• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.499-500
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• 2018

뷰티테크시장에서 출시하는 제품 중 영상처리기술을 반영한 다양한 뷰티디바이스가 증가하는 추세이다. 특히 스마트패키징의 경우 주목할 만한 이슈로 뷰티디바이스와 연계된 사용성 및 편리성을 갖춘 애플리케이션 개발이 중요해지고 있으며 사용자가 맞춤형 피부진단 관리 방법에 대한 뷰티정보, 분석리포트, 피부케어샵, 화장품 등을 추천받을 수 있는 뷰티솔루션이 설계되어야 한다. 본 연구는 뷰티케어시장의 전문서비스 영역과 경계를 대중화하여 사용자가 맞춤형 피부진단 관리를 할 수 있는 애플리케이션 설계에 주안점을 둔다. 이를 위해 피부진단 관리 데이터 수집방법의 변수를 정의하고 사용자에게 제안, 모니터링, 뷰티 홈케어 정보, 전문서비스와의 연계방안 등 맞춤형솔루션을 제공할 수 있는 비즈니스플랫폼모델을 제안하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.808-817
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• 2014

Purpose: This study was to investigate the recognition of skin infection (RSI) and management practice (MP) on caregivers in geriatric hospital. Methods: The subjects were 209 caregivers who work at geriatric hospital in G city and J do. Data were analyzed with ${\chi}^2$-test, t-test. and ANOVA using SPSS 18.0 program. Results: Prevalence rate of caregivers' skin diseases was 76.6%. Diagnosis of Skin disease was contact dermatitis 42.5%, scabies 26.9%, and skin xerosis 25.0%. The Mean RSI score was 3.81 and MP was 4.12. In addition, MP was significantly different by number of bed hospitals (F=4.63, p=.011) and number of caring patients (F=2.67, p=.049). Conclusion: This study will be a useful on continuing education for caregivers and basis for the guidance of medical infection control standards development.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.33-34
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• 2020

사용자의 피부 관리 및 피부질환을 검사하는 기존의 어플리케이션은 유도 질문에 따른 사용자의 응답을 기반으로 결과를 유추하기 때문에 부정확한 진단 결과를 야기한다. 본 논문에서는 사용자의 미용관련 피부질환 이미지를 바탕으로 딥러닝 이미지 인식 기술 적용하여 건선, 사마귀, 여드름, 한포진을 대상으로 피부 미용질환에 대한 식별 정보를 제공하는 어플리케이션을 제시한다. 또한 이미지 인식률이 높은 ResNet과 SE-ResNet 알고리즘을 적용하여 피부질환 식별 적용 시 효과성을 실험적으로 비교한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.386-389
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• 2016

최근 남녀노소를 불문하고 피부 관리에 대한 관심이 증가하면서, 피부 개선에 효과적인 화장품의 선택에 관심이 높아지고 있다. 하지만 다양한 화장품들을 대상으로 자동화된 고객 맞춤형 화장품 추천은 그 발전이 더디고, 이와 관련된 연구 또한 아직 미미한 실정이다. 또한, 다양한 특성을 가지는 고객 피부 데이터 셋의 확보가 어려운 상황에서, 소수의 데이터 표본만을 이용하여 화장품 추천이 진행되고 있어 추천의 정확도를 확보하지 못하고 있다. 본 논문은 스마트폰용 휴대용 카메라를 이용하여 고객의 피부 상태를 진단한 후, 고객의 피부 개선에 적합한 화장품을 자동으로 추천하는 기법을 제안한다. 먼저, 화장품 추천을 위해 사용자의 SNS 데이터와 피부 데이터를 수집 및 분석하여 추천 리스트를 생성한다. 이를 기반으로, 추천된 각 화장품의 스코어를 계산한다. 그 다음, 피부 개선 순위와 스코어 기반의 화장품 특성 순위 간의 상관계수를 이용하여 가장 높은 상관계수의 화장품을 우선 추천한다. 성능 평가를 위해 실제 화장품 회사에서 제시한 화장품 추천 리스트와 본 논문에서 제안한 방법을 적용한 화장품 추천 리스트를 비교함으로써 효용성과 타당성을 입증하였다.

• Journal of radiological science and technology
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• v.39 no.3
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• pp.305-312
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• 2016

This study were compared with the direct measurement and indirect dose methods through various dose calculation in head and wrist. And, the modified equation was proposed considering equipment type, setting conditions, tube voltage, inherent filter, added filter and its accompanied back scatter factor. As a result, it decreased the error of the direct measurement than the existing dose calculation. Accordingly, diagnostic radiography patient dose comparison would become easier and radiogrphic exposure control and evaluation will become more efficient. The study findings are expected to be useful in patients' effective dose rate evaluation and dose reduction.

• Journal of Oral Medicine and Pain
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• v.37 no.3
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• pp.135-139
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• 2012

Hereditary hemorrhagic telangiectasia is a rare autosomal dorminant disease that features abnormal and fragile vascular dilations of terminal vessels in skin and mucous membranes, as well as arteriovenous malformations of internal organs, particularly lungs, brain, and liver. Often patients have not been diagnosed with HHT for a long time, and undiagnosed HHT patients unnecessarily develop serious complications such as severe life-threatening hemorrhage, stroke or brain abscess. Therefore, early detection and appropriate screening is very important. Early detection of HHT allows the appropriate screening for the presence of silent disease such as AVMs in the lungs, liver, or brain, and preventive treatment in the patient and their affected family members. Dentists should be familiar with HHT because the telangiectases on skin and oral mucosa are often the most dramatic and most easily identified component of HHT. Recently, we experienced a case of HHT. We present the case with a review of the literature.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.6
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• pp.120-127
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• 2016

Several problems including respiratory and skin disorders due to the problems for sick house syndrome have occurred, there appears echo friendly materials to solve the problems. The research is lacking in quality management techniques ocher concrete using nondestructive tests. In this research, the experimental works were conducted to study the initial quality control for the compressive strength of Ocher concrete(21 MPa). The purpose of this study is the implementation platform for quality management of ocher concrete using nondestructive tests. It uses the relationship between the compressive strength and ultrasonic pulse velocity of the ocher concrete to estimate the compressive strength of the ocher concrete. And using the impact echo method to estimate the thickness of the ocher concrete. The platform is based on a Java script, so that the user can obtain the data through the platform.