• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.721-732
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• 2016

The accuracy and error characteristics of microwave Sea Surface Temperature (SST) measurements in the Northwest Pacific were analyzed by utilizing 162,264 collocated matchup data between GCOM-W1/AMSR2 data and oceanic in-situ temperature measurements from July 2012 to August 2016. The AMSR2 SST measurements had a Root-Mean-Square (RMS) error of about $0.63^{\circ}C$ and a bias error of about $0.05^{\circ}C$. The SST differences between AMSR2 and in-situ measurements were caused by various factors, such as wind speed, SST, distance from the coast, and the thermal front. The AMSR2 SST data showed an error due to the diurnal effect, which was much higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. In addition, the RMS error tended to be large in the winter because the emissivity of the sea surface was increased by high wind speeds and it could induce positive deviation in the SST retrieval. Low sensitivity at colder temperature and land contamination also affected an increase in the error of AMSR2 SST. An analysis of the effect of the thermal front on satellite SST error indicated that SST error increased as the magnitude of the spatial gradient of the SST increased and the distance from the front decreased. The purpose of this study was to provide a basis for further research applying microwave SST in the Northwest Pacific. In addition, the results suggested that analyzing the errors related to the environmental factors in the study area must precede any further analysis in order to obtain more accurate satellite SST measurements.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.2
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• pp.132-140
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• 2005

This study was conducted to develop regional site index equations for main tree species in Gangwon, Gyunggi-Chungcheong, Gyungsang, and Jeolla area of Korea, using environmental and soil factors obtained from a digital forest site map. Using the large data set obtained from the digital forest map, a total of 28 environmental and soil factors were regressed on site index by tree species for developing the best site index equations for each of the regions. The selected main tree species were Larix 1eptolepis, Pinus koraiensis, Pinus densiflora, Pinus thunbergii, and Quercus acutissima. Finally, four to five environmental and soil factors by species were chosen as independent variables in defining the best regional site index equations with the highest coefficients of determination $(R^2)$. For those site index equations, three evaluation statistics such as mean difference, standard deviation of difference and standard error of difference were applied to the data sets independently collected from fields within the region. According to the evaluation statistics, it was found that the regional site index equations by species developed in this study conformed well to the independent data set, having relatively low bias and variation. It was concluded that the regional site index equations by species had sufficient capability for the estimation of site productivity.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1243-1254
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• 2021

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.536-555
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• 2021

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.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Global Scholars of Marketing Science
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• v.20 no.3
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• pp.269-277
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• 2010

Previous studies have shown that the most important factor affecting customer loyalty in the service industry is service quality. However, on the subject of whether service quality has a direct or indirect effect on customer loyalty, scholars' views apparently vary. Some studies suggest that service quality has a direct and fundamental influence on customer loyalty (Bai and Liu, 2002). However, others have shown that service quality not only directly affects customer loyalty, it also has an indirect impact on customer loyalty by influencing customer satisfaction and perceived value (Cronin, Brady, and Hult, 2000). Currently, there are few domestic articles that specifically address the relationship between service quality and customer loyalty in the mobile communication industry. Moreover, research has studied customer loyalty as a whole variable, rather than breaking it down further into multiple dimensions. Based on this analysis, this paper summarizes previous study results, establishes an effect mechanism model among service quality, customer satisfaction, and customer loyalty in the mobile communication industry, and presents a statistical test on model assumptions by using customer investigation data from Heilongjiang Mobile Company. It provides theoretical guidance for mobile service management based on the discussion of the hypothesis test results. For data collection, the sample comprised mobile users in Harbin city, and the survey was taken by random sampling. Out of a total of 300 questionnaires, 276 (92.9%) were recovered. After excluding invalid questionnaires, 249 remained, for an effective rate of 82.6 percent for the study. Cronbach's ${\alpha}$ coefficient was adapted to assess the scale reliability, and validity testing was conducted on the questionnaire from three aspects: content validity, construct validity. and convergent validity. The study tested for goodness of fit mainly from the absolute and relative fit indexes. From the hypothesis testing results, overall, four assumptions have not been supported. The ultimate affective relationship of service quality, customer satisfaction, and customer loyalty is demonstrated in Figure 2. On the whole, the service quality of the communication industry not only has a direct positive significant effect on customer loyalty, it also has an indirect positive significant effect on customer loyalty through service quality; the affective mechanism and extent of customer loyalty are different, and are influenced by each dimension of service quality. This study used the questionnaires of existing literature from home and abroad and tested them in empirical research, with all questions adapted to seven-point Likert scales. With the SERVQUAL scale of Parasuraman, Zeithaml, and Berry (1988), or PZB, as a reference point, service quality was divided into five dimensions-tangibility, reliability, responsiveness, assurance, and empathy-and the questions were simplified down to nineteen. The measurement of customer satisfaction was based mainly on Fornell (1992) and Wang and Han (2003), ending up with four questions. Based on the study’s three indicators of price tolerance, first choice, and complaint reaction were used to measure attitudinal loyalty, while repurchase intention, recommendation, and reputation measured behavioral loyalty. The collection and collation of literature data produced a model of the relationship among service quality, customer satisfaction, and customer loyalty in mobile communications, and China Mobile in the city of Harbin in Heilongjiang province was used for conducting an empirical test of the model and obtaining some useful conclusions. First, service quality in mobile communication is formed by the five factors mentioned earlier: tangibility, reliability, responsiveness, assurance, and empathy. On the basis of PZB SERVQUAL, the study designed a measurement scale of service quality for the mobile communications industry, and obtained these five factors through exploratory factor analysis. The factors fit basically with the five elements, indicating the concept of five elements of service quality for the mobile communications industry. Second, service quality in mobile communications has both direct and indirect positive effects on attitudinal loyalty, with the indirect effect being produced through the intermediary variable, customer satisfaction. There are also both direct and indirect positive effects on behavioral loyalty, with the indirect effect produced through two intermediary variables: customer satisfaction and attitudinal loyalty. This shows that better service quality and higher customer satisfaction will activate the attitudinal to service providers more active and show loyalty to service providers much easier. In addition, the effect mechanism of all dimensions of service quality on all dimensions of customer loyalty is different. Third, customer satisfaction plays a significant intermediary role among service quality and attitudinal and behavioral loyalty, indicating that improving service quality can boost customer satisfaction and make it easier for satisfied customers to become loyal customers. Moreover, attitudinal loyalty plays a significant intermediary role between service quality and behavioral loyalty, indicating that only attitudinally and behaviorally loyal customers are truly loyal customers. The research conclusions have some indications for Chinese telecom operators and others to upgrade their service quality. Two limitations to the study are also mentioned. First, all data were collected in the Heilongjiang area, so there might be a common method bias that skews the results. Second, the discussion addresses the relationship between service quality and customer loyalty, setting customer satisfaction as mediator, but does not consider other factors, like customer value and consumer features, This research will be continued in the future.

• Journal of Korea Water Resources Association
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• v.48 no.7
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• pp.553-566
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• 2015

Acoustic Doppler Current Profilers(ADCPs) have capability to concurrently capitalize three-dimensional velocity vector and bathymetry with highly efficient and rapid manner, and thereby enabling ADCPs to document the hydrodynamic and morphologic data in very high spatial and temporal resolution better than other contemporary instruments. However, ADCPs are also limited in terms of the inevitable unmeasured regions near bottom, surface, and edges of a given cross-section. The velocity in those unmeasured regions are usually extrapolated or assumed for calculating flow discharge, which definitely affects the accuracy in the discharge assessment. This study aimed at scrutinizing a conventional extrapolation method(i.e., the 1/6 power law) for estimating the unmeasured regions to figure out the accuracy in ADCP discharge measurements. For the comparative analysis, we collected spatially dense velocity data using ADV as well as stationary ADCP in a real-scale straight river channel, and applied the 1/6 power law for testing its applicability in conjunction with the logarithmic law which is another representative velocity law. As results, the logarithmic law fitted better with actual velocity measurement than the 1/6 power law. In particular, the 1/6 power law showed a tendency to underestimate the velocity in the near surface region and overestimate in the near bottom region. This finding indicated that the 1/6 power law could be unsatisfactory to follow actual flow regime, thus that resulted discharge estimates in both unmeasured top and bottom region can give rise to discharge bias. Therefore, the logarithmic law should be considered as an alternative especially for the stationary ADCP discharge measurement. In addition, it was found that ADCP should be operated in at least more than 0.6 m of water depth in the left and right edges for better estimate edge discharges. In the future, similar comparative analysis might be required for the moving boat ADCP discharge measurement method, which has been more widely used in the field.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.39-50
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• 2019

This study aims to assess the influence of climate change on the hydrological cycle at a basin level in North Korea. The selected model for this study is MRI-CGCM 3, the one used for the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover, this study adopted the Spatial Disaggregation-Quantile Delta Mapping (SDQDM), which is one of the stochastic downscaling techniques, to conduct the bias correction for climate change scenarios. The comparison between the preapplication and postapplication of the SDQDM supported the study's review on the technique's validity. In addition, as this study determined the influence of climate change on the hydrological cycle, it also observed the runoff in North Korea. In predicting such influence, parameters of a runoff model used for the analysis should be optimized. However, North Korea is classified as an ungauged region for its political characteristics, and it was difficult to collect the country's runoff observation data. Hence, the study selected 16 basins with secured high-quality runoff data, and the M-RAT model's optimized parameters were calculated. The study also analyzed the correlation among variables for basin characteristics to consider multicollinearity. Then, based on a phased regression analysis, the study developed an equation to calculate parameters for ungauged basin areas. To verify the equation, the study assumed the Osipcheon River, Namdaecheon Stream, Yongdang Reservoir, and Yonggang Stream as ungauged basin areas and conducted cross-validation. As a result, for all the four basin areas, high efficiency was confirmed with the efficiency coefficients of 0.8 or higher. The study used climate change scenarios and parameters of the estimated runoff model to assess the changes in hydrological cycle processes at a basin level from climate change in the Amnokgang River of North Korea. The results showed that climate change would lead to an increase in precipitation, and the corresponding rise in temperature is predicted to cause elevating evapotranspiration. However, it was found that the storage capacity in the basin decreased. The result of the analysis on flow duration indicated a decrease in flow on the 95th day; an increase in the drought flow during the periods of Future 1 and Future 2; and an increase in both flows for the period of Future 3.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.