• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.171-184
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• 2020

Major League Baseball (MLB) records and releases the trajectory data for every baseball pitch, called the PITCHf/x, using three high-speed cameras installed in every stadium. In a previous study, the quality of the pitch was assessed as the expected number of bases yielded using PITCHf/x data. However, the number of bases yielded does not always lead to baseball scores, or runs. In this paper, we assess the quality of a pitch by combining baseball analytics metric Run Expectancy and Run Value using a Random Forests model. We compare the quality of pitches evaluated with Run Value to the quality of pitches evaluated with the expected number of bases yielded.

• Journal of Korean Gerontological Nursing
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• v.20 no.3
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• pp.193-203
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• 2018

Purpose: This study was done to develop a predictive model for quality of life of elderly residents in long-term care facilities (LTCF). The study was based on Brofenbrenner's ecological system theory and a literature review. Methods: Data were collected using a convenience sample of 205 elderly residents in 2 nursing homes located in D city and 1 nursing home located in K province. The exogenous variables were individual factors, family support, and facility environmental factors. The endogenous variables were self-esteem, accommodation adaptation and quality of life of elderly residents in LTCF. Collected data were analyzed through structural equation modeling using AMOS 20.0. Results: Eleven of the twelve hypotheses were supported, but the hypothesis that facility environment factors effect self-esteem was not supported. Quality of life of elderly residents in LTCF was explained first by facility environmental factors, followed by self-esteem, individual factors, accommodation adaptation, and family support with an explanatory power of 83.0%. Conclusion: To improve the quality of life of elderly residents in LTCF, the service and environment preparation provided by facilities is important, and it is necessary to provide emotional counseling to improve the self-esteem of these elders.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.93-103
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• 2022

The main purpose of this study was to gauge the patient satisfaction index and subsequently discuss the Importance-Performance (IP) matrix analysis of the inpatient services in the context of the private hospital setting. The Malaysian Customer Satisfaction Index Model was employed as the theoretical framework for the above purposes. This study involving 242 patients in Malaysian's private healthcare sector used a Web-based survey as the main method of data collection. Partial least square structural equation modeling (PLS-SEM) was utilized for data analysis. Using Fornell et al. (1996)'s formula, the resulting patient satisfaction index was slightly lower than the "very satisfied" category, the target level required for positioning as one of the world's premier medical tourism players. The IP matrix showed that medical quality is the main competitive advantage of the private hospitals that can propel their growth in the global healthcare marketplace. The results also indicate that outcome quality, patient rights, and privacy, and service quality are the three quality domains that need to be prioritized for further improvement. On the other hand, the servicescape quality domain needs to be strategized as the unique selling proposition as the performance of the private hospitals in this regard is already extremely good.

• Korean Journal of Construction Engineering and Management
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• v.13 no.4
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• pp.3-15
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• 2012

Currently, Building Information Modeling (BIM) is an emerging technological shift that involves applying and maintaining an integral digital representation of all building information for different phases of the project life-cycle. Although BIM has been recognized as a main stream in the construction industry, it is difficult to ensure the quality of BIM data due to the problems such as internal errors of BIM tools and lack of guidelines considering design quality. Such problems give lots of difficulties to BIM adoption for designers, constructors, and owners. Therefore, it is necessary to develop the requirements including the object-oriented modeling method and property definition by applying the Industry Foundation Classes (IFC) which is an ISO/PAS 16739 standard for ensuring the quality of BIM data. This research aims at proposing the requirements considering the quality of BIM data and demonstrating the efficiency of the requirements with using the Solibri Model Checker (SMC) which is a quality control tool in a case study.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.9
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• pp.22-32
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• 1997

This paper proposes an imprecise compuitation model for DCT considering QOS of images and a two dimensional DCT architecture for imprecise computations. In case that many processes are scheduling in a hard real time system, the system resources are shared among them. Thus all processes can not be allocated enough system resources (such as processing power and communication bandwidth). The imprecise computtion model can be used to provide scheduling flexibility and various QOS(quality of service)levels, to enhance fault tolerance, and to ensure service continuity in rela time systems. The DCT(discrete cosine transform) is known as one of popular image data compression techniques and adopted in JPEG and MPEG algorithms since the DCT can remove the spatial redundancy of 2-D image data efficiently. Even though many commercial data compression VLSI chips include the DCST hardware, the DCT computation is still a very time-consuming process and a lot of hardware resources are required for the DCT implementation. In this paper the DCT procedure is re-analyzed to fit to imprecise computation model. The test image is simulated on teh base of this model, and the computation time and the quality of restored image are studied. The row-column algorithm is used ot fit the proposed imprecise computation DCT which supports pipeline operatiions by pixel unit, various QOS levels and low speed stroage devices. The architecture has reduced I/O bandwidth which could make its implementation feasible in VLSI. The architecture is proved using a VHDL simulator in architecture level.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.408-413
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• 2015

The quality prediction of the semiconductor industry has been widely recognized as important and critical for quality improvement and productivity enhancement. The main objective of this paper is to predict the final quality of semiconductor chips based on fail bit count information obtained from probe tests. Our proposed method consists of solving the data imbalance problem, non-parametric variable selection, and adjusting the parameters of the model. We demonstrate the usefulness and applicability of the proposed procedure using a real data from a semiconductor manufacturing.

• Journal of Korea Water Resources Association
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• v.37 no.10
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• pp.857-868
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• 2004

Since the self-purification capacity of rivers in Korea is significantly controlled by environmental maintenance flow supplied by upstream reservoirs during drought season, it is obviously important to operate the river and reservoir systems considering not only water quantity aspect but also conservation of downstream water quality and ecosystem. In this study, an unsteady river water quality model KORIVl- WIN was developed as a tool for evaluating the impact. of reservoir operations on the downstream water quality. The model parameters were calibrated and verified using field data obtained in Geum River on September and October of 2002, respectively. Intensive data sampling was performed on November 22, 2003 to investigate the effect of a short-term flushing discharge of Daecheong Reservoir, which increased outflow from 30 $m^3$/s to 200 $m^3$/s for 6 hours, on downstream water quality. The model performance was evaluated by comparing simulated results with observed data including hydraulics, biochemical oxygen demand(BOD$_{5}$), nitrogen and phosphorus species during the flushing event. It showed very good performance in predicting the travel time of flushing flow and water quality variations of dissolved forms of nitrogen and phosphorus species, while revealed large deviations for BOD$_{5}$ possibly due to missing the effect of organic matters resuspension from river bottom sediment during the wave front passage.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.118-124
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• 2023

Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gastroscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high quality data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addition, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.