• Journal of Korean Society for Atmospheric Environment
• /
• v.22 no.E2
• /
• pp.57-68
• /
• 2006

Four intensive seasonal sampling campaigns between October 1998 and October 1999 were undertaken at an urban site of Chongju, in which polyurethane foam (PUF) sampler was used to collect particulate- and vapor-phase polycyclic aromatic hydrocarbons (PAHs). The contribution to total (particulate+vapor) PAH concentration by the vapor phase component exceeded the particulate phase contribution by factor of ${\sim}2.6$. Summed concentrations of phenanthrene (30.9%), pyrene (16.6%), naphthalene (11.3%) and fluoranthene (11.0%) account for significant amounts of the vapor-phase, while chrysene (12.5%), benzo[b]fluoranthene (11.6%), indeno[123-cd]pyrene (9.9%), benzo[ghi]perylene (9.5%), benzo[k]fluoranthene (9.4%), pyrene (8.9%), and benzo[a]pyrene (8.3%) are found to be the most common PAH compounds in the particulate phase. The results from application of principal component analysis to particulate-phase PAH data demonstrate that a combination of PAH and $PM_{2.5}$ inorganic data is a more powerful tracer of emission sources than PAH species data alone. Particulate-phase PAH species were found to be associated predominantly with emissions from diesel engine vehicles and incineration.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.27 no.6
• /
• pp.343-349
• /
• 2014

It is summarized that potential causes of performance degradations and failure mechanisms of crystalline silicon photovoltaic (PV) modules installed in Middle East area. In addition, we also reviewed current PV module qualification test (IEC 61215) and the methods for detection of wear-out fault. The failure of PV modules in the extreme environmental conditions such as deserts is mainly due to high temperature, humidity, and dust storms. In particular, cementation phenomenon caused by combination of sand and moisture leads to rapid degradation in the performance of PV modules. In order to evaluate and guarantee the long term reliability of PV modules, specific qualification tests such as sand dust test, salt mist test and potential induce degradation test considering operating environment of PV module should be carried out.

• Journal of Korean Society of Steel Construction
• /
• v.15 no.1
• /
• pp.41-50
• /
• 2003

Nondestructive Damage Evaluation (NDE) techniques yield the damage location and its size from the modal characteristics of pre-damaged and post-damaged structures. To predict the system reliability of the aging structure, results from the NDE are integrated into the element/component failure probabilities. The element/component failure probabilities can be calculated from failure functions for each element/component with the aid of techniques from a structural reliability analysis. In this paper, a method to estimate the system reliability of a structure that is based on the reliabilities of elements/components in a given structure is presented. The efficacy of the combination of the nondestructive damage detection and the structural reliability evaluation is demonstrated using pre-damaged and post-damaged modal data obtained from numerical simulations of a rigid frame.

• Computers and Concrete
• /
• v.10 no.6
• /
• pp.557-573
• /
• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.

• The KIPS Transactions:PartD
• /
• v.12D no.2 s.98
• /
• pp.267-274
• /
• 2005

New game softwares usually have much similarities with old one in the sense of properties and procedures. But nevertheless, the development could be duplicated several times without referencing or reusing of others. In addition, because there is no standardized process about the game engine, the products generated by other software development processes are difficult to understand and to reuse. Therefore, the enterprise developing new game software newly analyze and design although it is same process as the old one. This paper proposes the improved process of the game engine, analysis of structures and relations, classification of the class and the module and their combination methods, implementation of storage, and processor model to apply the component based development method to the game engine.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.175-186
• /
• 2004

We present a hybrid approach of texture-based method and connected component (CC)-based method for text extraction in complex images. Two primary methods, which are mainly utilized in this area, are sequentially merged for compensating for their weak points. An automatically constructed MLP-based texture classifier can increase recall rates for complex images with small amount of user intervention and without explicit feature extraction. CC-based filtering based on the shape information using NMF enhances the precision rate without affecting overall performance. As a result, a combination of texture and CC-based methods leads to not only robust but also efficient text extraction. We also enhance the processing speed by adopting appropriate region marking methods for each input image category.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.9
• /
• pp.621-628
• /
• 2013

This paper presents a framework for context-aware smart phone applications based on Android. The mobile context-aware system is composed of a low level context collection module, a high level context generation module and a service provision module. Existing android system cannot provide an appropriate application framework to integrate these independent modules. In this paper, we provide an application framework which make each module a component, and provide appropriate services to each component. This framework hides the Android platform, so that the complexity for organical combination can be minimized and the application developers can make the mobile context-aware applications easily.

• The KIPS Transactions:PartB
• /
• v.16B no.6
• /
• pp.443-450
• /
• 2009

This paper develops a novel technique of face detection using brightness distribution in the surrounding area of eye. The proposed face detection consists of facial component candidate extraction, facial component candidate filtering through eye-lip combination, left/right eye classification using brightness distribution, face verification confirming edges in nose region. Because the proposed technique don't use any skin color, it can detect multiple faces in color images with complicated backgrounds and different illumination levels. The experimental results reveal that the proposed technique is better than the traditional techniques in terms of detection ratio.

• Journal of international Conference on Electrical Machines and Systems
• /
• v.2 no.3
• /
• pp.275-282
• /
• 2013

Applications such as vibration isolation, gravity compensation, pick-and-place machines, etc., would benefit from (long-stroke) cylindrical/tubular permanent magnet (PM) actuators with integrated passive gravity compensation to minimize the power consumption. As an example, in component placing (pick-and-place) machines on printed circuit boards, passive devices allow the powerless counteraction of translator including nozzles or tooling bits. In these applications, an increasing demand is arising for high-speed actuation with high precision and bandwidth capability mainly due to the placement head being at the foundation of the motion chain, hence, a large mass of this device will result in high force/power requirements for the driving mechanism (i.e. an H-bridge with three linear permanent magnet motors placed in an H-configuration). This paper investigates a tubular actuator topology combined with passive gravity compensation. These two functionalities are separately introduced, where the combination is verified using comprehensive three dimensional (3D) finite element analyses.

• Journal of Pharmaceutical Investigation
• /
• v.37 no.6
• /
• pp.377-395
• /
• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.