• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.322-340
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• 2021

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.65-70
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• 2005

Those specifications of the CLIA analytical tolerance limits are consistent with the performance goals in Six Sigma Quality Management. Six sigma analysis determines performance quality from bias and precision statistics. It also shows if the method meets the criteria for the six sigma performance. Performance standards calculates allowable total error from several different criteria. Six sigma means six standard deviations from the target value or mean value and about 3.4 failures per million opportunities for failure. Sigma Quality Level is an indicator of process centering and process variation total error allowable. Tolerance specification is replaced by a Total Error specification, which is a common form of a quality specification for a laboratory test. The CLIA criteria for acceptable performance in proficiency testing events are given in the form of an allowable total error, TEa. Thus there is a published list of TEa specifications for regulated analytes. In terms of TEa, Six Sigma Quality Management sets a precision goal of TEa/6 and an accuracy goal of 1.5 (TEa/6). This concept is based on the proficiency testing specification of target value +/-3s, TEa from reference intervals, biological variation, and peer group median mean surveys. We have found rules to calculate as a fraction of a reference interval and peer group median mean surveys. We studied to develop total error allowable from peer group survey results and CLIA 88 rules in US on 19 items TP, ALB, T.B, ALP, AST, ALT, CL, LD, K, Na, CRE, BUN, T.C, GLU, GGT, CA, phosphorus, UA, TG tests in chematology were follows. Sigma level versus TEa from peer group median mean CV of each item by group mean were assessed by process performance, fitting within six sigma tolerance limits were TP ($6.1{\delta}$/9.3%), ALB ($6.9{\delta}$/11.3%), T.B ($3.4{\delta}$/25.6%), ALP ($6.8{\delta}$/31.5%), AST ($4.5{\delta}$/16.8%), ALT ($1.6{\delta}$/19.3%), CL ($4.6{\delta}$/8.4%), LD ($11.5{\delta}$/20.07%), K ($2.5{\delta}$/0.39mmol/L), Na ($3.6{\delta}$/6.87mmol/L), CRE ($9.9{\delta}$/21.8%), BUN ($4.3{\delta}$/13.3%), UA ($5.9{\delta}$/11.5%), T.C ($2.2{\delta}$/10.7%), GLU ($4.8{\delta}$/10.2%), GGT ($7.5{\delta}$/27.3%), CA ($5.5{\delta}$/0.87mmol/L), IP ($8.5{\delta}$/13.17%), TG ($9.6{\delta}$/17.7%). Peer group survey median CV in Korean External Assessment greater than CLIA criteria were CL (8.45%/5%), BUN (13.3%/9%), CRE (21.8%/15%), T.B (25.6%/20%), and Na (6.87mmol/L/4mmol/L). Peer group survey median CV less than it were as TP (9.3%/10%), AST (16.8%/20%), ALT (19.3%/20%), K (0.39mmol/L/0.5mmol/L), UA (11.5%/17%), Ca (0.87mg/dL1mg/L), TG (17.7%/25%). TEa in 17 items were same one in 14 items with 82.35%. We found out the truth on increasing sigma level due to increased total error allowable, and were sure that the goal of setting total error allowable would affect the evaluation of sigma metrics in the process, if sustaining the same process.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.89-97
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• 2012

In this paper, we propose a novel approach to noise power estimation for speech enhancement in noisy environments. The method based on IMCRA (improved minima controlled recursive averaging) which is widely used in speech enhancement utilizes a rough VAD (voice activity detection) algorithm which excludes speech components during speech periods in order to improves the performance of the noise power estimation by reducing the speech distortion caused by the conventional algorithm based on the minimum power spectrum derived from the noisy speech. However, since the VAD algorithm is not sufficient to distinguish speech from noise at non-stationary noise and low SNRs (signal-to-noise ratios), the speech distortion resulted from the minimum tracking during speech periods still remained. In the proposed method, minimum power estimate obtained by IMCRA is modified by SMT (spectral minima tracking) to reduce the speech distortion derived from the bias of the estimated minimum power. In addition, in order to effectively estimate minimum power by considering the distribution characteristic of the speech and noise spectrum, the presented method combines the minimum estimates provided by IMCRA and SMT depending on the weighting factor based on the subband. Performance of the proposed algorithm is evaluated by subjective and objective quality tests under various environments and better results compared with the conventional method are obtained.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.365-382
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• 2016

The results from the Global Positioning System (GPS) measurements of the Mobile Observation Vehicle (MOVE) on the eastern coast of Korea have been compared with REFerence (REF) values from the fixed GPS sites to assess the performance of Precipitable Water Vapor (PWV) retrievals in a kinematic environment. MOVE-PWV retrievals had comparatively similar trends and fairly good agreements with REF-PWV with a Root-Mean-Square Error (RMSE) of 7.4 mm and $R^2$ of 0.61, indicating statistical significance with a p-value of 0.01. PWV retrievals from the June cases showed better agreement than those of the other month cases, with a mean bias of 2.1 mm and RMSE of 3.8 mm. We further investigated the relationships of the determinant factors of GPS signals with the PWV retrievals for detailed error analysis. As a result, both MultiPath (MP) errors of L1 and L2 pseudo-range had the best indices for the June cases, 0.75-0.99 m. We also found that both Position Dilution Of Precision (PDOP) and Signal to Noise Ratio (SNR) values in the June cases were better than those in other cases. That is, the analytical results of the key factors such as MP errors, PDOP, and SNR that can affect GPS signals should be considered for obtaining more stable performance. The data of MOVE can be used to provide water vapor information with high spatial and temporal resolutions in the case of dramatic changes of severe weather such as those frequently occurring in the Korean Peninsula.

• Journal of Microbiology and Biotechnology
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• v.19 no.7
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• pp.718-726
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• 2009

The objective of this study was to develop and validate secondary models that can predict growth parameters of L. monocytogenes Scott A as a function of concentrations (0-3%) of a commercial potassium lactate (PL) and sodium diacetate (SDA) mixture, pH (5.5-7.0), and temperature (4-37DC). A total of 120 growth curves were fitted to the Baranyi primary model that directly estimates lag time (LT) and specific growth rate (SGR). The effects of the variables on L. monocytogenes Scott A growth kinetics were modeled by response surface analysis using quadratic and cubic polynomial models of the natural logarithm transformation of both LT and SGR. Model performance was evaluated with dependent data and independent data using the prediction bias ($B_f$) and accuracy factors ($A_f$) as well as the acceptable prediction zone method [percentage of relative errors (%RE)]. Comparison of predicted versus observed values of SGR indicated that the cubic model fits better than the quadratic model, particularly at 4 and $10^{\circ}C$. The $B_f$and $A_f$for independent SGR were 1.00 and 1.08 for the cubic model and 1.08 and 1.16 for the quadratic model, respectively. For cubic and quadratic models, the %REs for the independent SGR data were 92.6 and 85.7, respectively. Both quadratic and cubic polynomial models for SGR and LT provided acceptable predictions of L. monocytogenes Scott A growth in the matrix of conditions described in the present study. Model performance can be more accurately evaluated with $B_f$and $A_f$and % RE together.

• Atmosphere
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• v.25 no.4
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• pp.639-657
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• 2015

There is an increasing need to improve the air quality over South Korea to protect public health from local and remote anthropogenic pollutant emissions that are in an increasing trend. Here, we evaluate the performance of the WRF-Chem (Weather Research and Forecasting-Chemistry) model in simulating near-surface air quality of major Korean cities, and investigate the impacts of time-varying chemical initial and lateral boundary conditions (IC/BCs) on the air quality simulation using a chemical downscaling technique. The model domain was configured over the East Asian region and anthropogenic MICS-Asia 2010 emissions and biogenic MEGAN-2 emissions were applied with RACM gaseous chemistry and MADE/SORGAM aerosol mechanism. Two simulations were conducted for a 30-days period on April 2010 with chemical IC/BCs from the WRF-Chem default chemical species profiles ('WRF experiment') and the MOZART-4 (Model for OZone And Related chemical Tracers version 4) ('WRF_MOZART experiment'), respectively. The WRF_MOZART experiment has showed a better performance to predict near-surface CO, $NO_2$, $SO_2$, and $O_3$ mixing ratios at 7 major Korean cities than the WRF experiment, showing lower mean bias error (MBE) and higher index of agreement (IOA). The quantitative impacts of the chemical IC/BCs have depended on atmospheric residence time of the pollutants as well as the relative difference of chemical mixing ratios between the WRF and WRF_MOZART experiments at the lateral boundaries. Specifically, the WRF_MOZART experiment has reduced MBE in CO and O3 mixing ratios by 60~80 ppb and 5~10 ppb over South Korea than those in the WRF-Chem default simulation, while it has a marginal impact on $NO_2$ and $SO_2$ mixing ratios. Without using MOZART-4 chemical IC, the WRF simulation has required approximately 6-days chemical spin-up time for the East Asian model domain. Overall, the results indicate that realistic chemical IC/BCs are prerequisite in the WRF-Chem simulation to improve a forecast skill of local air quality over South Korea, even in case the model domain is sufficiently large to represent anthropogenic emissions from China, Japan, and South Korea.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.08a
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• pp.120-120
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• 2011

Graphene is a carbon based material and it has great potential of being utilized in various fields such as electronics, optics, and mechanics. In order to develop graphene-based logic systems, graphene field-effect transistor (GFET) has been extensively explored. GFET requires supporting devices, such as volatile memory, to function in an embedded logic system. As far as we understand, graphene has not been studied for volatile memory application, although several graphene non-volatile memories (GNVMs) have been reported. However, we think that these GNVM are unable to serve the logic system properly due to the very slow program/read speed. In this study, a GVM based on the GFET structure and using an engineered graphene channel is proposed. By manipulating the deposition condition, charge traps are introduced to graphene channel, which store charges temporarily, so as to enable volatile data storage for GFET. The proposed GVM shows satisfying performance in fast program/erase (P/E) and read speed. Moreover, this GVM has good compatibility with GFET in device fabrication process. This GVM can be designed to be dynamic random access memory (DRAM) in serving the logic systems application. We demonstrated GVM with the structure of FET. By manipulating the graphene synthesis process, we could engineer the charge trap density of graphene layer. In the range that our measurement system can support, we achieved a high performance of GVM in refresh (>10 ${\mu}s$) and retention time (~100 s). Because of high speed, when compared with other graphene based memory devices, GVM proposed in this study can be a strong contender for future electrical system applications.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.3
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• pp.119-128
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• 2014

One of the theoretically and empirically grounded black boxes in HRD and firm performance link is employee' attitudes such as organizational commitment and job satisfaction. However, most studies were conducted with the regression analysis at the organizational level. This study used HLM(hierarchical linear modeling) analysis, which made it possible to estimate more accurate relationship between variables that were measured from two different levels. In addition, this study attempted to open an the black box(learning culture) in the relationship between HRD and employee attitudes. The result showed that the HRD have a positive effect on the organizational commitment and the job satisfaction. Also the HRD showed full mediation effect of organization commitment and the job satisfaction on the Learning culture. And the result showed that the HRD in 2007 have a positive effect on employee' attitudes in 2009. These findings concluded that systematic HRD like employee's education and training must be built and also the positive culture for employee's learning like support of management's learning organization must be improved in order to promote the organizational performance(organizational commitment, job satisfaction) in company.

• Journal of Korea Water Resources Association
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• v.48 no.6
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• pp.491-500
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• 2015

In order to improve the runoff estimation accuracy of the Natural Resources Conservation Service (NRCS) curve number (CN) model, this study incorporated rainfall and maximum potential retention as contributors for initial abstraction. The modification was proposed based on 658 rank-order data of rainfall and subsequent runoff from 15 watersheds. The NRCS-CN model (M1), one of its inspired modified model (M2), and the proposed model (M3) were analyzed employing different CN approaches. Using tabulated, calculated and least squares fitted CNs ($CN_T$, $CN_C$, $CN_{LSF}$, respectively), the models' performances were evaluated based on Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS). Applications of model M1, M2, and M3, respectively exhibited watershed cumulative mean [RMSE (23.60, 18.12, 16.04), NSE (0.54, 0.73, 0.79), and PBIAS (36.54, 20.25, 12.00)]. Similarly, using CNC (for M1 and M2 model) and $CN_{LSF}$ (for M3 model), the performance of three models respectively were assessed based on watershed cumulative mean [RMSE (17.17, 15.88, 13.82), NSE (0.76, 0.80, 0.85), and PBIAS (3.06, 4.47, 0.11)]. The proposed model (M3) that linked all of the NRCS-CN variants showed more statistically significant agreement between the observed and estimated data.