and achieved the highest accuracy of 87.9%.

• Journal of Advanced Research in Ocean Engineering
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• 제2권4호
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• pp.202-208
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• 2016

In this paper, we propose novel seabed sediment classification algorithm using feature obtained by continuous wavelet transform (CWT). Contrast to previous researches using direct reflection coefficient of seabed which is function of frequency and is highly influenced by sediment types, we develop an algorithm using both direct reflection signal and backscattering signal. In order to obtain feature vector, we employ CWT of the signal and obtain histograms extracted from local binary patterns of the scalogram. The proposed algorithm also adopts principal component analysis (PCA) to reduce dimension of the feature vector so that it requires low computational cost to classify seabed sediment. For training and classification, we adopts K-means clustering algorithm which can be done with low computational cost and does not require prior information of the sediment. To verify the proposed algorithm, we obtain field data measured at near Jeju island and show that the proposed classification algorithm has reliable discrimination performance by comparing the classification results with actual physical properties of the sediments.

• 한국군사과학기술학회지
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• 제27권3호
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• pp.319-328
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• 2024

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

• 대한원격탐사학회지
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• 제24권5호
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• pp.517-524
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• 2008

In order to classify an satellite imagery into geospatial features of interest, the supervised classification needs to be trained to distinguish these features through training sampling. However, even though an imagery is classified, different results of classification could be generated according to operator's experience and expertise in training process. Users who practically exploit an classification result to their applications need the research accomplishment for the consistent result as well as the accuracy improvement. The experiment includes the classification results for training process used VITD polygons as a prior probability and training parameter, instead of manual sampling. As results, classification accuracy using VITD polygons as prior probabilities shows the highest results in several methods. The training using unsupervised classification with VITD have produced similar classification results as manual training and/or with prior probability.

• 대한임베디드공학회논문지
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• 제12권5호
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• pp.277-286
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• 2017

This paper introduces mathematical modelling and control algorithm of expendable mine disposal vehicle. This vehicle has two longitudinal thrusters, one vertical thruster and internal mass moving system which can control pitch rate. Also, the vehicle has an optical camera and forward looking sonar for underwater mine detection and classification. The vehicle is controlled via an optical cable connected with operating console on the mother ship. We describe the vehicle's 6DOF dynamic model and controller which can track the desired trajectory for the way-point tracking. These simulation results shows guidance and maneuvering performance which has other sensor data or not.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.189-196
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• 2024

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1464-1466
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• 2003

classification of the satellite images provides information about land cover and/or land use. Quality of the classification result depends mainly on the spatial and spectral resolutions of the images. In this study, image fusion in terms of resolution merging, and band integration with multi-source of the satellite images; Landsat ETM+ and Ikonos were carried out to improve classification. Resolution merging and band integration could generate imagery of high resolution with more spectral bands. Precise image co-registration is required to remove geometric distortion between different sources of images. Combination of unsupervised and supervised classification of the fused imagery was implemented to improve classification. 3D display of the results was possible by combining DEM with the classification result so that interpretability could be improved.