• IE interfaces
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• v.19 no.3
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• pp.255-269
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• 2006

Digital manufacturing(DM) is the ability to describe every aspect of the design-to-manufacture process digitally-using tools that include digital design, CAD, office documents, PLM(Product Life-cycle Management) systems, analysis software, simulation, CAM software and so on. The major automotive companies are already deeply invested in DM with almost every process being digital rather than paper-based. But it has taken a long time for the digital process to mature into something usable and there have been some major barriers that have prevented from the DM becoming a reality. Thus many companies hesitate to make a decision of implementing the DM. This paper deals with a study investigating which factors are important for implementing the DM to industries successfully. The extended technology acceptance model (ETAM) is used as the relation model of cause and effect. The quality of hardware, the quality of software, the range of collaboration among companies and the preference of the user are defined as the external factors. Interview method is used for gathering input data, and the results are analyzed with SPSS. The results indicate that four external factors are effective on the successful implementation of DM, and the perceived usefulness is most important.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.249-255
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• 2019

Type 2 diabetes mellitus(T2DM) is included in metabolic disorders characterized by hyperglycemia, which causes many complications, and requires long-term treatment resulting in massive medical expenses each year. There have been many studies to solve this problem, but the existing studies have not been accurate by learning and predicting the data at specific time point. Thus, this study proposed a model using RNN to increase the accuracy of prediction of T2DM. This work propose a T2DM prediction model based on Korean Genome and Epidemiology study(Ansan, Anseong Korea). We trained all of the data over time to create prediction model of diabetes. To verify the results of the prediction model, we compared the accuracy with the existing machine learning methods, LR, k-NN, and SVM. Proposed prediction model accuracy was 0.92 and the AUC was 0.92, which were higher than the other. Therefore predicting the onset of T2DM by using the proposed diabetes prediction model in this study, it could lead to healthier lifestyle and hyperglycemic control resulting in lower risk of diabetes by alerted diabetes occurrence.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1240-1252
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• 2021

Objective: To compare the accuracy for detecting breast cancer in the diagnostic setting between the use of digital breast tomosynthesis (DBT), defined as DBT alone or combined DBT and digital mammography (DM), and the use of DM alone through a systematic review and meta-analysis. Materials and Methods: Ovid-MEDLINE, Ovid-Embase, Cochrane Library and five Korean local databases were searched for articles published until March 25, 2020. We selected studies that reported diagnostic accuracy in women who were recalled after screening or symptomatic. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to estimate pooled sensitivity and specificity. We compared the diagnostic accuracy between DBT and DM alone using meta-regression and subgroup analyses by modality of intervention, country, existence of calcifications, breast density, Breast Imaging Reporting and Data System category threshold, study design, protocol for participant sampling, sample size, reason for diagnostic examination, and number of readers who interpreted the studies. Results: Twenty studies (n = 44513) that compared DBT and DM alone were included. The pooled sensitivity and specificity were 0.90 (95% confidence interval [CI] 0.86-0.93) and 0.90 (95% CI 0.84-0.94), respectively, for DBT, which were higher than 0.76 (95% CI 0.68-0.83) and 0.83 (95% CI 0.73-0.89), respectively, for DM alone (p < 0.001). The area under the summary receiver operating characteristics curve was 0.95 (95% CI 0.93-0.97) for DBT and 0.86 (95% CI 0.82-0.88) for DM alone. The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses. Conclusion: Use of DBT was more accurate than DM alone for the diagnosis of breast cancer. Women with clinical symptoms or abnormal screening findings could be more effectively evaluated for breast cancer using DBT, which has a superior diagnostic performance compared to DM alone.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• Korean Journal of Computational Design and Engineering
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• v.11 no.1
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• pp.11-19
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• 2006

For several years, a research about the simulation for shipyard and shipbuilding has been performed. This research is based on the concept of PLM (Product Lifecycle Management) and DM (Digital Manufacturing). Global leading companies and research center are trying to get a good position of PLM, especially M&S field. Digital shipbuilding is to computerize shipyard facilities and shipbuilding processes, and to simulate expected scenarios of shipbuilding processes using a computer model in order to resolve a potential problem such as a bottleneck processes, and over loaded resources. In this paper, simulation methodology for shipbuilding is described. In addition, a local and global strategy for the use of simulation methodology is suggested. Finally, case studies about an indoor shop and an outdoor shop are described.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.501-512
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• 2023

This study aimed to assess and determine the optimal model for predicting the full bloom date of 'Fuji' apples across South Korea. We evaluated the performance of four distinct models: the Development Rate Model (DVR)1, DVR2, the Chill Days (CD) model, and a sequentially integrated approach that combined the Dynamic model (DM) and the Growing Degree Hours (GDH) model. The full bloom dates and air temperatures were collected over a three-year period from six orchards located in the major apple production regions of South Korea: Pocheon, Hwaseong, Geochang, Cheongsong, Gunwi, and Chungju. Among these models, the one that combined DM for calculating chilling accumulation and the GDH model for estimating heat accumulation in sequence demonstrated the most accurate predictive performance, in contrast to the CD model that exhibited the lowest predictive precision. Furthermore, the DVR1 model exhibited an underestimation error at orchard located in Hwaseong. It projected a faster progression of the full bloom dates than the actual observations. This area is characterized by minimal diurnal temperature ranges, where the daily minimum temperature is high and the daily maximum temperature is relatively low. Therefore, to achieve a comprehensive prediction of the blooming date of 'Fuji' apples across South Korea, it is recommended to integrate a DM model for calculating the necessary chilling accumulation to break dormancy with a GDH model for estimating the requisite heat accumulation for flowering after dormancy release. This results in a combined DM+GDH model recognized as the most effective approach. However, further data collection and evaluation from different regions are needed to further refine its accuracy and applicability.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.7
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• pp.3-12
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• 2004

The need to predict the rate of soil erosion, both under existing conditions and those expected to occur following soil conservation practice, has been led to the development of various models. In this study Morgan model especially developed for field-sized areas on hill slopes was applied to assess the rate of soil erosion using RS/GIS environment in the Dukchun river basin, one of two tributaries flowing into Jinyang lake. In order to run the model, land cover mapping was made by the supervised classification method with Landsat TM satellite image data, the digital soil map was generated from scanning and screen digitizing from the hard copy of soil maps, digital elevation map (DEM) in order to generate the slope map was made by the digital map (DM) produced by National Geographic Information Institute (NGII). Almost all model parameters were generated to the multiple raster data layers, and the map calculation was made by the raster based GIS software, IL WIS which was developed by ITC, the Netherlands. Model results show that the annual soil loss rates are 5.2, 18.4, 30.3, 58.2 and 60.2 ton/ha/year in forest, paddy fields, built-up area, bare soil, and upland fields respectively. The estimated rates seemed to be high under the normal climatic conditions because of exaggerated land slopes due to DEM generation using 100 m contour interval. However, the results were worthwhile to estimate soil loss in hilly areas and the more precise result could be expected when the more accurate slope data is available.

• Journal of Technologic Dentistry
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• v.41 no.4
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• pp.287-293
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• 2019

Purpose: To evaluate the accuracy of the 3D printed die models and to investigate its clinical applicability. Methods: Stone die models were fabricated from conventional impressions(stone die model; SDM, n=7). 3D virtual models obtained from the digital impressions were manufactured as a 3D printed die models using a 3D printer(3D printed die models;3DM, n=7). Reference model, stone die models and 3D printed die models were scanned with a reference scanner. All dies model dataset were superimposed with the reference model file by the "Best fit alignment" method using 3D analysis software. Statistical analysis was performed using the independent t-test and 2-way ANOVA (α=.05). Results: The RMS value of the 3D printed die model was significantly larger than the RMS value of the stone die model (P<.001). As a result of 2-way ANOVA, significant differences were found between the model group (P<.001) and the part (P<.001), and their interaction effects (P<.001). Conclusion: The 3D printed die model showed lower accuracy than the stone die model. Therefore, it is necessary to further improve the performance of 3D printer in order to apply the 3D printed model in prosthodontics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.8 no.3
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• pp.113-119
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• 1983

In this paper, a method of the step size adaption is studied on the delta modulation coding of speech signals. Exponential adaption processes are reserched by a new circuit model. It is presented a shorten error recovery in decoder step size. Practical considerations favor one algorithm, and its digital implementation has been adapted for the illustration of above method, using the rate multipliers and the validity is verified by laboratory experiment.