• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05c
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• pp.17-20
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• 2004

PV model is presented based on the shockley diode equation. The simple model has a photo-current source, an single diode junction and a series resistance and includes temperature dependences. An accurate PV module electrical model is presented, matching with boost converter MPPT strategy and demosntarted in Matlab for a typical general purpose solar cell. Given solar insolation and temperature, the model returns current vector and MPP.

• Proceedings of the KIPE Conference
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• 2010.11a
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• pp.341-342
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• 2010

본 논문에서는 종전의 3상 H-브리지 PWM 인버터와 다른 토폴로지를 갖는 3상 벅-부스트 DC-AC 인버터를 위한 제어 기법을 제안하였다. 이를 위하여 공간벡터 변조 방식(SVM : Space Vector Modulation) 및 PI 제어기를 벅-부스터 DC-AC 인버터에 적용하고 이를 PSIM 시뮬레이션 통하여 타당성을 검증하였다.

• Journal of Platform Technology
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• v.6 no.4
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.146-150
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• 2001

A new three-phase soft-switching active front-end inverter is presented. The topology consists of a quasi-resonant PWM boost converter with an additional resonant branch, which provides low loss at high frequency operation. This leads to a high conversion efficiency and a remarkable reduction in the size of the input inductor. To synchronise the PWM pattern with the resonance cycle, a modified space vector modulation with asymmetrical PWM pattern is used. A high power factor can be achieved for both power flow directions. Due to a new control strategy the converter features a low content of harmonics in the line currents even for distorted line voltages.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.408-416
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• 2017

This paper proposes a real-time side-rear vehicle detection algorithm that detects vehicles quickly and accurately in blind spot areas when driving. The proposed algorithm uses a cascade classifier created by AdaBoost Learning using the MCT (modified census transformation) feature vector. Using this classifier, the smaller the detection window, the faster the processing speed of the MCT classifier, and the larger the detection window, the greater the accuracy of the MCT classifier. By considering these characteristics, the proposed algorithm uses two classifiers with different detection window sizes. The first classifier quickly generates candidates with a small detection window. The second classifier accurately verifies the generated candidates with a large detection window. Furthermore, the vehicle classifier and the wheel classifier are simultaneously used to effectively detect a vehicle entering the blind spot area, along with an adjacent vehicle in the blind spot area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.485-494
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• 2011

In this paper, novel sub-space interference alignment algorithms are proposed to boost the capacity in multi-cell environment. In the case of conventional sub-space alignment, arbitrary reference vectors have been adopted as transmitting vectors at the transmitter side, and the inter-cell interference among users are eliminated by using orthogonal vectors of the chosen reference vectors at the receiver side. However, in this case, sum-rate varies using different reference vectors even though the channel values keep constant, and vice versa. Therefore, the relationship between reference vectors and channel values are analyzed in this paper, and novel interference alignment algorithms are proposed to increase multi-cell capacity. Reference vectors with similar magnitude are adopted in the proposed algorithm. Simulation results show that the proposed algorithms provide about 50 % higher sum-rate than conventional algorithm.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.69-77
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• 2017

S The need for an automated geo-positioning approach for near real-time results and to boost cost-effectiveness has become increasingly urgent. Following this trend, a new approach to automatically compensate for the bias of the rational function model (RFM) was proposed. The core idea of this approach is to remove the bias of RFM only using tie points, which are corrected by matching with the digital elevation model (DEM) without any additional ground control points (GCPs). However, there has to be a additional evaluation according to the quality of DEM because DEM is used as a core element in this approach. To address this issue, this paper compared the quality effects of DEM in the conduct of the this approach using the Shuttle Radar Topographic Mission (SRTM) DEM with the spatial resolution of 90m. and the National Geographic Information Institute (NGII) DEM with the spatial resolution of 5m. One KOMPSAT-2 stereo-pair image acquired at Busan, Korea was used as experimental data. The accuracy was compared to 29 check points acquired by GPS surveying. After bias-compensation using the two DEMs, the Root Mean Square (RMS) errors were less than 6 m in all coordinate components. When SRTM DEM was used, the RMSE vector was about 11.2m. On the other hand, when NGII DEM was used, the RMSE vector was about 7.8 m. The experimental results showed that automated geo-positioning approach can be accomplished more effectively by using NGII DEM with higher resolution than SRTM DEM.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.19-26
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• 2014

An ensemble classifier system is a widely-used multi-classifier system, which combines the results from each classifier and, as a result, achieves better classification result than any single classifier used. Several methods have been used to build an ensemble classifier including boosting, which is a cascade method where misclassified examples in previous stage are used to boost the performance in current stage. Boosting is, however, a serial method which does not form a complete feedback loop. In this paper, proposed is context sensitive SVM ensemble (CASE) which adopts SVM, one of the best classifiers in term of classification rate, as a basic classifier and clustering method to divide feature space into contexts. As CASE divides feature space and trains SVMs simultaneously, the result from one component can be applied to the other and CASE achieves better result than boosting. Experimental results prove the usefulness of the proposed method.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.63-70
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• 2009

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.