• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제13권3호
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• pp.15-24
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• 2007

The concept of modern medical science has been changing from the old period which is simple treatment of diseases to the new period which is active prevention from diseases. Because preventing diseases is more effective and economical than treating diseases. This is the reason that needs to HPC at the concept of Health diagnosis. Another reason of HPC is to diagnose the stresses and to prescribe an effective exercise and to show the way of nutrition intake in order to keep up the condition of individual health. According to these reasons, I foresee the demand of remodelling HPC from existing HEC on this study. I define the common space that HEC uses duplication with HPC. I also aim to analyze them and to examine the alterable function and proper size of common space in case of remodelling.

• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제11권1호
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• pp.33-41
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• 2005

The construction can proceed in different ways according to the acquired profitability of the hospital during the construction and to the features of departments or areas. This study is an analysis of remodeling construction processes to resolve major tasks of remodeling. The remodeling strategies gained from this study can be summed up as follows: 1) Remodeling work in hospitals involves the acquire relocation of space through extensive area renovations and then moving back to the space, and lastly working on the empty space. Thus, it is more advantageous in terms of construction work to demolish the existing buildings than to acquire the relocation space through extensions or renovations. That is, demolition after the maximum utilization of the existing buildings is the most desirable in terms of space availability. 2) The construction methods for remodeling are two: a method of carrying out construction by dividing the plane areas into several individual ones and of working on it floor by floor. In case of ward areas, and the outpatient area, the construction proceeds after securing the relocation space and partially setting construction areas in order to minimize the decrease in profitability due to the smaller number of beds and treatment rooms during construction. If the outpatient diagnosis/ treatment area and the supply area relocate together with the ward areas, there may be extra expenses. Thus, doing construction by area, while partially operating those areas or after relocating the whole areas.

• Journal of Periodontal and Implant Science
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• 제48권2호
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• KIEAE Journal
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• 제17권4호
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• pp.41-48
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• 2017

Purpose : Currently, 25% of the domestic energy consumption structure is used as building energy, and more than 18% of this energy is consumed in the residential. Accordingly, various efforts and policies that can save energy of the building is being performed. The various researchers are conducting research to diagnose the thermal performance of existing buildings. This study is to apply in the field of precision thermal insulation performance diagnostic method for thermal performance analysis of existing detached house in Seoul, Gangreung, Gyeongju, Pohang. And this paper is analyzed quantitatively measure the existing detached house energy performance. Method: Research methodology analyzed the thermal performance over the Heat Flow Meter method by applying the measurement process and method by applying the criteria of ISO 9869-1 & ASTR method. In this study, the surface heat transfer coefficient was calibrated by applying indoor surface heat transfer resistance with reference to ISO 6946 standard. The measurement error rate between the HFM diagnosis method and the ASTR diagnosis method was reduced and the measurement reliability was obtained through measurement method error verification. Result : As a result of the study, the thermal performance vulnerable parts of the building were quantitatively analyzed, and presented for methods which can be improved capable of efficient energy use buildings.

• 대한두개안면성형외과학회지
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• 제13권1호
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• pp.76-79
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• 2012

Purpose: Sebaceous epithelioma (sebaceoma) is a benign tumor with sebaceous differentiation. It presents primarily as a yellowish papule or nodule on the face and scalp. It must be differentiated from basal cell carcinoma and other appendageal tumors. We report a giant sebaceous epithelioma on the scalp and describe the immunohistochemical character of the cells in sebaceous epithelioma to epithelial membrane antigen (EMA). Methods: A 55-year-old-man who presented with 5-cm-diameter 2-cm-height, round shape exophytic ulcerated tumor on his head presented for treatment. The patient had noticed the lesion 40 years prior as a small yellowish plaque and 18 months ago, the plaque started to grow progressively larger. We excised the lesion with 1 cm resection margin, considering the possibility of malignancy because this lesion grossly resembled basal cell carcinoma (BCC). The defect was repaired with the use of a splitthickness skin graft. Results: When we excised the lesion, the margin was clear. Histology showed nodules that consisted of an admixture of basaloid cells and mature adipocytes lacking an organized lobular architecture. Strong expression of EMA on mature adipose cells confirmed the differential diagnosis from BCC with sebaceous differentiation because of the absence of a nuclear palisade pattern and cleft-like spaces on the hematoxylin and eosin (H&E) section. Conclusion: We treated the giant sebaceous epithelioma on the scalp with surgical excision and a split-thickness skin graft. It is important to know that the diagnosis of sebaceous epithelioma should be made based on the histologic pattern of the H&E section. Immunohistochemistry with EMA can help to confirm the differential diagnosis between sebaceous epithelioma and BCC.

• 한국구조물진단유지관리공학회 논문집
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• 제24권1호
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• pp.133-141
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• 2020

한국시설안전공단에서는 '시설물의 안전관리에 관한 특별법'에 따라 철근콘크리트 구조물의 안전점검 및 정밀안전진단을 실시하도록 제시하고 있다. 그러나 한국시설안전공단 안전점검 및 정밀안전진단 세부지침의 평가방법에서는 평가결과를 등급으로 제시하기 때문에 구조물의 잔존수명을 알 수 없으며 부등침하가 구조물의 잔존수명에 미치는 영향을 반영하지 못한다. 따라서, 이 연구에서는 부등침하의 영향이 반영된 구조물의 잔존수명 평가모델을 제시하고자 하였다. 부등침하와 각 변위의 상관관계를 나타내는 기존의 연구를 바탕으로 부재의 공칭강도에 부등침하의 영향을 반영시키기 위한 식을 제시하였으며, 실제 철근콘크리트 구조물의 현장데이터를 활용하여 부등침하가 구조물의 잔존수명에 미치는 영향을 분석하였다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권3호
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• pp.83-90
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• 2022

당뇨병은 오늘날 주변에서 흔히 찾아볼 수 있는 질병이며, 당뇨병성 족부 궤양(당뇨발)이라는 심각한 합병증으로 발전하는 사례 또한 많이 나타난다. 따라서 이를 사전에 진단하고 예방하는 것은 중요한 과제이며 본 논문에서 그 방안을 제시한다. 본문에서 소개하는 기존의 연구들을 바탕으로 발의 압력과 온도 정보는 당뇨발과 깊은 상관관계가 있음을 알 수 있으며, 해당 지표들을 측정하는 IoT 기기인 스마틴솔을 개발과정 및 아키텍쳐를 소개한다. 또한, 더 나아가 스마틴솔로 측정한 데이터들의 실제 당뇨발 진단을 위한 AI 분석 전처리 과정을 기술하며, 측정된 압력 그래프와 실제 사람의 발걸음 분포의 비교 등을 통해 실시간으로 수집하는 다중 정보들이 기존의 IoT 기기들보다 효율적이고 신뢰성 있다는 결과를 제시한다.

• 한국구조물진단유지관리공학회 논문집
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• 제25권3호
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• pp.85-91
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• 2021

건축시설물 중 제1종 시설물과 제2종 시설물은 유지관리를 위해 정기적으로 정밀안전점검과 정밀안전진단을 실시한다. 건축물의 점검 및 진단을 실시하는 경우 상태평가, 기울기 및 침하, 안전성평가를 종합하여 건축물의 등급이 결정된다. 그리고 평가등급은 점검 및 진단의 주기를 결정한다. 평가 등급이 좋지 않다면 점검 및 진단 주기는 짧아지고, 이로 인한 유지관리 비용은 증가하게 된다. 따라서 건축물을 평가하는 방법은 명확하여야 한다. 상태평가는 부재단위 평가, 층단위 평가, 종합 평가로 이루어진다. 이 중 부재단위 평가는 현장조사와 시험 결과를 통해 등급을 판정한다. 본 연구에서는 상태평가의 첫 번째 단계인 부재단위 평가에 대해 평가방법과 평가기준을 분석하여 개선점을 도출하였으며, 이를 개선하여 현재 부재의 상태점수를 반영할 수 있는 함수식을 제시하였다.

• 대한전자공학회논문지TC
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• 제48권10호
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• pp.43-52
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• 2011

본 논문에서는 스마트 헬스케어 서비스를 위한 정책 기반 응급 생체 정보 구조를 제시한다. 제안된 서비스 구조를 통해 의료진이 원격지 환자의 응급 생체데이터를 빠르고 정확하게 모니터링 할 수 있다. 제안된 시스템은 생체 데이터 수집 및 전송 기능을 가진 IEEE 11073 표준 기반 에이전트와 매니저, IEEE 11073과 HL7 간 변환 기능 및 정책 기반의 자동 진단 기능을 가진 EMS (Emergency Management Server), HL7 표준 기반의 HL7 의료 시스템으로 크게 3 부분으로 구성한다. 마지막으로, 제안된 시스템을 구현함으로써 스마트 헬스케어 서비스에서 생체 데이터의 수집 및 응급 데이터 관리가 가능함을 보였다.

• 대한의용생체공학회:의공학회지
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• 제41권1호
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.