• Journal of Broadcast Engineering
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• v.12 no.1 s.34
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• pp.11-27
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• 2007

T-DMB(Terrestrial-Digital Multimedia Broadcasting) for portable and mobile broadcasting service was officially launched in the metropolitan area of Korea in December 2005. The development of interactive data services and the expansion of T-DMB network have been now progressed, and the regional T-DMB broadcasters will be selected in the near future. Although it is important to evaluate service area and reduce a fringe area for optimizing RF coverage of T-DMB, there was no professional system to support those works, and therefore lots of resources in budget, manpower and time were required. The measurement considering characteristics of SFN(Single Frequency Network) is essential in the implementation of T-DMB network which needs to control synchronization, spacing and output power of transmitters. In addition, mobile measurement, enough measured parameters for many-sided analysis of reception quality, efficient management of enormous measured data and representation of measured results on the electronic map are also important in evaluating service area. In this paper, we derived requirements for a new measurement system in T-DMB by considering the above details, and we proposed and developed an integrated measurement and analysis system. The developed system was applied to the implementation of T-DMB network of KBS and field tests, and it proved its efficiency and accuracy in result.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.351-360
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• 2018

Human emotion recognition is a research topic that is receiving continuous attention in computer vision and artificial intelligence domains. This paper proposes a method for classifying human emotions through multiple neural networks based on multi-modal signals which consist of image, landmark, and audio in a wild environment. The proposed method has the following features. First, the learning performance of the image-based network is greatly improved by employing both multi-task learning and semi-supervised learning using the spatio-temporal characteristic of videos. Second, a model for converting 1-dimensional (1D) landmark information of face into two-dimensional (2D) images, is newly proposed, and a CNN-LSTM network based on the model is proposed for better emotion recognition. Third, based on an observation that audio signals are often very effective for specific emotions, we propose an audio deep learning mechanism robust to the specific emotions. Finally, so-called emotion adaptive fusion is applied to enable synergy of multiple networks. The proposed network improves emotion classification performance by appropriately integrating existing supervised learning and semi-supervised learning networks. In the fifth attempt on the given test set in the EmotiW2017 challenge, the proposed method achieved a classification accuracy of 57.12%.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.99-110
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• 1999

This study deals with the adaptability questions of O-D table estimation models. Its objectives are two-fold; (1) to estimate the characteristics of various O-D table estimation models(i.e. linear regression models. entropy models and statistic models) and (2) to find the model which estimates the O-D table with the best accuracy under the various data conditions. In Pursuing the above, this study gives the particular attentions to the test of the models, using the Sioux Falls network and equilibrium assignment method of MINUTP. The major findings are the followings. Firstly. it finds that the statistic models have the most goodness of fat among all models, if the required data are all Prepared. But it Presents that statistic models are the most sensitive against the underspecification and inconsistency problems of link data. Secondly, It shows that the linear regression models have the worst goodness of fat among all models. But the linear regression models are the most insensitive to the underspecification and inconsistency problems. Thirdly, THE/1 model of entropy model is sensitive against the underspecification and incon-sistency problems, but THE/2 model is insensitive. Finally, other informations like total volume, zonal Production and attraction volumes in 0-D table, help models to gain the better goodness of fit. Especially, in the statistic models. both the zonal production and attraction volume data are helpful to estimate the link volumes. It can be expected that the results dive some implications not only to the selection of optimal model under the various given data, but also to the development or modification of model.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.97-106
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• 2011

Mode choice model is an essential element for estimating- the demand of new means of transportation in the planning stage as well as in the establishment phase. In general, current demand analysis model developed for the mode choice analysis applies common parameters of utility function in each region which causes inaccuracy in forecasting mode choice behavior. Several critical problems from using common parameters are: a common parameter set can not reflect different distribution of coefficient for travel time and travel cost by different population. Consequently, the resulting model fails to accurately explain policy variables such as travel time and travel cost. In particular, the nonlinear logit model applied to aggregation data is vulnerable to the aggregation error. The purpose of this paper is to consider the regional characteristics by adopting the parameters fitted to each area, so as to reduce prediction errors and enhance accuracy of the resulting mode choice model. In order to estimate parameter of each area, this study used Household Travel Survey Data of Metropolitan Transportation Authority. For the verification of the model, the value of time by marginal rate of substitution is evaluated and statistical test for resulting coefficients is also carried out. In order to crosscheck the applicability and reliability of the model, changes in mode choice are analyzed when Seoul subway line 9 is newly opened and the results are compared with those from the existing model developed without considering the regional characteristics.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.17-25
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• 2013

In this paper we interpreted the changes in wind field over complex mountainous terrains. The results of our study can be applied for predicting the direction of fire spread and for establishing strategies for fire prevention. The study area is bounded by $12{\times}12$ km domains of the Samcheok's long-term ecological research (LTER) site located in the east coast, in which a large-fire had occurred from 7 to 13 April 2000. Because of the area's complex topography, we compared the result of the Weather Research and Forecasting (WRF) mesoscale model with those observed by four automated weather stations. The WRF simulation overestimated the wind speed by 5 to 8 m/s (~200%) in comparison with those from four automated weather stations. The wind directions observed by the AWSs were from various directions whereas those from WRF model were mostly west wind at all stations. Overall, the simulations by the WRF mesoscale models were not appropriate for the estimation of microscale wind fields over complex mountainous areas. To overcome such inadequacy of reproducing the wind fields, we employed the ENVI-met model over Samcheok's LTER site. In order to test the model's sensitivity with the terrain effects, experimental simulations were conducted with various initial conditions. The simulation results of the ENVI-met model showed a reasonable agreement in wind speeds (about 70% accuracy) with those of the four AWSs. Also, that the variations in wind directions agreed reasonably well with changes in terrain effect. We concluded that the ENVI-met model is more appropriate in representing the microscale wind field over complex mountain terrains, which is required to predict fire spread and to establish strategies for forest fire prevention.

• The Journal of Engineering Geology
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• v.15 no.2 s.42
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• pp.201-212
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• 2005

A photogrammetric camera system and a software have been built for capturing planned stereo images. To evaluate the system,25 planar data from a constructed rock slope were measured using both geological compass and photo system. Comparison of the data groups from both system showed matching relationship that falls within the error range of $5.25\pm4.53$ in strike and $3.18\pm3.17$ in dip angles, when the 2 standard deviation error distributions were considered. To evaluate the errors of the Photo matching and non planarity of the surface, orientations of the same plane were repeatedly measured 20times. These measurements showed error ranges of $8.2\pm3.4$in strike and $6.6\pm3.4$ in dip angle, considering the same error distributions. Measured strikes and dips were compared with the corresponding compass measurements in 5 constructed. slopes to test the system. Stereonet plots showed that the photo system measured data coincided well with the compass measurements. With these evaluations, the photo system can measure the planar structure in inaccessible locations with reliable accuracy at the same time reducing the data gathering period therefore resulting to an efficient geological survey.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.4
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• pp.341-348
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• 2016

Micro-algae, one of the biological resources for alternative energy, has been heavily studied. Among various methods to analyze the status of the micro-algae including counting, screening, and flocculation, the flocculation approach has been widely accepted in many critical applications such as red tide removal study or microalgae resource study. To characterize the flocculation status of the micro-alga. A traditional optical modality, i.e., photospectrometry, measuring the optical density of the flocs has been frequently employed. While this traditional optical method needs shorter time than the counting method in flocculation status analysis, it has relatively lower detection accuracy. To address this issue, a novel real-time micro-algae flocculation analysis method based on the lens-free shadow imaging technique (LSIT) is introduced. Both single cell detection and floc detection are simultaneously available with a proposed lens-free shadow image, confirmed by comparing the results with optical microscope images. And three shadow parameters, e.g., number of flocs, effective area of flocs, and maximum size of floc, enabling quantification of the flocculation phenomenon of micro-alga, are firstly demonstrated in this article. The efficacy of each shadow parameter is verified with the real-time flocculation monitoring experiments using custom developed cohesive agents.

• The Korean Journal of Pesticide Science
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• v.18 no.4
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• pp.321-329
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• 2014

This experiment was conducted to establish an analytical method for residues of amisulbrom, as recently developed an oomycete-specific fungicide showing inhibition of fungal respiration, in crops using HPLC-UVD/MS. Amisulbrom residue was extracted with acetonitrile from representative samples of five raw products which comprised apple, green pepper, kimchi cabbage, potato and hulled rice. The extract was diluted with 50 mL of saline water and directly partitioned into dichloromethane to remove polar co-extractives in the aqueous phase. For the hulled rice sample, n-hexane/acetonitrile partition was additionally employed to remove non-polar lipids. The extract was finally purified by optimized Florisil column chromatography. On an octadecylsilyl column in HPLC, amisulbrom was successfully separated from sample co-extractives and sensitively quantitated by ultraviolet absorption at 255 nm with no interference. Accuracy and precision of the proposed method was validated by the recovery test on every crop samples fortified with amisulbrom at 3 concentration levels per crop in each triplication. Mean recoveries ranged from 85.3% to 105.6% in five representative agricultural commodities. The coefficients of variation were all less than 10%, irrespective of sample types and fortification levels. Limit of quantitation (LOQ) of amisulbrom was 0.04 mg/kg as verified by the recovery experiment. A confirmatory method using LC/MS with selected-ion monitoring technique was also provided to clearly identify the suspected residue. The proposed method was sensitive, reproducible and easy-to-operate enough to routinely determine the residue of amisulbrom in agricultural commodities.

• Korean Journal of Food Science and Technology
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• v.23 no.1
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• pp.88-92
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• 1991

To analyze the effects of the system parameters on the target parameters, which include the amount of water evaporation, water solubility index(WSI) and water absorption index(WAI), test trials of fractional factorial design of the three process variables at three levels were carried out for corn grit with a laboratory twin-screw extruder with three different screw configurations. The system parameters collected from the trials, such as extrusion temperature, specific mechanical energy input(SME) and mean residence time(RT), were showed the ranges of $129{\sim}182^{\circ}C$, $67{\sim}163\;kwh/ton$ and $12{\sim}34\;sec$, respectively. Within these ranges of the system parameters, the target parameters were able to be quantified by using multiple regression equations. The correlation of results with the system parameters blocked by the screw configuration as dependent variables, yield correlation coefficients above 0.90, and the correlation using the system parameters obtained from whole experiment system as the dependent variables yield correlation coefficients around 0.80. The functional relationship, which can be quantified by second order polynomial regression equation with only two system parameters within necessary degree of accuracy, can he graped in three dimensional surface response and contour diagrams.