• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.439-439
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• 2015

The temporal and spatial relationship of the weather elements such as rainfall and temperature is closely linked to the streamflow simulation, especially, to the flood forecasting problems. For the study area, Imjin river basin, which has the specific characteristics in geography with river cross operation between North and South Korea, the meteorological information in the northern area is totally deficiency, lead to the inaccuracy of streamflow estimation. In the paper, this problem is solved by using the combination of global (such as soil moisture content, land use) and local hydrologic components data such as weather data (precipitation, evapotranspiration, humidity, etc.) for the model-driven runoff (surface flow, lateral flow and groundwater flow) data in each subbasin. To compute the streamflow in Imjin river basin, this study is applied the hydrologic model SURR (Sejong Univ. Rainfall-Runoff) which is the continuous rainfall-runoff model used physical foundations, originally based on Storage Function Model (SFM) to simulate the intercourse of the soil properties, weather factors and flow value. The result indicates the spatial variation in the runoff response of the different subbasins influenced by the input data. The dependancy of runoff simulation accuracy depending on the qualities of input data and model parameters is suggested in this study. The southern region with the dense of gauges and the adequate data shows the good results of the simulated discharge. Eventually, the application of SURR model in Imjin riverbasin gives the accurate consequence in simulation, and become the subsequent runoff for prediction in the future process.

• Journal of Korean Society on Water Environment
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• v.22 no.4
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• pp.666-671
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• 2006

Effects of a rainfall event (July 28, 2005) on the hydrochemical characteristics of the Jungrang river, the biggest tributary of the Han river, was investigated. Significant spatial variations in the hydrochemical characteristics were observed. At JR2 location, concentrations of T-N and T-P were relatively low indicating occurrence of active oxidation in the stepped drop structure. At JR3 location, concentrations of Na, K, Cl, $NH_4-N$ and EC were elevated suggesting increased discharge from the nearby waste-water treatment plant and tributaries. The rain event diluted major dissolved ion concentrations in the river by 12~52%. The $NO_3-N$ levels were preserved during the rain then increased about twofold after rainfall, suggesting increased discharge of nitrate-contaminated groundwater. Heavy metals including Cd, Co, Cr, Cu and Pb were not detected in all water samples and the leachates from surface sediment samples. Concentrations of Fe, Mn, Al and Zn were below the Korean Drinking Water Guideline. Results of this study suggested that establishment of water-quality monitoring protocols describing temporal and spatial variations in parameters sensitive to rainfall events, relatively steady factors, and contaminant sources is required.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.195-201
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• 2019

The Outgoing Longwave Radiation (OLR) is an important satellite-driven variable for understanding the Earth's energy budget balance. The geostationary OLR retrievals require angular and spectral integration using an empirical equation for irradiance flux-to-OLR from a regression analysis, which determines the accuracy of the narrowband satellite-based OLR. We selected homogeneous pixels which is satisfied less temporal-spatial variability of cloud, on three infrared channels (6.7, 10.8, $12.0{\mu}m$) of the first multipurpose geostationary satellite in Korea, namely the Communication, Ocean and Meteorological Satellite/Meteorological Imager (COMS/MI). Multiple regression analysis was performed to retrieve OLR with improved accuracy using selected parameters based on theoretical and physical significance. This algorithm yielded retrieval with higher accuracy than broadband-based OLR retrieval: RMSE of 10.54 to $3.81W\;m^{-2}$, and bias of -8.49 to $-0.07W\;m^{-2}$.

• ETRI Journal
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• v.33 no.5
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• pp.759-769
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• 2011

Cooperative hybrid-automatic repeat request (HARQ) protocols, which can exploit the spatial and temporal diversities, have been widely studied. The efficiency of cooperative HARQ protocols is higher than that of cooperative protocols because retransmissions are only performed when necessary. We classify cooperative HARQ protocols as three decode-and-forward-based HARQ (DF-HARQ) protocols and two amplified-and-forward-based HARQ (AF-HARQ) protocols. To compare these protocols and obtain the optimum parameters, two unified frameworks are developed for protocol analysis. Using the frameworks, we can evaluate and compare the maximum throughput and outage probabilities according to the SNR, the relay location, and the delay constraint. From the analysis we can see that the maximum achievable throughput of the DF-HARQ protocols can be much greater than that of the AF-HARQ protocols due to the incremental redundancy transmission at the relay.

• The Journal of Korean Physical Therapy
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• v.18 no.1
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• pp.95-106
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• 2006

Purpose: The aim of this study was to investigate the kinematics of young adults during ramp climbing at different inclinations. Methods: Twenty-three subjects ascended and descended four steps at four different inclinations(level, $8^{\circ},\;16^{\circ},\;24^{\circ}$). Temporal-spatial parameters were measured by GaitRite system(standard mat). Groups difference was analysed with on-way ANOVA and Student-Newman-Keuls test. Results: The different kinematics of ramp ascent and descent were analysed and compared to level walking patterns. In ascending ramps, step length and stride length decreased with $24^{\circ}$ inclination (p<.000). Stance duration increased with $24^{\circ}$, but swing duration decreased with $24^{\circ}$ inclination (p<.000). Step time and velocity decreased with $16^{\circ}C,\;24^{\circ}$ inclination (p<.000). Cadence decreased with all inclination($8^{\circ},\;16^{\circ},\;24^{\circ}$)(p<.000). In descending ramps, step length and stride length, velocity decreased with all inclination($8^{\circ},\;16^{\circ},\;-24^{\circ}$)(p<.000). Stance duration increased with all inclination($8^{\circ},\;-16^{\circ},\;-24^{\circ}$) and swing duration decreased with all inclination($-8^{\circ},\;-16^{\circ},\;-24^{\circ}$)(p<.000). But Step time was not differentiated with different inclinations. Cadence decreased with only. $8^{\circ}$ inclination(p<.05). Conclusion: These results suggest that there is a certain inclination angle or angular range where subjects do switch between level walking and ramp walking gait pattern. This shows their motor control strategy between level and ramp walking. Further studies are necessary to confirm and detect the ascent and descent ramp gait patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3479-3492
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• 2022

Longitudinal quantification of brain changes due to development, aging or disease plays an important role in the filed of personalized-medicine applications. However, due to the temporal variability in shape and different imaging equipment and parameters, estimating anatomical changes in longitudinal studies is significantly challenging. In this paper, a longitudinal Magnetic Resonance(MR) brain image segmentation algorithm proposed by combining intensity information and anisotropic smoothness term which contain a spatial smoothness constraint and longitudinal consistent constraint into a variational framework. The minimization of the proposed energy functional is strictly and effectively derived from a fast optimization algorithm. A large number of experimental results show that the proposed method can guarantee segmentation accuracy and longitudinal consistency in both simulated and real longitudinal MR brain images for analysis of anatomical changes over time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1614-1632
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• 2017

Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.

• Journal of Mechanical Science and Technology
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• v.20 no.5
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• pp.692-709
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• 2006

Nucleate pool boiling experiments with constant wall temperature were performed using pure R1l3 for subcooled, saturated, and superheated pool conditions. A microscale heater array and Wheatstone bridge circuits were used to maintain the constant wall temperature and to measure the instantaneous heat flow rate accurately with high temporal and spatial resolutions. Images of bubble growth were taken at 5,000 frames per second using a high-speed CCD camera synchronized with the heat flow rate measurements. The bubble geometry was obtained from the captured bubble images. The effect of the pool conditions on the bubble growth behavior was analyzed using dimensionless parameters for the initial and thermal growth regions. The effect of the pool conditions on the heat flow rate behavior was also examined. This study will provide good experimental data with precise constant wall temperature boundary condition for such works.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.5 s.23
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• pp.29-43
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• 2002

Accesses to lands, houses, the Internet and other utilities are regarded as basic human needs to escape poverty and are required to recognize the relationships between the digital divide and socio-economical inequality. The digital divide might not be a single technical disparity, but stems from a complicated mixture of economic and socio-technical symptoms of inequality. There is growing awareness of scrutinizing causal mechanisms between the digital divide and poverty since combating poverty could be a primary step to mitigate the digital divide. In this paper, the Hexad model is proposed to explicate poverty interpretation by using 6 parameters as a major tool for partly assisting in poverty monitoring system in connection with land information. A solution model of the Internet is suggested to break the digital divide. It expounds a conceptual framework and new idea for poverty management to notify spatio-temporal locations of poor actors and geography of the digital divide when efforts of poverty eradication hinge on understandings of geographic location of digital disadvantaged groups through parcel-based land information.

• ETRI Journal
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• v.44 no.4
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• pp.624-640
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• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.