• 대한화장품학회지
• /
• 제31권3호
• /
• pp.253-257
• /
• 2005

피부노화의 정도를 판정하기 위해 사용되는 주름측정법들은 객관성과 재현성의 확보가 중요한 요소이다. 최근의 경향은 주름의 형태나 깊이에 주는 영향을 최소화하기 위해 주름 측정시 피부에 직접 기계나 도구를 접촉하지 않고 측정하는 비접촉식 측정방법으로 빠르게 전환되고 있는 상황이다. 저자들은 주름측정 기술의 변천 과정을 간단히 살펴보고, 비접촉식 fringe projection 방식의 미세주름 측정기기인 PRIMOS를 중심으로 측정원리, 특징들을 접촉식 측정방법인 모사판을 이용한 방법과 비교하였다.

• 한국재료학회지
• /
• 제30권8호
• /
• pp.435-440
• /
• 2020

The relationship between the precipitation of secondary phase and the thermal properties of Al-4.5%Cu alloy (in wt.%) after various heat treatments has been studied. Solid solution treatment of alloy was performed at 808 K for 6 hours, followed by warm water quenching; then, the samples were aged in air at 473 K for different times. The thermal diffusivity of the Al-4.5%Cu alloy changed with the heat treatment conditions of the alloy at temperatures below 523 K. The as-quenched specimen had the lowest thermal diffusivity, and as the artificial aging time increased, the thermal diffusivity of the specimen increased in the temperature range between 298 and 523 K. For the specimen aged for five hours, the thermal conductivity was 12% higher than that of the as-quenched specimens at 298 K. It is confirmed that the thermal diffusivity and thermal conductivity of the Al-4.5%Cu alloy significantly depend on their thermal history at temperatures below 523 K. The precipitation and dissolution of the Al₂Cu phase were confirmed via DSC for the alloys, and the formation of coefficient of thermal expansion peaks in TMA was caused by precipitation. The precipitation of supersaturated solid solution of Al-4.5%Cu alloys had an additional linear expansion of ≈ 0.05 % at 643 K during thermal expansion measurement.

• 로봇학회논문지
• /
• 제15권3호
• /
• pp.269-276
• /
• 2020

As we become an aging society, the number of elderly patients continues to increase. Pressure sores that can easily occur in patients with trauma cause serious socio-economic problems. In general, prevention of bedsores through predicting the patient's posture is being developed. Developed method usually use artificial intelligence techniques to estimate the patient's posture by measured pressure images in the mattress. In this method, it has a problem the reduction of estimation accuracy when posture of patient is changed. Therefore, it is necessary to use the filter of pressure images in the position transition of patient. In this paper, we propose an algorithm to predict the patient's posture, and an algorithm to reduce the ambiguity that can occur in the patient's posture transition section. By obtaining stable data through this algorithm, learning/prediction stability of the neural network can be expected, and prediction performance is improved accordingly. Through experiments, the effectiveness of the algorithm was verified.

• 한국조명전기설비학회지:조명전기설비
• /
• 제11권6호
• /
• pp.50-56
• /
• 1997

전극들과 절연재료 사이의 결합과 고분자 절연체 내부에서 여러 가지 결함에 의한 부분 방전이 발생함으로 일어날 수 있는 절연 재료의 트리 열화를 파괴 예지할 목적으로 하였다. 부분 방전에 기인한 트리잉은 절연 재료의 파괴를 일으킬 수 있는 중요한 원인 가운데 하나다. 최근에는 절연 파괴 예지와 절연 재료의 열화 진단을 하는 방법이 중요하게 되었다. 연구 목적은 자동 계측 시스템을 사용하여 인가전압 11[kV], 인공적인 침상보이드(1.5[mm])을 지닌 고분자 절연체 내부에서 음향 방출시스템과 프랙탈 차원을 사용하여 트리 현상을 관찰하였다. 따라서 본 논문에는 최소자승법에 의한 회귀분석을 사용하여 위상각-음향방출 펄스크기-열화시간 양상과 위상각-음향방출 펄스수-열화시간과 프랙탈 차원의 관계를 통하여 파괴가 발생하기 전의 파괴 예지법으로 사용하였다.

• 노인간호학회지
• /
• 제20권sup1호
• /
• pp.160-165
• /
• 2018

Purpose: There is a need to examine changes in the health care environment and the impact on gerontological nursing care in the era of the Fourth Industrial Revolution. In this article recent healthcare paradigm changes, gerontechnology, high tech and high touch, person-centered approaches are discussed. Methods: A narrative review was used. Results: Cyber physical system, artificial intelligence, advance and convergence of bioengineering, and information communication technology are changing the health care paradigm to "precision", "prediction" and "personalization". Entry into the global aging society and the surge in the elderly population worldwide has led to searches for a new means to prepare for projected demands of this growing population. Thus, efforts such as gerontechnology have been made to apply and utilize recent innovative science and technology in order to promote the health and life of elders. There is a great emphasis on the convergence of high tech and high touch, in which humanistic and artistic approach are critical in order to assure that technology is beneficial to human beings rather than harmful. Conclusion: Positive healthcare experiences among patients and their families are emphasized by utilizing new technology and employing high touch while providing personalized care with a person-centered approach.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2019년도 춘계학술대회
• /
• pp.355-357
• /
• 2019

고령화 사회로 진입하고 있는 지금, 시니어 세대에게는 새로운 디바이스나 IoT기술에 대한 사용자 접점은 매우 불편하다. 이를 개선하기 위하여 우리는 AI 기술 기반 지능형 시니어 도우미 음성인식 시스템을 제안한다. 제안 시스템은 Cloud platform기반 API를 구현하여 머신러닝 처리 활용을 위한 데이터를 축적하며, 치매진단, 치매예방활동을 위한 콘텐츠를 제공하며,시니어 세대를 위한 챗봇 콘텐츠를 제공한다. 우리가 제안한 개념모델을 이용한 서비스를 API로 제공함으로서 시니어 세대에 대한 IoT기반 등 새로운 디바이스의 접근성 및 편리성을 증대하는 계기가 될 것으로 기대한다.

• 한국멀티미디어학회논문지
• /
• 제23권12호
• /
• pp.1486-1495
• /
• 2020

The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.

• 전력전자학회논문지
• /
• 제26권1호
• /
• pp.1-8
• /
• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2021년도 추계학술대회
• /
• pp.439-441
• /
• 2021

매년 고령화 사회로 인해 욕창환자가 증가하고 있으며 COVID-19의 팬데믹 상황으로 간호인의 업무 부하로 욕창 관리의 중요성이 대두되고 있다. 욕창은 부동자세로 인해 궤양이 생기는 질병으로 간호인이 주기적으로 체위변경을 해줘야 하기에 간호 부담이 큰 질병이다. 이에 본 연구에서는 인공지능이 욕창 유발 위험을 검출하고 호발 현황을 실시간 모니터링 해줌으로써 간호인의 업무 부담을 줄일 수 있는 시스템을 제시한다. 본 시스템을 통하여 간호인의 욕창 간호의 어려움을 해소시켜 간호 업무의 증대할수 있을 것이다.

• 한국공간구조학회논문집
• /
• 제23권4호
• /
• pp.81-88
• /
• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.