• Journal of Multimedia Information System
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• v.8 no.4
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.174-177
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• 1995

Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.591-600
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• 2018

Recent SAR systems provide fully polarimetric SAR data, which is known to be useful in a variety of applications such as disaster monitoring, target recognition, and land cover classification. The objective of this study is to evaluate the performance of polarization SAR data for landslide detection. The detectability of different SAR parameters was investigated based on the supervised classification approach. The classifier used in this study is the Adaptive Boosting algorithms. A fully polarimetric L-band PALSAR-2 data was used to examine landslides caused by the 2016 Kumamoto earthquake in Kyushu, Japan. Experimental results show that fully polarimetric features from the target decomposition technique can provide improved detectability of landslide site with significant reduction of false alarms as compared with the single polarimetric observables.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.149-150
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• 2008

Generally, the propeller shaft rate (PSR) estimation algorithm for the classification of the sonar target has the following problems: it requires both accurate and efficient the fundamental finding method because it is essential and difficult to distinguish harmonic family from the frequency spectrum, and further, it requires an easy design procedure in terms of its structures and parameters. To solve these problems, an evolutionary PSR estimation algorithm using an expert knowledge and the evolution strategy, is proposed. Simulation results show that the proposed algorithm effectively solves the problems in the realtime system application.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.3
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• pp.217-224
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• 2017

In this paper, we propose a fusion classification method combining HRRP and JEM classifier with complementary properties for the classification of aircraft. The fusion method is based on the confidence of a classifier for a classification result to improve performance compared with single classifier in various situations. The confidence is defined as the posterior probability estimated from the classification performance of a classifier and it depends on the aspect angle and the certainty for a classification result. Through the classification test using simulation data, we can verify that the proposed fusion method shows good performance by fusing the classifiers effectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1227-1236
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• 2021

Adversarial attack, one of the attacks on deep learning classification model, is attack that add indistinguishable perturbations to input data and cause deep learning classification model to misclassify the input data. There are various adversarial attack algorithms. Accordingly, many studies have been conducted to detect adversarial attack but few studies have been conducted to classify what adversarial attack algorithms to generate adversarial input. if adversarial attacks can be classified, more robust deep learning classification model can be established by analyzing differences between attacks. In this paper, we proposed a model that detects and classifies adversarial attacks by constructing a random forest classification model with input features extracted from a target deep learning model. In feature extraction, feature is extracted from a output value of hidden layer based on class predicted by the target deep learning model. Through Experiments the model proposed has shown 3.02% accuracy on clean data, 0.80% accuracy on adversarial data higher than the result of pre-existing studies and classify new adversarial attack that was not classified in pre-existing studies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.7
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• pp.601-608
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• 2021

Launch Acceptability Region of Air-to-Surface Guided Bomb is defined as the set of release points in order to reach a target successfully. LAR is consisted of fixed target area and moving target area whether the target maneuvers or not. In this paper, the computational algorithm of LAR is studied for fixed and moving target. First, multi-simulations are performed varying platform, target, and atmospheric environment to attain Min/Max DB. Second, the LAR functions are obtained using regression and classification algorithm. For operational suitability, the algorithm for display of LAR is studied to obtain appropriate LAR. In this progress, the results of LAR are suitable to apply air-to-ground guided bomb for moving target.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.294-303
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• 2008

ISAR(Inverse Synthetic Aperture Radar) images represent the 2-D(two-dimensional) spatial distribution of RCS (Radar Cross Section) of an object, and they can be applied to the problem of target identification. A traditional approach to ISAR imaging is to use a 2-D IFFT(Inverse Fast Fourier Transform). However, the 2-D IFFT results in low resolution ISAR images especially when the measured frequency bandwidth and angular region are limited. In order to improve the resolution capability of the Fourier transform, various high-resolution spectral estimation approaches have been applied to obtain ISAR images, such as AR(Auto Regressive), MUSIC(Multiple Signal Classification) or Modified MUSIC algorithms. In this study, these high-resolution spectral estimators as well as 2-D IFFT approach are combined with a recently developed ISAR image classification algorithm, and their performances are carefully analyzed and compared in the framework of radar target recognition.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.220-225
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• 2011

Based on the supply status statistics of new renewable energy according to public obligation system, current status of overall application centered on solar heat, solar ray, and geothermal heat as energy sources that can be applied to buildings may be analyzed as follows. (1) After the public obligation system, the investment costs on the total construction costs by years were between 5.21% and 7.12%: they were 7.12% in 2004, where the system was initially implemented; and they were gradually declined from 2005 to 2011, 5.76% in average. The ratio of equipment investment per energy sources in the total construction costs was 5.9%, which was slightly more than the obliged ratio. The order of investment costs per energy source was solar ray, geothermal heat, and solar heat. (2) Among the 1,433 sites in the plan of new renewable energy installation based on the public obligation system, "for cultural & social use" was most in target institution, and facilities for education & research was most in use classification, followed by public working, culture & rally, and sports. The number of facilities applied according to the case for planning installation per use classification of the target institution was between 1.1 and 1.5, or 1.4 in average of energy source. Conclusively, the authors of this study investigated overall current status of new renewable energy supply from the analysis of statistic data, and it may be needed of further supplementation of various examinations by visiting investigation and interviews with practitioners based on classification of use of target institutions.