• 산업공학
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• 제19권3호
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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.

• 디지털융복합연구
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• 제17권8호
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• pp.249-255
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• 2019

제2형 당뇨병은 고혈당이 특징인 대사성 분비 장애로 여러 합병증을 야기하는 질병이며, 장기적인 치료가 필요하기 때문에 매년 많은 의료비를 지출한다. 이를 해결하기 위해 많은 연구들이 있어왔지만, 기존의 연구들은 한 시점에서의 데이터를 학습시켜 예측함으로써 정확도가 높지 않았다. 그래서 본 연구는 제2형 당뇨병 발생 예측에 대한 정확도를 높이기 위하여 RNN을 이용한 모델을 제안하였다. 본 모델을 개발하기 위해 한국인유전체역학조사 지역사회 코호트(안산 안성) 데이터를 이용하였으며, 시간의 흐름에 따른 데이터들을 모두 학습시켜 당뇨병 발생 예측모델을 만들었다. 예측 모델의 성능을 검증하기 위해 기존의 기계 학습 방법인 LR, k-NN, SVM과 정확도를 비교하였다. 비교한 결과 제안한 예측모델의 accuracy는 0.92, AUC는 0.92로 다른 기계 학습 방법보다 높은 정확도를 보였다. 따라서 본 연구에서 제안한 제2형 당뇨병 발생 예측 모델을 활용하여 발병을 조기 예측함으로써 생활습관 개선 및 혈당조절을 통해 당뇨병 발병을 예방하고 늦출 수 있을 것이다.

• Korean Journal of Radiology
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• 제22권8호
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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.

• 디지털융복합연구
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• 제18권8호
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• pp.231-242
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• 2020

연구의 목적은 가정에서 안정 시 인체의 생리적 활력 정보를 센서와 ICT 정보 기술을 통해 연속적으로 수집하는 시스템과 수집된 정보를 이용하여 당뇨병증 유무를 예측하는 인공신경망 기계학습 방법과 필수적인 기본 변수 값을 제시하였다. 연구 방법은 정상인(DM-) 20명과 당뇨병(DM+) 15명을 대상으로 BCG와 ECG 센서의 심박수 측정값의 상관 관계를 분석하였으며 상관 계수는 R²=0.959이다. Artificial Neural Network(ANN) 기계학습 프로그램을 이용하여 당뇨병증 예측 가능성을 확인하였고 입력 변수는 심박변이도의 시계열정보와 심박수, 심박변이도, 호흡율, 박동량 정보, 최저혈압, 최고혈압, 년령, 성별이며 ANN 기계학습 예측 정확도는 99.53%이다. 그리고 향후 ANN 기계학습 방법을 활용하여 BMI 정보를 이용한 당뇨예측 모델, 심장 기능 장애 예측 모델, 수면장애 분석 모델 등의 계속적인 연구가 필요하다.

• 한국CDE학회논문집
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• 제11권1호
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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.

• 생물환경조절학회지
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• 제32권4호
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• pp.501-512
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• 2023

사과 '후지' 만개일의 예측을 위해 전국적으로 활용이 가능한 모델을 선발하기 위해 우리나라에서 사용된 사례가 있는 대표적인 모델 4종을 평가하였다. 이를 위해 우리나라 사과 주산지 6곳(포천, 화성, 거창, 청송, 군위, 충주)의 사과원에서 3년간 관측된 기온과 만개일을 수집하여 각 모델에 적용하고 모델의 예측력을 평가 하였다. 냉각량 추정을 위한 Dynamic(DM) 모델과 가온량 추정을 위한 Growing Degree Days(GDH) 모델을 순차적으로 결합한 모델이 예측력이 가장 좋았으며, Chill Days(CD) 모델의 예측력이 가장 낮았다. Development Rate Model 1(DVR1)은 화성 지역과 같이 일 최저기온이 높고 상대적으로 일 최고기온이 낮아 일교차가 작은 지역에서는 실측일보다 빠르게 예측되는 과소추정오차를 보였다. 따라서 단일 모델로 우리나라 전국적인 사과 '후지' 만개일 예측을 위해서는 휴면타파에 필요한 냉각량 산정을 위한 DM 모델과 휴면타파 이후 개화에 필요한 고온량 산정을 위한 GDH 모델이 결합된 DM+GDH 모델이 가장 효과적일 것으로 판단된다. 그러나 보다 광범위한 지역의 장기간의 자료 수집과 이를 이용한 평가가 필요하다.

• 한국농공학회논문집
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• 제46권7호
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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.

• 대한치과기공학회지
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• 제41권4호
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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.

• 한국통신학회논문지
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• 제8권3호
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• pp.113-119
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• 1983

본 논문은 음성신호 델타 변조(이하 DM이라고 약칭함)에 관한 step size의 적응(adaption)에 대하여 연구한 것이다. 선형 및 지수적인 적응 중에서 지수적 적응 앨고리즘을 채택하고 또 복호기에서의 오차회복시간을 단축시켰다. 그리고 한 앨고리즘에 대해서 비례 적산기를 이용하여 실제적인 논리회로 구성법을 제안하였으며 500Hz의 정현과 신호를 16kHz의 펄스로 표본화하여 제안회로에 대한 타당성을 실험적으로 확인하였다.