• Fire Science and Engineering
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• v.31 no.3
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• pp.63-72
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• 2017

This study fabricated a low-voltage 10 circuit distribution board based on the KEMC (Korea Electrical Manufacturers Cooperative) 2102-610 standard and performed a characteristics assessment of the developed 10 circuit distribution board to secure product stability. The developed 10 circuit distribution board is designed to have the characteristics of insulation materials, as well as resistance to corrosion ultraviolet radiation and mechanical impact. The developed distribution board is fabricated to have an appropriate protection class of enclosure, electric shock prevention and protection circuits, switchgear and its components, internal electrical circuits and connectors, external conduct terminal, insulation characteristics, temperature rise test, heat resistance, etc. The developed 10 circuit distribution board consists of a single phase circuit and 3-phase circuits. It is possible to measure in real time the leakage current generated from the load distribution line by installing a sensor module at the load side of each of the branched switchgears. In addition, it is possible to increase a circuit according to the use and purpose of the load and to also manage and check the load in real time. Temperature rise tests were performed on the developed 10 circuit distribution board at 18 places including the inlet connection, main circuit and distribution circuit bus bars and bus bar supports, etc. The highest temperature of $65.3^{\circ}C$ was measured at the R-Phase of the connection of the MCCB power supply for the branch circuit bus bar and a temperature rise of $61.6^{\circ}C$ was measured at the T-Phase of the load side. When applying thermal stress to an MCCB for 6 hours at $180^{\circ}C$ using a heat resistant experimental device, it was found that the actuator lever was transformed and moved in the tripped state.

• KSBB Journal
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• v.23 no.3
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• pp.263-270
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• 2008

A poly-(dimethylsiloxane) (PDMS) surface modified by the successive deposition of the polyelectrolytes, poly-(allylamine hydrochloride) (PAH), poly-(diallyldimethylammoniumchloride) (PDAC), poly-(4-ammonium styrenesulfonic acid) (PSS), and poly-(acrylic acid) (PAA), was presented for the application of selective cell immobilization. It is formed via electrostatic attraction between adjacent layers of opposite charge. The modified PDMS surface was examined using static contact angle measurements and fourier transform infrared (FT-IR) spectrophotometer. The wettability of the PDMS surface could be easily controlled and functionalized to be biocompatible through regulation of layer numbers. The modified PDMS surface provides appropriate environment for adhesion to cells, which is essential technology for cell patterning with high yield and viability in the patterning process. This method is reproducible, convenient, and rapid. It could be applied to the fabrication of biological sensing, patterning, microelectronics devices, screening system, and study of cell-surface interaction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.917-924
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• 2015

In this study, we study that the video attributes captured by built-in camera in smart-phone can effect on the quality of PPG signal. The conditions of video attributes were composed of the bitrate, the resolution, the flash. As each condition, we measured a change in the red value of the video image and calculated a PPI(Pulse to Pulse Interval) for extracting the pulse wave signal. 20 subjects participated in the experiment and this experiment was carried out 18 tasks. The PPG signal was measured simultaneously for two minutes with the PPG sensor in the middle finger and Smart-phone in the forefinger of the right hand. By proceeding the correlation analysis, we obtained the highest correlation condition(83%, p=0.01), which the resolution was $640{\times}480$, bitrate was 5000kbps, flash was on. As a result, this study will be a useful guide for quality of signals in the pulse signal measurement system using built-in camera in smart-phone.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.179-197
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• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.19-27
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• 2014

Kompsat-3 is an optical high-resolution earth observation satellite launched in May 2012. In addition to its 0.7m spatial resolution, Kompsat-3 is capable of in-track stereo acquisition enabling quality Digital Elevation Model(DEM) generation. Typical DEM generation procedure requires accurate control points well-distributed over the entire image region. But we often face difficult situations especially when the area of interests is oversea or inaccessible area. One solution to this is to use existing geospatial data even though they only cover a part of the image. This paper aimed to assess accuracy of DEM from Kompsat-3 with different scenarios including no control point, Rational Polynomial Coefficients(RPC) relative adjustment, and RPC adjustment with control points. Experiments were carried out for Kompsat-3 stereo data in USA. We used Digital Orthophoto Quadrangle(DOQ) and Shuttle Radar Topography Mission(SRTM) as control points sources. The generated DEMs are compared to a LiDAR DEM for accuracy assessment. The test results showed that the relative RPC adjustment significantly improved DEM accuracy without any control point. And comparable DEM could be derived from single control point from DOQ and SRTM, showing 7 meters of mean elevation error.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.275-283
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• 2013

The patient radiation dose is different depending on selection of Ion chamber when taking Chest PA which using AEC. In this paper, we studied acquiring the best diagnostic images according to selection of Ion chamber on AEC mode as well as minimizing patient radiation dose. Experimental methods were selection of Ion chamber and change of sensitivity under the same conditions as Chest PA projection. At AEC mode, two upper ion chambers sensors and one lower ion chamber sensor were divided into 7 cases according to selection of on/off. after measuring five times respectively, we obtained average value and calculated exposure dose. Image assessment was done with measured Modulation Transfer Function, Peak Signal to Noise Ratio, Root Mean Square, Signal to Noise Ratio, Contrast to Noise Ratio, Mean to Standard deviation Ratio respectively. In exposure assessment results, selection of two upper chambers was the lowest. In resolution assessment results, image of two upper chambers had the second high spatial frequency at sensitivity at 625(High) was 1.343 lp/mm. RMS value of image selecting two upper chambers was low secondly. SNR, CNR, MSR were the high value secondly. As the sensitivity was increased, radiation dose was decreased but better image could be obtained on image quality. In order to obtain the best medical images while minimizing the dose, usage of two upper ion chambers is considered to be clinically useful at sensitivity 625(High).

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.117-125
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• 2015

In this paper, we propose a classification algorithm based on the obtained EEG(Electroencephalogram) signal for the control of 'left' and 'right' turnings of which a driving system composed of EEG sensor, Labview, DAQ, Matlab and driving robot. The proposed algorithm uses features extracted from frequency band information obtained by DWT (Discrete Wavelet Transform) and selects features of high discrimination by using Fisher score. We, also propose the number of feature vectors for the best classification performance by using SVM(Support Vector Machine) classifier and propose a decision pending algorithm based on MLD (Maximum Likelihood Decision) to prevent malfunction due to misclassification. The selected four feature vectors for the proposed algorithm are the mean of absolute value of voltage and the standard deviation of d5(2-4Hz) and d2(16-32Hz) frequency bands of P8 channel according to the international standard electrode placement method. By using the SVM classifier, we obtained 98.75% accuracy and 1.25% error rate. Also, when we specify error probability of 70% for decision pending, we obtained 95.63% accuracy and 0% error rate by using the proposed decision pending algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.6
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• pp.134-142
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• 2012

This paper presents a study of system identification on the tilt response of the L-type retaining wall located at Tanhyun 11th ACE Apartment, Ilsan in order to understand mechanism how the structure behaves in operational conditions and to provide a reference tilt values for assessing structural abnormality. The retaining wall was extraordinarily tall (14m) in urban area so the long-term monitoring system had been installed with 3 tilts-meters and 9 temperature sensors operational from Oct 2004 upto Nov 2007. By using 5-months continuous data in which all the 12 channels were up and running, the two prediction models, 1) the linear model, and 2) the state-space equation (SSE) model, have been identified by finding the best fitness model among all possible 511 combinations of input temperatures out of the 9 temperatures. The linear model which was simple in the model structure achieved the validation fittness of 68% due to the fact that the static model wasn't able to represent thermal dynamics. The SSE model achieved the validation fitness of 90% which was quite accurate considering various unexpected noises happening in field measurements.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.