• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.385-393
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• 2022

In this paper, we consider univariate time series models that are well known in the field of forecasting and we study on forecasting performance for their simple combinations. The univariate time series models include exponential smoothing methods and ARIMA (autoregressive integrated moving average) models, their extended models, and non-seasonal and seasonal random walk models, which is frequently used as benchmark models for forecasting. The median and mean are simply used for the combination method, and the data set used for performance evaluation is M3-competition data composed of 3,003 various time series data. As results of evaluating the performance by sMAPE (symmetric mean absolute percentage error) and MASE (mean absolute scaled error), we assure that the simple combinations of the univariate models perform very well in the M3-competition dataset.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.661-676
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• 2017

This study suggests a new method not only to produce the 1 km-resolution seasonal prediction but also to improve the seasonal prediction skill of temperature over South Korea. This method consists of four stages of experiments. The first stage, EXP1, is a low-resolution seasonal prediction of temperature obtained from Pusan National University Coupled General Circulation Model, and EXP2 is to produce 1 km-resolution seasonal prediction of temperature over South Korea by applying statistical downscaling to the results of EXP1. EXP3 is a seasonal prediction which considers the effect of temperature changes according to the altitude on the result of EXP2. Here, we use altitude information from ASTER GDEM, satellite observation. EXP4 is a bias corrected seasonal prediction using genetic algorithm in EXP3. EXP1 and EXP2 show poorer prediction skill than other experiments because the topographical characteristic of South Korea is not considered at all. Especially, the prediction skills of two experiments are lower at the high altitude observation site. On the other hand, EXP3 and EXP4 applying the high resolution elevation data based on remote sensing have higher prediction skill than other experiments by effectively reflecting the topographical characteristics such as temperature decrease as altitude increases. In addition, EXP4 reduced the systematic bias of seasonal prediction using genetic algorithm shows the superior performance for temporal variability such as temporal correlation, normalized standard deviation, hit rate and false alarm rate. It means that the method proposed in this study can produces high-resolution and high-quality seasonal prediction effectively.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.7
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• pp.557-563
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• 2001

Small capacity gas absorption systems for cooling and heating have been favorably considered to reduce the seasonal imbalance of electrical loads and LNG consumption recently. A multifunctional plate-fin heat exchanger was adopted as an absorber and the performance was tested and analyzed to reduce the size and weight of the absorption heat pump. The test was performed using breadboard type ammonia absorption machine. The performance was compared with the plate type absorber and there was little difference in heat and mass transfer characteristics. The heat and mass transfer performance was a function of poor solution and vapor flow rates and the mass transfer was dependent on vapor flow rate more than heat transfer.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.4
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• pp.15-20
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• 2009

The purpose of this study was to test the applicability of the HSPEXP model performance criteria for calibrating hydrologic parameters of HSPF. Baran watershed, located at Whasung city, was selected as a study watershed in this study. Input data for the HSPF model were obtained from the digital elevation map, landuse map, soil map and others. Water flow data from 1996 to 2000 was used for calibration and from 2002 to 2007 was for validation. Using the HSPEXP decision-support software, hydrology parameters were adjusted based on total volume, then low flows, storm flows, and finally seasonal flows. Suggested criteria for each model performance variables were referenced from the previous research. For the calibration period, all the HSPEXP model performance criteria were satisfied while two criteria were slightly violated for the validation period.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.334-338
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• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.

• Journal of Animal Reproduction and Biotechnology
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• v.35 no.2
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• pp.149-154
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• 2020

The aim of the current study was to compare the effects of season and breed on the reproductive performance of male and female sheep using 12 rams and 318 ewes of Assaf and Awassi breeds under the seasonal environmental condition of United Arab Emirates for two years. The blood level of testosterone hormone was measured monthly. Semen was collected twice a month from each male using artificial vagina and evaluated for volume, motility, livability, abnormality and concentration. The scrotal circumference and thickness as well as the left testicular length, width, height and volume were measured at one-month intervals. The level of testosterone in Assaf breed was significantly higher in autumn than winter and summer. The scrotal circumference and thickness as well as the left testicular length were significantly higher in Assaf breed than Awassi breed. While, left testicular width and volume were significantly higher in Awassi breed than Assaf breed. Scrotal circumference which was higher in spring and summer than in autumn and winter season in both breeds. The SCC of semen was significantly higher in autumn than in other seasons in both breeds. The sperm abnormality was significantly higher in summer than other seasons in both breeds. The livability was significantly lower in summer in both breeds. Fecundity and prolificacy were significantly higher in Assaf than Awassi breed during autumn season. Assaf breed showed the superior reproductive performance in the autumn season when compared with Awassi breed in the same season and other seasons. The Assaf breed tolerated the climatic conditions in UAE and kept the litter size of 1.72 in comparison to Awassi breed which showed litter size of 1.09. in conclusion, the results showed the superiority of Assaf over Awassi breed and offer a good model of breeding with increased fecundity and prolificacy specially in autumn season.

• Journal of Soil and Groundwater Environment
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• v.21 no.3
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• pp.64-81
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• 2016

To analyze the influence of various groundwater flow rates (specific discharge) on BHE system with balanced and unbalanced energy loads under assuming same initial temperature (15℃) of ground and groundwater, numerical modeling using FEFLOW was used for this study. When groundwater flow is increased from 1 × 10⁻⁷ to 4 × 10⁻⁷m/s under balanced energy load, the performance of BHE system is improved about 26.7% in summer and 22.7% at winter time in a single BHE case as well as about 12.0~18.6% in summer and 7.6~8.7% in winter time depending on the number of boreholes in the grid, their array type, and bore hole separation in multiple BHE system case. In other words, the performance of BHE system is improved due to lower avT in summer and higher avT in winter time when groundwater flow becomes larger. On the contrary it is decreased owing to higher avT in summer and lower avT in winter time when the numbers of BHEs in an array are increased, Geothermal plume created at down-gradient area by groundwater flow is relatively small in balanced load condition while quite large in unbalanced load condition. Groundwater flow enhances in general the thermal efficiency by transferring heat away from the BHEs. Therefore it is highly required to obtain and to use adequate informations on hydrogeologic characterristics (K, S, hydraulic gradient, seasonal variation of groundwater temperature and water level) along with integrating groundwater flow and also hydrogeothermal properties (thermal conductivity, seasonal variation of ground temperatures etc.) of the relevant area for achieving the optimal design of BHE system.

• Atmosphere
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• v.29 no.2
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• pp.149-164
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• 2019

The purpose of this study is to introduce the improvement of current operational climate prediction system of KMA and to compare previous and improved that. Whereas the previous system is based on GloSea5GA3, the improved one is built on GloSea5GC2. GloSea5GC2 is a fully coupled global climate model with an atmosphere, ocean, sea-ice and land components through the coupler OASIS. This is comprised of component configurations Global Atmosphere 6.0 (GA6.0), Global Land 6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0 (GSI6.0). The compositions have improved sea-ice parameters over the previous model. The model resolution is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, the predictability of each system is evaluated using by RMSE, Correlation and MSSS, and the variables are 500 hPa geopotential height (h500), 850 hPa temperature (t850) and Sea surface temperature (SST). A predictive performance shows that GloSea5GC2 is better than GloSea5GA3. For example, the RMSE of h500 of 1-month forecast is decreased from 23.89 gpm to 22.21 gpm in East Asia. For Nino3.4 area of SST, the improvements to GloSeaGC2 result in a decrease in RMSE, which become apparent over time. It can be concluded that GloSea5GC2 has a great performance for seasonal prediction.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.997-1021
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• 2010

This paper compares the two most widely used seasonal adjustment methods: the X-12-ARIMA and TRAMO-SEATS procedures. The basic features of these methods are discussed and compared in both their theoretical and empirical aspects. In doing so, the X-13A-S program is used to reevaluate their applicability to Korean macroeconomic data by considering possible structural breaks in the series. The finding is that both methods provide very reliable and stable estimates of seasonal factors and seasonally adjusted data. As for the empirical comparisons, TRAMO-SEATS appears to outperform X-12-ARIMA, although the results are somewhat mixed depending on the comparison criteria used and on the series under analysis. In particular, the performance of TRAMO-SEATS turns out to compare more favorably when seasonal adjustment is carried out to each sub-samples (by taking possible structural breaks into account) than when the whole sample period is used. The result suggests that as the model-based TRAMO-SEATS has a considerable theoretical appeal, some features of TRAMO-SEATS should further be incorporated into X-12-ARIMA until a standard and integrated procedure is reached by combining the theoretical coherence of TRAMO-SEATS and the empirical usefulness of X-12-ARIMA.