• Food Science and Industry
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• v.45 no.2
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• pp.10-18
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• 2012

Maesil began to grow in Gwangyang after the old Mr. Yulsan Kim Oh-cheon brought 5,000 trees of maesil(Prunes mume) from Japan in 1931. Today, Gwangyang maesil comprises approximately 25% of total national output. Gwangyang produces a variety of foods, manufactured foods and beverages using maesil. Besides, numerous tourists came to the 15th Gwangyang International Ume Flower Culture Festival to enjoy the festival and appreciate blossoming ume flowers. More than 1.9 million people visited Blue Ume Flower Farm in Gwangyang in the year of 2010. As many visitors came to the city simultaneously, however, there occurred confusion. So, it is thought that the following measures are necessary to enhance the tourism value of Gwangyang maesil. First, a symbolic story for Gwangyang maesil or maehwa(ume flower) needs to be created. Second, snack foods for sightseers need to be developed. Third, diverse attractive elements to prolong tourists' stays are worth developing. Fourth, it is necessary for Gwangyang to hold competitions for ideas to activate tourism through maesil. Fifth, Gwangyang needs to promote collaborative development of tourist items and collaborative tourism marketing in close cooperation with neighboring cities and counties. Finally, it is worthwhile for Gwangyang to host an international fruits and seeds exposition or exhibition and it needs to strengthen active promotion and marketing suitable for the Smart Age.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.605-612
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• 2019

Traditionally, travel demand forecasts have been conducted based on the data collected by a survey of individual travel behavior, and their limitations such as the accuracy of travel demand forecasts have been also raised. In recent, advancements in information and communication technologies are enabling new datasets in travel demand forecasting research. Such datasets include data from global positioning system (GPS) devices, data from mobile phone signalling, and data from call detail record (CDR), and they are used for reducing the errors in travel demand forecasts. Based on these background, the objective of this study is to assess the feasibility of CDR as a base data for travel demand forecasts. To perform this objective, CDR data collected for Daegu Metropolitan area for four days in April including weekdays and weekend days, 2017, were used. Based on these data, we analyzed the correlation between CDR and travel demand by travel survey data. The result showed that there exists the correlation and the correlation tends to be higher in discretionary trips such as non-home based business, non-home based shopping, and non-home based other trips.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1061-1069
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• 2021

Recently, the magnitude and frequency of extreme heavy rains and localized heavy rains have increased due to abnormal climate, which caused increased flood damage in river basin. As a result, the nonlinearity of the hydrological system of rivers or basins is increasing, and there is a limitation in that the lead time is insufficient to predict the water level using the existing physical-based hydrological model. This study predicted the water level at Ulsan (Taehwagyo) with a lead time of 0, 1, 2, 3, 6, 12 hours by applying deep learning techniques based on Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and evaluated the prediction accuracy. As a result, DNN model using the sliding window concept showed the highest accuracy with a correlation coefficient of 0.97 and RMSE of 0.82 m. If deep learning-based water level prediction using a DNN model is performed in the future, high prediction accuracy and sufficient lead time can be secured than water level prediction using existing physical-based hydrological models.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1517-1526
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• 2021

With the recent global and domestic development of Earth observation satellites, the applications of satellite images have been widened. Research for improving the geometric accuracy of satellite images is being actively carried out. This paper studies the possibility of automated ground control point (GCP) generation for CAS-4 satellite, to be launched in 2025 with the capability of image acquisition at 5 m ground sampling distance (GSD). In particular, this paper focuses to check whether GCP chips with 25 cm GSD established for CAS-1 satellite images can be used for CAS-4 and to check whether optimalspatial resolution for matching between CAS-4 images and GCP chips can be determined to improve matching performance. Experiments were carried out using RapidEye images, which have similar GSD to CAS-4. Original satellite images were upsampled to make satellite images with smaller GSDs. At each GSD level, up-sampled satellite images were matched against GCP chips and precision sensor models were estimated. Results shows that the accuracy of sensor models were improved with images atsmaller GSD compared to the sensor model accuracy established with original images. At 1.25~1.67 m GSD, the accuracy of about 2.4 m was achieved. This finding lead that the possibility of automated GCP extraction and precision ortho-image generation for CAS-4 with improved accuracy.

• Industry Promotion Research
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• v.6 no.4
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• pp.23-30
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• 2021

This study is a study on the satisfaction of the living conditions of the residents of Chungcheongnam-do, and the analysis data was based on the results of the Chungcheongnam-do social survey conducted in 2020 by Statistics Korea. The results of the analysis on the satisfaction of the living conditions of the residents of Chungnam Province are summarized as follows. The level of satisfaction with the quality of life of the living environment, which is a personal characteristic, was 5.92 out of 10 for the degree of satisfaction with one's life, and 6.28 out of 10 for the overall value of the work one is doing. The overall life satisfaction of the region (city and gun) was analyzed as 5.81 out of 10, indicating that the satisfaction of Chungnam residents was more than average. In addition, satisfaction with the residential housing environment was analyzed with the highest frequency and ratio of 43.5%, with 226 people being slightly satisfied. Satisfaction with facility use was also slightly higher in 231 people, showing 44.5% response rate, and slightly higher in women than in men. This study is meaningful in that it provides basic data such as policy implications for improving the quality of life by grasping the social interests related to the quality of life and the subjective consciousness of the people of Chungnam.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.111-120
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• 2021

When a fire occurs in a commercial building, the evacuation route is complicated and the direction of smoke and flame is similar to that of the egress route of occupants, resulting in many casualties. Performance-based evacuation design for buildings is essential to minimize human casualties. In order to apply the performance-based evacuation design to buildings, it requires a complex fire simulation for each building, demanding a large amount of time and manpower. In order to supplement this, it would be very useful to develop an Available Safe Egress Time (ASET) prediction model that can rationally derive the ASET without performing a fire simulation. In this study, the correlations between fire temperature with visibility and toxic gas concentration were investigated through a fire simulation on a commercial building, from which databases for the training of artificial neural networks (ANN) were created. Based on this, an ANN model that can predict the available safe egress time was developed. In order to examine whether the proposed ANN model can be applied to other commercial buildings, it was applied to another commercial building, and the proposed model was found to estimate the available safe egress time of the commercial building very accurately.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.83-91
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• 2021

In this study, to improve the connection performance between the existing reinforced concrete (R/C) frame and the strengthening member, we proposed a new H-section steel frame with elastic pad (HSFEP) system for seismic rehabilitation of existing medium-to-low-rise reinforced concrete (R/C) buildings. This HSFEP strengthening system exhibits an excellent connection performance because an elastic pad is installed between the existing structure and reinforcing frame. The method shows a strength design approach implemented via retrofitting, to easily increase the ultimate lateral load capacity of R/C buildings lacking seismic data, which exhibit shear failure mechanism. Two full-size two-story R/C frame specimens were designed based on an existing R/C building in Korea lacking seismic data, and then strengthened using the HSFEP system; thus, one control specimen and one specimen strengthened with the HSFEP system were used. Pseudodynamic tests were conducted to verify the effects of seismic retrofitting, and the earthquake response behavior with use of the proposed method, in terms of the maximum response strength, response displacement, and degree of earthquake damage compared with the control R/C frame. Test results revealed that the proposed HSFEP strengthening method, internally applied to the R/C frame, effectively increased the lateral ultimate strength, resulting in reduced response displacement of R/C structures under large scale earthquake conditions.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.177-198
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• 2021

Existing change detection researches have been focused on changes of land use and land cover (LULC), damaged areas, or large vegetated and water regions. On the other hands, increased temporal and spatial resolution of satellite images are strongly suggesting the feasibility of change detection of small objects such as vehicles and ships. In order to check the feasibility, this paper analyzes the performance of existing pixel-based change detection methods over small objects. We applied pixel differencing, PCA (principal component analysis) analysis, MAD (Multivariate Alteration Detection), and IR-MAD (Iteratively Reweighted-MAD) to Kompsat-3A and Google Map images taken within 10 days. We extracted ground references for changed and non-changed small objects from the images and used them for performance analysis of change detection results. Our analysis showed that MAD and IR-MAD, that are known to perform best over LULC and large areal changes, offered best performance over small object changes among the methods tested. It also showed that the spectral band with high reflectivity of the object of interest needs to be included for change analysis.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.29-43
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• 2019

The calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. This method can be extended to other optimization tasks using GA in transportation studies.