In this study, in order to investigate the characteristics of secondary consolidation in the soft clay ground, oedometer tests were carried out in a normally consolidated condition, and the consolidation characteristics of the soft clay ground were examined by the Finite Difference Method (FDM) based on the Elasto-Viscous model proposed by Yoshikuni. The consolidation tests adjusted the consolidation load increment ratio(${\Delta}p/p_0$) to 1.0 for the four cases with initial consolidation pressures of 0.8, 1.6, 3.2, and 6.4 kgf/$cm^2$. The long-term consolidation tests were examined by the tests that changed the load increment ratio to clarify the effect of consolidation load increment. Although the numerical analysis was delayed in the primary consolidation process, from the result of the numerical analysis of the laboratory tests, the applicability of the Elasto-Viscous model was verified from the agreement of the secondary consolidation process. Based on the developing of model ground consist of general soft clay, influences of consoliation parameters on the consolidation characteristics were studied by the numerical analysis.
The research compared the motivational network of traits predictive of complaint attitudes across consumers in the U.S. and South Korean cultures. Overall, the results revealed a similar pattern of traits predictive of complaint attitudes in the two cultures. The traits of value consciousness, general self-efficacy, emotional instability, and the need for material resources were positively related to attitudes toward complaining. In contrast, conscientiousness was negatively related to complaint attitudes. The only trait predictor of complaining attitude that was significantly different between the Korean and U.S. samples was shopping enjoyment. It was negatively related to complaining attitude in the U.S. sample but unrelated to complaining attitude in the Korean sample. Understanding the personality traits predictive of complaint attitudes has the potential to help marketers develop messages that will encourage the low complaint prone to voice their dissatisfaction. This is important, because when a consumer complains about and unsatisfactory purchase, it gives the firm a chance to take actions to avoid losing a customer.
The recent economic recession and price rise reduces excessive consumption as a whole. So companies take more interest in and use discount coupons as a means of sales promotion to reinforce their competitiveness. The combination of Internet and mobile communication technology leads to an explosive increase in the use of mobile Internet service, which promotes commercialization of mobile coupons. Nevertheless, there are absolutely insufficient researches on mobile coupons than those on paper ones. In this context, this study tries to consider intention of accepting and using mobile coupons. The innovated Technology Acceptance Model (TAM) was used to see factors of using mobile coupons considered important by customers. Through the combination of characteristics of mobile coupon service and values obtained from mobile coupons, effects of variables to enhance intention of using mobile coupons were empirically analyzed. In particular, this study suggested importance of psychological as well as economic values of mobile coupons and emphasized good considerations of the psychological aspect, such as shame, stinginess, and reputation sensitivity, in using mobile coupons as an important factor for intention of using the coupons. Another empirical analysis was made of what moderating roles consumers' self-construalplayed in the effects of mobile coupon values perceived by consumers on intention of using coupons. As a result, immediate connectivity and situational provision among characteristics of mobile coupon service were found to affect ease and usability. It was also shown that perceived ease and usability had significant effects on both economic and psychological values, which then had significant effects on intention of using a mobile system. After testing moderating effects of self-construal, the degree of effects of perceived mobile coupon values on intention of using mobile coupons was greater among inter-dependent self-construal users than among independent ones. This study considered various schemes of improving intention to use mobile coupons and provided a foundation to help companies make a strategy for mobile coupons to be activated in the future.
Sea-surface wind is an important variable in ocean-atmosphere interactions, leading to the changes in ocean surface currents and circulation, mixed layers, and heat flux. With the development of satellite technology, sea-surface winds data retrieved from scatterometer observation data have been used for various purposes. In a complex marine environment such as the Korean Peninsula coast, scatterometer-observed sea-surface wind is an important factor for analyzing ocean and atmospheric phenomena. Therefore, the validation results of wind accuracy can be used for diverse applications. In this study, the sea-surface winds derived from ASCAT (Advanced SCATterometer) mounted on MetOp-A/B (METeorological Operational Satellite-A/B) were validated compared to in-situ wind measurements at 16 marine buoy stations around the Korean Peninsula from January to December 2020. The buoy winds measured at a height of 4-5 m from the sea surface were converted to 10-m neutral winds using the LKB (Liu-Katsaros-Businger) model. The matchup procedure produced 5,544 and 10,051 collocation points for MetOp-A and MetOp-B, respectively. The root mean square errors (RMSE) were 1.36 and 1.28 m s-1, and bias errors amounted to 0.44 and 0.65 m s-1 for MetOp-A and MetOp-B, respectively. The wind directions of both scatterometers exhibited negative biases of -8.03° and -6.97° and RMSE values of 32.46° and 36.06° for MetOp-A and MetOp-B, respectively. These errors were likely associated with the stratification and dynamics of the marine-atmospheric boundary layer. In the seas around the Korean Peninsula, the sea-surface winds of the ASCAT tended to be more overestimated than the in-situ wind speeds, particularly at weak wind speeds. In addition, the closer the distance from the coast, the more the amplification of error. The present results could contribute to the development of a prediction model as improved input data and the understanding of air-sea interaction and impact of typhoons in the coastal regions around the Korean Peninsula.
The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.
District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.
As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.
As the internet technologies develop, the geographic information system environment is changing to the web-based service. Since geospatial information of the existing Web-GIS services were developed independently, there is no interoperability to support diverse map formats. In spite of the same geospatial information object it can be used for various proposes that is duplicated in GIS separately. It needs intelligent strategies for optimal replica selection, which is identification of replication geospatial information objects. And for management of replication objects, OMG, GLOBE and GRID computing suggested related frameworks. But these researches are not thorough going enough in case of geospatial information object. This paper presents a model of location service, which is supported for optimal selection among replication and management of replication objects. It is consist of tree main services. The first is binding service which can save names and properties of object defined by users according to service offers and enable clients to search them on the service of offers. The second is location service which can manage location information with contact records. And obtains performance information by the Load Sharing Facility on system independently with contact address. The third is intelligent selection service which can obtain basic/performance information from the binding service/location service and provide both faster access and better performance characteristics by rules as intelligent model based on rough sets. For the validity of location service model, this research presents the processes of location service execution with Graphic User Interface.
Jungkyun Shin;Jiho Ha;Gabseok Seo;Young-Jun Kim;Nyeonkeon Kang;Jounggyu Choi;Dongwoo Cho;Hanhui Lee;Seong-Pil Kim
Geophysics and Geophysical Exploration
/
v.26
no.2
/
pp.52-61
/
2023
The use of ultra-high-resolution (UHR) seismic surveys to preceisly characterize coastal and shallow structures have increased recently. UHR surveys derive a spatial resolution of 3.125 m using a high-frequency source (80 Hz to 1 kHz). A digital streamer system is an essential module for acquiring high-quality UHR seismic data. Localization studies have focused on reducing purchase costs and decreasing maintenance periods. Basic performance verification and application tests of the developed streamer have been successfully carried out; however, a comparative analysis with the existing benchmark model was not conducted. In this study, we characterized data obtained by using a developed streamer and a benchmark model simultaneously. Tamhae 2 and auxiliary equipment of the Korea Institute of Geoscience and Mineral Resources were used to acquire 2D seismic data, which were analyzed from different perspectives. The data obtained using the developed streamer differed in sensitivity from that obtained using benchmark model by frequency band.However, both type of data had a very high level of similarity in the range corresponding to the central frequency band of the seismic source. However, in the low frequency band below 60 Hz, data obtained using the developed streamer showed a lower signal-to-noise ratio than that obtained using the benchmark model.This lower ratio can hinder the quality in data acquisition using low-frequency sound sources such as cluster air guns. Three causes for this difference were, and streamers developed in future will attempt to reflect on these improvements.
Permeability coefficient of concrete is a substaintial key parameter for understanding the durability performance of concrete and its microstructural densification. Many researches for the issue have been accomplished, however, it is very rare to deal with the theoretical study on permeability coefficient in connection with carbonation of concrete and the the effect of volumetric fraction of cement paste or aggregate on the permeability coefficient. The majority of these researches have not dealt with this issue combined with carbonation of concrete, although carbonation can significantly impact on the permeability coefficient of concrete. The purpose of this study is to establish a fundamental approach to compute the permeability coefficeint of (non)carbonated concrete. When simulating a microstructural characteristics as a starting point for deriving a model for the permeability coefficient by the numerical simulation program for cementitious materials, HYMOSTRUC, a more realistic formulation can be achieved. For several compositions of cement pastes, the permeability coefficient was calculated with the analytical formulation, followed by a microstructure-based model. Emphasis was on the microstructural changes and its effective change of the permeability coefficient of carbonated concrete. For carbonated concrete, reduced porosity was calculated and this was used for calculating the permeability coefficeint. The computational result was compared with experimental outcome.
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