• 영재교육연구
• /
• 제10권2호
• /
• pp.1-23
• /
• 2000

영재 교육 체제는 영재 각자의 능력과 다양한 흥미와 변화하는 요구를 수용할 수 있을 만큼의 충분한 융통성과 개방성을 지녀야 하며, 우리의 교육 실정에 적합한 교육 발달의 틀이 필요하다. 본 연구의 목적은 아직 이론적이고 내용적 수준에 머물러 있는 영재 교육의 개념적 모형을 실제 교수${\cdot}$학습 장면에서 쉽게 적용할 수 있는 절차적이며 처방적인 체제적 모형으로 전호나함으로써 보다 효과적인 영재 교육 체제를 개발하고자 함니다. 이를 위해 영재 교육의 가장 대표적인 모형인 Renzulli의 전교 심화학습 모형(Schoolwide Enrichment Model: SEM)을 바탕으로 교수체제설계(ISD)를 적용한 결과, 기획${\cdot}$진단${\cdot}$처방${\cdot}$실행${\cdot}$평가의 5단계로 SEM의 체제적 모형을 재구성하였다. 또한 체제적으로 수정된 SEM 모형이 갖는 영제 교수${\cdot}$학습 상황에서의 특징을 6가지로 요약하여 정리하였다. 그러나, 각 학교에 최적한 모형을 개발하기 위해서는 본 연구에서 제시된 체제적 모형에 바탕해 계속적인 수정과 검토가 있어야 할 것이다.

• International Journal of Contents
• /
• 제18권2호
• /
• pp.47-57
• /
• 2022

e-Learning content can be defined as digital content to achieve educational goals. Since it is an educational material that can be distributed in offline, online, and mobile environments, it is important to create content that meets the learner's education environment and educational goals. In particular, if the learner is a public official, the vision, philosophy, and characteristics of each local government must reflect. As non-face-to-face online education expands further due to the COVID-19 pandemic, local governments that have relied on onsite education in the past urgently require developing strong basic competency education and special task competency content that reflect regional characteristics. Such e-learning content, however, hardly exists and the ability to independently develop them is also insufficient. In this circumstance, this case study describes the process of self-production of e-learning content suitable for Busan's characteristics by the Human Resource Development (HRD) Institute of Busan City, a local government. The field of instructional design and instructional technology is always evolving and growing by blending technological innovation into instructional platform design and adapting to the changes in society. Busan HRD Institute (BHI), therefore, tried to implement blended learning by developing content that reflected the recent trend of micro-learning in e-learning through a detailed analysis. For this, an e-learning content developer with certain requirements was selected and contracted, and the process of developing content through a collaboration between the client and developer was described in this study according to the ADDIE model of Instructional Systems Development (ISD).

• Imaging Science in Dentistry
• /
• 제52권2호
• /
• pp.187-195
• /
• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• 대한산업공학회지
• /
• 제32권2호
• /
• pp.82-90
• /
• 2006

Reconfigurability of machine tools is one of the critical factors to realize the responsive manufacturing systems to satisfy the mass customization production. This paper presents the methods to model the reconfigurable machine tools (RMTs) on Internet in response to change in the machining requirements. Specifically, the structure and motion model of machine tools using module combination rules and connectivity graph are developed. And we developed Internet-based simulator for designing of RMTs (ISD-RMT). In response to the user requirements, various structures of RMTs can be derived using the module combination rules and connectivity graph relationships. In addition, the user can verify generating structures through the control and simulation procedures.

• 통상정보연구
• /
• 제14권4호
• /
• pp.409-431
• /
• 2012

본 연구는 미국과의 FTA에서 이의 비준과 관련하여 찬반 양쪽으로 첨예한 의견이 대립되는 ISDS에 관한 연구를 통하여 우리나라가 취할 수 있는 입장에 대하여 분석하였다. 이를 위하여 특별히 ICSID에 서명은 하였으나 인준을 하지 않은 국가 중, 캐나다와 호주의 사례를 중심으로 분석하였다. NAFTA에 의하여 미국과 멕시코를 포함하는 자유무역협정을 체결하고 있음에도 ICSID 협정을 인준하지 않고 있으며, 호주 또한 ICSID 협정을 거부하고 있다. 이러한 두 국가가 ICSID를 거부하고 있는 사유를 우리나라 역시 ICSID를 거부하여야 하는 문제점으로 제시하고 있는 실정임으로 양국의 입장을 분석하고 우리나라의 실정에 적용해 봄은 학술적으로 의미가 있다고 할 수 있을 것이다. 이에 본 연구는 캐나다와 호주의 ICSID 비인준 입장을 분석하고 이와 ICSID를 바탕으로 한미 FTA에서의 ISDS 문제를 논의하며, 국가간 분쟁과 ISDS의 의미를 재고하여 결론을 제시하도록 한다.

• 한국실천공학교육학회논문지
• /
• 제3권2호
• /
• pp.142-146
• /
• 2011

본 논문은 고령친화산업의 활성화를 위한 고령친화산업체에 재직하는 현장인력재교육사업의 사례를 소개한다. 고령친화산업체 재직자를 대상으로 2009년 8월 이후 2년여동안 IT기반 고령친화산업 현장기술인력 재교육사업을 운영하면서 개발된 IT기반의 고령친화제품 개발에 필요한 교과과정을 소개한다. 본 교과과정은 ISD(instruction system design: 교수체제설계) 모형에 기반하여 개발했다. IT기반 고령친화 제품 개발을 위해서 재직자 및 전문가들이 인간공학기반 설계와 IT기반의 설계와 관련된 교육을 요구해서 인간공학기반 제품설계는 "고령친화 인체특성 및 고령자 생활공학", "고령자 색체감성 및 유니버셜디자인", "디자인의 이해 및 디자인 프로세스" 교과목으로 구성되고 고령친화 생활 건강관리기기 설계과정은 "고령친화 IT 기기용 임베디드 시스템 설계 및 디버깅 실습", "고령친화 안드로이드 구현 설계", "실버케어 안드로이드 기반 스마트 장치 설계 및 실습" 교과목으로 구성된다.

• Imaging Science in Dentistry
• /
• 제52권3호
• /
• pp.275-281
• /
• 2022

Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort

• 한국진공학회:학술대회논문집
• /
• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
• /
• pp.376-376
• /
• 2012

본 연구는 Poly-Si에 이온 주입된 Boron의 Activation 거동을 연구하고자 SLS (Sequential Lateral Solidification) Poly-Si과 ELA (Excimer Laser Annealing) Poly-Si의 활성화 거동을 비교 분석하였다. SLS 및 ELA 결정화 방법으로 제조된 Poly-Si을 모재로 비 질량 분리 방식의 ISD (Ion Shower Doping) System을 사용하여 2.5~7.0 kV까지 이온주입 하였다. 이온주입 후 두 가지의 열처리 방법, 즉, FA 열처리(Furnace Annealing)와 RTA 열처리(Rapid Thermal Annealing)를 사용하여 도펀트 활성화 열처리를 수행하고 이온주입 조건 및 활성화 열처리 방법에 따른 결함 회복 및 도펀트 활성화 거동의 변화를 관찰하였다. TRIM-code Simulation 결과 가속 이온 에너지와 조사량이 증가 할수록 이온주입 시 발생하는 결함의 양이 증가하는 것을 정량적으로 계산하였다. 실험 결과 결함의 양이 증가 할수록 Activation이 잘되는 것을 관찰할 수 있었다. SLS Poly-Si에 비하여 ELA Poly-Si의 경우 도펀트 활성화 열처리 후 활성화 효율이 높게 나타났다. 본 결과는 Grain Boundary의 역할과 밀접한 관계가 있으며 간단한 정성적인 Model을 제시하였다. 활성화 효율의 경우 RTA 열처리 시편이 FA 시편에 비하여 높은 것이 관찰되었다. 본 결과는 열처리 온도 및 시간에 따라 변화하는 Boron의 특이한 활성화 거동인 Reverse Annealing 효과에 기인하는 것으로 규명되었다.

• Imaging Science in Dentistry
• /
• 제49권4호
• /
• pp.257-263
• /
• 2019

Purpose: This study was performed to evaluate the dimensional accuracy of digital dental models constructed from cone-beam computed tomographic (CBCT) scans of polyvinyl siloxane (PVS) impressions and cast scan models. Materials and Methods: A pair of PVS impressions was obtained from 20 subjects and scanned using CBCT (resolution, 0.1 mm). A cast scan model was constructed by scanning the gypsum model using a model scanner. After reconstruction of the digital models, the mesio-distal width of each tooth, inter-canine width, and inter-molar width were measured, and the Bolton ratios were calculated and compared. The 2 models were superimposed and the difference between the models was measured using 3-dimensional analysis. Results: The range of mean error between the cast scan model and the CBCT scan model was -0.15 mm to 0.13 mm in the mesio-distal width of the teeth and 0.03 mm to 0.42 mm in the width analysis. The differences in the Bolton ratios between the cast scan models and CBCT scan models were 0.87 (anterior ratio) and 0.72 (overall ratio), with no significant difference (P>0.05). The mean maxillary and mandibular difference when the cast scan model and the CBCT scan model were superimposed was 53 ㎛. Conclusion: There was no statistically significant difference in most of the measurements. The maximum tooth size difference was 0.15mm, and the average difference in model overlap was 53 ㎛. Digital models produced by scanning impressions at a high resolution using CBCT can be used in clinical practice.

• Imaging Science in Dentistry
• /
• 제53권1호
• /
• pp.27-34
• /
• 2023

Purpose: The aim of this study was to clarify the influence of training with a different kind of lesion on the performance of a target model. Materials and Methods: A total of 310 patients(211 men, 99 women; average age, 47.9±16.1 years) were selected and their panoramic images were used in this study. We created a source model using panoramic radiographs including mandibular radiolucent cyst-like lesions (radicular cyst, dentigerous cyst, odontogenic keratocyst, and ameloblastoma). The model was simulatively transferred and trained on images of Stafne's bone cavity. A learning model was created using a customized DetectNet built in the Digits version 5.0 (NVIDIA, Santa Clara, CA). Two machines(Machines A and B) with identical specifications were used to simulate transfer learning. A source model was created from the data consisting of ameloblastoma, odontogenic keratocyst, dentigerous cyst, and radicular cyst in Machine A. Thereafter, it was transferred to Machine B and trained on additional data of Stafne's bone cavity to create target models. To investigate the effect of the number of cases, we created several target models with different numbers of Stafne's bone cavity cases. Results: When the Stafne's bone cavity data were added to the training, both the detection and classification performances for this pathology improved. Even for lesions other than Stafne's bone cavity, the detection sensitivities tended to increase with the increase in the number of Stafne's bone cavities. Conclusion: This study showed that using different lesions for transfer learning improves the performance of the model.