• Journal of Korean Society of Environmental Engineers
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• v.37 no.5
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• pp.262-268
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• 2015

This study established an analytical method to simultaneously determine six organophosphorous pesticides [methyldemetone-S, diazinon, fenitrothion, parathion, phentoate, and O-ethyl O-(4-nitrophenyl) phenylphosphonothioate (EPN)] and carbaryl in water using a gas chromatography/mass spectrometry (GC/MS) system coupled with on-line micro extraction by packed sorbent (MEPS) and programmed temperature vaporizer (PTV) injector. Polystyrene divinylbenzene (PDVB) was used as a sorbent of MEPS. The effects of elution solvents, pH, elution volume and draw-eject cycles of samples on sample pretreatment process were investigated. Also, quality assurance and quality control (QA/QC) and the recovery of the pesticides in environmental samples were evaluated. The elution was performed using $30{\mu}L$ of a mixed solvent (acetone : dichloromethane = 80 : 20 (v/v)). Sample pretreatment processes were optimized with seven cycles of draw-eject of sample (1 mL) spiking an internal standard and sulfuric acid. At lower pH, the analytical sensitivity of diazinon decreased, but that of carbaryl increased. The method detection limit and the limit of quantification for this method were 0.02~0.18 and $0.08{\sim}0.59{\mu}g/L$, respectively. The method precision and accuracy were 1.5~11.5% and 83.3~129.8%, respectively, at concentrations of $0.5{\sim}5.0{\mu}g/L$. The recovery rates for all the pesticides except carbaryl in various environmental samples ranged 75.7~129.3%. The recovery rate of carbaryl in effluent sample was over 200% whereas carbaryl in drinking water, groundwater, and river water were in the acceptable range.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.786-792
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• 2010

This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of $320{\times}240$.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.11
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• pp.1682-1686
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• 2015

This study was conducted to establish an HPLC analysis method for determination of marker compounds as part of materials standardization for development of health functional food materials from pear pomace. The quantitative determination method of caffeic acid and chlorogenic acid as marker compounds of pear pomace extract (PPE) was optimized by HPLC analysis using a C18 column ($5{\times}250mm$, $5{\mu}m$) with a 0.2% elution gradient of acetic acid and methanol as the mobile phase at a flow rate of 0.8 mL/min and detection wavelength of 330 nm. The HPLC/UV method was applied successfully to the quantification of marker compounds in PPE after validation of the method with linearity, accuracy, and precision. The method showed high linearity of the calibration curve with a coefficient of correlation ($R^2$) of 0.9999, and limit of detection and limit of quantification were $1.14{\mu}g/mL$ (caffeic acid) and $1.61{\mu}g/mL$ (chlorogenic acid) as well as $4.9{\mu}g/mL$ (caffeic acid) and $4.9{\mu}g/mL$ (chlorogenic acid), respectively. Relative standard deviation values from intra- and inter-day precision were less than 3.1% (caffeic acid) and 4.0% (chlorogenic acid), respectively. Recovery rates of caffeic acid and chlorogenic acid at 12.5, 25, and $50{\mu}g/mL$ were 93.66~106.32% and 97.33~105.68%, respectively. An optimized method for extraction of caffeic acid and chlorogenic acid in PPE was established through diverse extraction conditions, and the validation indicated that the method is very useful for evaluation of marker compounds in PPE to develop a health functional food material.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.21-32
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• 2006

The license plate recognition (LPR) unit consists of the following core components: plate region segmentation, individual character extraction, and character recognition. Out of the above three components, accuracy in the performance of plate region segmentation determines the overall recognition rate of the LPR unit. This paper proposes an algorithm for segmenting the license plate region on the front or rear of a vehicle in a fast and accurate manner. In the case of the proposed algorithm images are captured on the spot where unmanned monitoring of illegal parking and stowage is performed with a variety of roadway environments taken into account. As a means of enhancing the segmentation performance of the on-the-spot-captured images of license plate regions, the proposed algorithm uses a mathematical model for license plate colors to convert color images into digital data. In addition, this algorithm uses Gaussian smoothing and double threshold to eliminate image noises, one-pass boundary tracing to do region labeling, and MBR to determine license plate region candidates and extract individual characters from the determined license plate region candidates, thereby segmenting the license plate region on the front or rear of a vehicle through a verification process. This study contributed to addressing the inability of conventional techniques to segment the license plate region on the front or rear of a vehicle where the frame of the license plate is damaged, through processing images in a real-time manner, thereby allowing for the practical application of the proposed algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.137-143
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• 2015

A delay-line type time-to-digital converter (TDC) implemented in a field-programmable gate array (FPGA) is most widely owing due to its simple structure and high conversion rate. However, the delay-line type TDC suffers from nonlinearity error caused by the long delay-line because its time interval measurement range is determined by the length of the used delay line. In this study, a new TDC structure with a shorter delay line by taking a pulse train as an input is proposed for improved time accuracy and efficient use of resources. The proposed TDC utilizes a pulse-train with four transitions and a transition state detector that identifies the used transition among four transitions and prevents the meta-stable state without a synchronizer. With 72 delay cells, the measured resolution and maximum non-linearity were 20.53 ps, and 1.46 LSB, respectively, and the time interval measurement range was 5070 ps which was enhanced by approximately 343 % compared to the conventional delay-line type TDC.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.126-134
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• 2015

A sleep disorder is being recognized as one of the major health issues related to high levels of stress. At the same time, interests about quality of sleep are rapidly increasing. However, diagnosing sleep disorder is not a simple task because patients should undergo polysomnography test, which requires a long time and high cost. To solve this problem, an accelerometer embedded wrist-worn device is being considered as a simple and low cost solution. However, conventional methods determine a state of user to "sleep" or "wake" according to whether values of individual section's accelerometer data exceed a certain threshold or not. As a result, a high miss-classification rate is observed due to user's intermittent movements while sleeping and tiny movements while awake. In this paper, we propose a novel method that resolves the above problems by employing a dynamic classifier which evaluates a similarity between the neighboring data scores obtained from SVM classifier. A performance of the proposed method is evaluated using 50 data sets and its superiority is verified by achieving 88.9% accuracy, 88.9% sensitivity, and 88.5% specificity.

• Journal of Korea Water Resources Association
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• v.49 no.9
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• pp.761-771
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• 2016

The GloSea5 (Global Seasonal forecasting system version 5) is provided and operated by the KMA (Korea Meteorological Administration). GloSea5 provides Forecast (FCST) and Hindcast (HCST) data and its horizontal resolution is about 60km ($0.83^{\circ}{\times}0.56^{\circ}$) in the mid-latitudes. In order to use this data in watershed-scale water management, GloSea5 needs spatial-temporal downscaling. As such, statistical downscaling was used to correct for systematic biases of variables and to improve data reliability. HCST data is provided in ensemble format, and the highest statistical correlation ($R^2=0.60$, RMSE = 88.92, NSE = 0.57) of ensemble precipitation was reported for the Yongdam Dam watershed on the #6 grid. Additionally, the original GloSea5 (600.1 mm) showed the greatest difference (-26.5%) compared to observations (816.1 mm) during the summer flood season. However, downscaled GloSea5 was shown to have only a -3.1% error rate. Most of the underestimated results corresponded to precipitation levels during the flood season and the downscaled GloSea5 showed important results of restoration in precipitation levels. Per the analysis results of spatial autocorrelation using seasonal Moran's I, the spatial distribution was shown to be statistically significant. These results can improve the uncertainty of original GloSea5 and substantiate its spatial-temporal accuracy and validity. The spatial-temporal reproducibility assessment will play a very important role as basic data for watershed-scale water management.

• Journal of Periodontal and Implant Science
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• v.36 no.1
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• pp.147-154
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• 2006

Surgical intervention in the posterior maxillary region requires detailed knowledge of maxillary sinus anatomy and the possible anatomical variations. This study evaluated the incidence, location of maxillary sinus septa by using radiographic (panoramic radiography and computed tomography) findings and comparison of panoramic radography with CT in antral anatomical variation. This study was based on data from 70 sinuses in partial dentate maxilla. The sample consisted of 61 patients(25 women and 36 men, with ages ranging between 19 and 77 years and a mean age of $49.4{\pm}11.3$ years) who were being treatment-planned to receive implant-supported restorations. First, the panoramic images were examined for the presence of antral septa by radiologist and examiner who don't know about CT findings. And incidence of antral septa was evaluated using an axial plane of CT image. The incidence of septa was compared between panoramic radiography and CT. The accuracy of the incidence was compared between radiologists and dentists. A total of 20 septa were found in 70 sinuses on CT image and the prevalence of one or more septa per sinus was found to be 28.6%. The assumed incidence of septa on panoramic radiography was $27.6%{\pm}2.2%$ in radiologist and $31.9%{\pm}5.8%$ in dentists. Erroneous diagnosis rate was 11.42% in radiologist and 15.96% in dentists. 40% of antral septa were located in the anterior(premolar) region, 30% of septa were located in the middle(first molar) and posterior(second molar) region separately. Prior to implant placement, it seems appropriate to consider panoramic radiography as a standard radiographic examination and periapical radiographs may be used to complete the findings in regions not sharply depicted in the panoramic radiograph. And cross-sectional imaging should be used in sites with severe bone loss and close proximity of the maxillary sinus.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.279-282
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• 2017

Kinect depth sensor, a depth camera developed by Microsoft as a natural user interface for game appeared as a very useful tool in computer vision field. In this paper, due to kinect's depth sensor and its high frame rate, we developed a distance measurement system using Kinect camera to test it for unmanned vehicles which need vision systems to perceive the surrounding environment like human do in order to detect objects in their path. Therefore, kinect depth sensor is used to detect objects in its field of view and enhance the distance measurement system from objects to the vision sensor. Detected object is identified in accuracy way to determine if it is a real object or a pixel nose to reduce the processing time by ignoring pixels which are not a part of a real object. Using depth segmentation techniques along with Open CV library for image processing, we can identify present objects within Kinect camera's field of view and measure the distance from them to the sensor. Tests show promising results that this system can be used as well for autonomous vehicles equipped with low-cost range sensor, Kinect camera, for further processing depending on the application type when they reach a certain distance far from detected objects.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.643-652
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• 2006

Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.