• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.111-118
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• 2017

Performance factors such as the EER(Energy Efficiency Ratio) and the COP (Coefficient of Performance) are being replaced by seasonal energy efficiency factors, like the SEER (Seasonal EER) and the SCOP (Seasonal COP) to evaluate the performance of a heat pump by the time of the year. Seasonal performance factors, such as the CSPF (Cooling Seasonal Performance Factor) and the HSPF (Heating Seasonal Performance Factor) are used to describe the heat pump's performance during the cool and hot seasons. In this study, the optimization of all heat pump's operating parameters was experimentally conducted to enhance the SEER based on the EU standard (EN 14825). Moreover, the SEER was improved by the compressor frequency, as well as indoor and outdoor fan speeds. In addition, the performance characteristics of the heat pump were studied under partial load conditions. As a result, the SEER was enhanced by 17% when the compressor frequency was optimized. An additional 2% improvement was achievable with the optimization of indoor and outdoor fan speeds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Atmosphere
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• v.33 no.5
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• pp.531-548
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• 2023

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACI_SnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACI_YrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACI_SnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACI_YrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACI_SnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACI_YrOpt to ACI_SnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

• Geomechanics and Engineering
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• v.17 no.2
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• pp.133-143
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• 2019

An optimal hydraulic section is critical for irrigated water conservancy in seasonal frozen ground due to a large proportion of water leakage, as investigated by in-situ surveys. This is highly correlated with the frost heave of underlain soils in cold season. This paper firstly derived a practical model for frost heave of clayey soils, with temperature dependent thermal indexes incorporating phase change effect. A model test carried out on clay was used to verify the rationality of the model. A novel approach for optimizing the cross-section of irrigation canals in cold regions was suggested with live updated geometry characterized by three unique geometric constraints including slope of canal, ratio of practical flow section to the optimal and lining thickness. Allowable frost heave deformation and tensile stress in canal lining are utilized as standard in computation iterating with geometry updating while the construction cost per unit length is regarded as the eventual target in optimization. A typical section along the Jinghui irrigation canal was selected to be optimized with the above requirements satisfied. Results prove that the optimized hydraulic section exhibits smaller frost heave deformation, lower tensile stress and lower construction cost.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.23-31
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• 2019

In this study, we collected data through online travel notes from January to December 2018 and analyzed the seasonal travel characteristics of individual visiting Chinese by utilizing social network analysis. The analysis showed that Seoul is a hub for Chinese travel to Korea and the main destinations for individual visiting Chinese are concentrated in Seoul, Busan, Jeju Island, Gyeongju and Gangneung, with wide differences in seasons. The research results can be used as basic data for the development of tourism courses for individual Chinese tourists to Korea, provision of tourism services and optimization of tourism facility layout. Future research can consider continuing to use network travel notes to study the tourist destination and the mode of transportation between tourist nodes, which can provide reference for the development of tourist market and the planning and design of tourist traffic.

• Journal of Environmental Science International
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• v.26 no.10
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• pp.1125-1133
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• 2017

In this study a method for filling in missing data of river water temperature using a pre-constructed mathematical relationship between air and water temperatures is presented. A regression between water temperatures at individual stations and ambient air temperatures at nearby weather stations can provide a practical method for representing missing water temperature data for an entire region. Air and water temperature data that were collected from two test sites (one coastal and, one inland) were individually fitted to a nonlinear regression model. To consider seasonal hysteresis effects, separate functions were fitted to the data in the rising and falling limbs. A single-criterion, multi-parameter optimization technique was used to determine the optimal parameter sets. This method minimizes the differences between the time series of the measured and estimated data. The constructed air-water temperature relationship was subsequently applied to represent missing water temperature data. It was found that the RMSEs(MBEs) were in the range of $1.843-1.976^{\circ}C(-0.329-0.201^{\circ}C)$ and the coefficient of determination were in the range of 0.92-0.96. The results demonstrate that the predicted water temperatures using the regression equations were reasonably accurate.

• Journal of the Korean Society for Marine Environment & Energy
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• v.4 no.2
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• pp.35-42
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• 2001

We demonstrated the importance of initial estimates of model parameters and the utility of an optimization approach of the uncertain forcing of wind-driven circulation off the southeastern coastal waters of Korea. The wind stress represents the upper boundary condition in this model and enters in the model equation as a forcing term in the numerical formalism. The wind field contributes to maintain the almost time-independent distribution of the upper layer thickness feature in a north-south direction and negative wind stress curl to maintain the formation of warm eddy off the southeastern coastal waters of Korea. Elucidated is the variational characteristics of the East Korean Warm Current due to the variations of the zonally averaged wind stress (southward transport) from the seasonal variations of the meridional transport by the Ekman transport.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.221-226
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• 1999

A data assimilation system for a 1-dimensional mixed layer model has been constructed using the adjoint method. The classical adjoint method does not work well for the mixed layer variabilities due to the occurrence of spikes in the gradient of the cost function. To solve this problem, the two techniques of scaling the cost function and optimization in the frequency space are used. As a result, the heat flux can be reliably estimated with an accuracy of 8Wm$^{-2}$ rms error in the identical twin experiments. We then applied this system to the tropical Pacific TOGA-TAO buoy data. The air-sea heat flux as well as the mixed layer variability were estimated in close approximation to the buoy data, particularly on time scales longer than the seasonal one.

• Environmental Engineering Research
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• v.20 no.3
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• pp.230-236
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• 2015

26 multi-regional water treatment plants (WTPs) were investigated, to determine the characteristics of manganese (Mn) concentration and removal in Korea. Mn concentrations of raw water in most WTPs were higher than the drinking water standard (i.e., 0.05 mg/L); thus, proper removal of Mn at the WTPs is needed. Mn concentration was generally higher in lakes than rivers due to seasonal lake turnovers. The Mn concentrations of treated water at 26 WTPs in 2012 were less than 0.05 mg/L, due to strict law enforcement and water treatment processes optimization. However, before 2010, those concentrations were more than 0.05 mg/L, which could have led to an accumulation of Mn oxides in the distribution system. This could be one of the main reasons for black water occurrence. Therefore, regular monitoring of Mn concentration in the distribution system, flushing, and proper Mn removal at WTPs are needed, to supply clean and palatable tap water.