• 한국정보통신학회논문지
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• 제25권3호
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• pp.361-367
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• 2021

유튜브와 같은 동영상 스트리밍 서비스에서의 정보 검색은 전통적인 정보 검색 서비스를 대체하고 있다. 이러한 동영상 안에서 원하는 세부적인 정보를 찾기 위해서는 사용자가 여러 부분을 반복해서 탐색하며 시간과 네트워크 대역폭을 낭비해야 하는 문제점이 있다. 본 논문에서는 클러스터링과 LSTM을 이용하여 이러한 사용자의 동영상 내 정보 검색을 보조하는 방법을 제안한다. 제안하는 방법은 사용자의 정보 검색을 위한 탐색 지점 순서와 DBSCAN이 범주화한 최종 목적 지점 범주를 이용하여 LSTM 모델을 학습하고, 이 모델을 이용하여 사용자가 검색을 시작할 때 선택한 탐색 지점 순서에 기반을 둔 사용자의 예상 목적 지점 범주를 제시한다. 실험 결과, 제안하는 방법이 사용자가 원하는 목적 지점을 평균적으로 98%의 정확도와 7초의 시간 오차로 찾아내는 것을 보였다.

• Imaging Science in Dentistry
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• 제52권4호
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.239-260
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• 2023

The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system.

• Korean Journal of Radiology
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• 제22권6호
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• pp.912-921
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• 2021

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

• 한국도서관정보학회지
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• 제40권3호
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• pp.203-223
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• 2009

미국 학교도서관 기준은 미국의 각 주(州) 정부의 학교도서관 정책은 물론 전 세계 학교도서관계에 지대한 영향을 미치고 있다. 미국의 학교도서관 기준은 미국교육학회(NEA) 주도로 1920년에 처음으로 제정된 이래 교육 및 기술 변화를 적극 수용하여 약 10여 차례 개정되어 왔으며, 가장 최근의 기준으로는 2009년에 AASL에서 발행한 "학습자 힘 기르기"가 있다. "학습자 힘 기르기"는 2007년과 2009년에 각각 발행한 "21세기 학습자를 위한 기준", "21세기 학습자를 위한 기준의 실천"을 바탕으로 하고 있으며, 전 세계의 학교도서관계에 영향을 끼칠 것으로 예상된다. 이에 본 연구에서는 2007년에 제정한 학교도서관 기준 관련 문서 "21세기 학습자를 위한 기준"과 "21세기 학습자를 위한 기준의 실천"의 구조와 내용을 분석하여 학교도서관계의 동향을 파악하고, 우리나라 학교도서관 정책의 바로미터로 삼고자 한다. "21세기 학습자를 위한 기준"은 학교도서관의 교육적 비전을 제시하고, 학교도서관과 사서교사가 학생들의 학습에 어떻게 기여할 수 있는가를 보여주고 있다. "21세기 학습자를 위한 기준의 실천"은 학교도서관을 통해서 기준을 실천할 수 있는 방안을 제시하고 있다.

• Korean Journal of Radiology
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• 제23권3호
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• pp.343-354
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• 2022

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). Conclusion: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

• 대한영상의학회지
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• 제84권1호
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• pp.240-252
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• 2023

목적 소아 환자에서 두부 컴퓨터단층촬영(이하 CT)에 대한 딥러닝 이미지 재구성(deep learning image reconstruction; 이하 DLIR; TrueFidelity; GE Healthcare, Milwaukee, WI, USA)의 효과를 평가하고자 한다. 대상과 방법 총 126개의 소아 두부 CT 이미지를 수집했으며, adaptive statistical iterative reconstruction (이하 ASiR)-V를 사용한 반복적 재구성 및 세 가지 수준의 DLIR을 사용한 재구성을 시행하였다. 각 이미지 세트 그룹은 환자의 연령에 따라 4개의 그룹으로 구분하였으며 각 연령군의 임상 및 방사선량 관련 데이터를 검토하였다. 양적 매개 변수에는 signal to noise ratio (이하 SNR) 및 contrast to noise ratio (이하 CNR)가 포함되었으며 질적 매개 변수로 영상의 잡음(noise), 회백질의 구분 정도, 선명도, 인공물 및 수용 가능성(acceptability), 영상의 질감이 포함되었고 이에 대한 평가와 비교를 시행하였다. 결과 모든 연령 그룹의 모든 수준의 SNR 및 CNR은 높은 수준의 DLIR 사용 시 증가하였다. ASiR-V와 비교했을 때 높은 수준의 DLIR은 SNR 및 CNR이 개선되었다(p < 0.05). 그리고 DLIR의 수준이 증가될수록 순차적인 잡음 감소, 회백질 구분 개선, 선명도 개선이 나타났다. 이러한 변수들에서 높은 수준의 DLIR 사용 시 ASiR-V와 유사한 정도의 수치가 측정되었다. 인공물과 수용 가능성의 경우에 적용된 DLIR 수준 간에 큰 차이를 보이지 않았다. 결론 소아 두부 CT에 고수준 DLIR을 적용하면 영상의 노이즈를 줄일 수 있으나 인공물 처리에 대한 개선이 필요하다.

• 간호행정학회지
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• 제20권2호
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• pp.227-236
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• 2014

Purpose: This study was designed to develop and evaluate the a web-based simulation program on patient rights education using integrated decision making model into values clarification for nurse students. Methods: The program was designed based on the Aless & Trollip model and Ford, Trygstad-Durland & Nelms's decision model. Focus groups interviews, surveys on learning needs for patient rights, and specialist interviews were used to develop for simulation scenarios and decision making modules. The simulation program was evaluated between May, 2011 and April, 2012 by 30 student nurses using an application of the web-based program evaluation tools by Chung. Results: Simulation content was composed of two scenarios on patient rights: the rights of patients with HIV and the rights of psychiatric patients. It was composed of two decision making modules which were established for value clarifications, behavioral objective formations, problems identifications, option generations, alternatives analysis, and decision evaluations. The simulation program was composed of screens for teacher and learner. The program was positively evaluated with a mean score of $3.14{\pm}0.33$. Conclusion: These study results make an important contribution to the application of educational simulation programs for nurse students' behavior and their decision making ability in protecting the patient rights.

• 한국도서관정보학회지
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• 제38권4호
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• pp.87-116
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• 2007

학교도서관 교육은 학생의 정보활용능력을 위한 도구교과 중 하나이다. 따라서 학교 교육과정과의 연계가 학교도서관 교육의 활성화를 위한 중요한 전략이다. 학교도서관 통합 교육과정은 학교도서관 교육의 사고전략과 교과교육 과정의 학습주제를 통합한 것이다. 학교도서관 통합교육과정의 목적은 사서교사의 교육적 역할을 강화하고 학생의 자기주도적 학습능력을 신장하는 것이다. 본 연구에서는 연계와 협동 전략으로써 범교과 학습주제를 기반으로 10학년을 위한 통합교육과정 개발 사례를 제시하였다.

• 한국가정과교육학회지
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• 제11권2호
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• pp.51-63
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• 1999

For Korean education to be able to handle increasing knowledge and new values in the 21st century, it needs to change. Korean education needs various teaching methods. At the present, CAI, multimedia, interact video system and computer communication are being used. Compared to the traditional teaching methods, computer-assisted instructions were reported to be more effective. The students’demands for these kind of lessons are adamant and increasing. In this study, an attempt was made to develop “child-development and parent education”using CAI program. This chapter is in high demand for high school students. This study model was developed help the students’understanding and make their learning easier. A lesson plan was proposed using CAI program which was developed by authors with assistance of professional computer programmers. The CAI program includes following curriculum contents：1. Child development, 2. the meaning of parenthood, 3. pregnancy, 4. delivery, 5. abortion. The CAI program was designed to allow students to participated activity in several current issue related with parenthood and aborting problems. This study ultimately aimed to show students moving pictures, animation, vivid photos, and music to motivate them. Another goal was to help the Home Economics teachers give lessons using CAI and to show an example of the teaching model.