• 의학교육논단
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• 제18권2호
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• pp.51-57
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• 2016

This literature review explores artificial intelligence (AI) technology trends and IBM Watson health and medical references. This study explains how healthcare will be changed by the evolution of AI technology, and also summarizes key technologies in AI, specifically the technology of IBM Watson. We look at this issue from the perspective of 'information overload,' in that medical literature doubles every three years, with approximately 700,000 new scientific articles being published every year, in addition to the explosion of patient data. Estimates are also forecasting a shortage of oncologists, with the demand expected to grow by 42%. Due to this projected shortage, physicians won't likely be able to explore the best treatment options for patients in clinical trials. This issue can be addressed by the AI Watson motivation to solve healthcare industry issues. In addition, the Watson Oncology solution is reviewed from the end user interface point of view. This study also investigates global company platform business to explain how AI and machine learning technology are expanding in the market with use cases. It emphasizes ecosystem partner business models that can support startup and venture businesses including healthcare models. Finally, we identify a need for healthcare company partnerships to be reviewed from the aspect of solution transformation. AI and Watson will change a lot in the healthcare business. This study addresses what we need to prepare for AI, Cognitive Era those are understanding of AI innovation, Cloud Platform business, the importance of data sets, and needs for further enhancement in our knowledge base.

• 방사선산업학회지
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• 제5권2호
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• pp.137-143
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• 2011

Epoxy resins are widely used as high performance thermosets in many industrial applications, such as coatings, adhesives and composites. Recently, a lot of research has been carried out in order to improve their mechanical properties and thermal stability in various fields. Carbon nanotubes possess high physical and mechanical properties that are considered to be ideal reinforcing materials in composites. CNT-reinforced epoxy system hold the promise of delivering superior composite materials with their high strength, light weight and multi functional features. Therefore, this study used multi-walled carbon nanotubes (MWNT) and gamma rays to improve the mechanical and thermal properties of epoxy. The diglycidyl ether of bisphenol A (DGEBA) as epoxy resins were cured by gamma ray irradiation with well-dispersed MWNTs as a reinforcing agent and triarylsulfonium hexafluoroantimonate (TASHFA) as an initiator. The flexural modulus was measured by UTM (universal testing machine). At this point, the flexural modulus factor exhibits an upper limit at 0.1 wt% MWNT. The thermal properties had improved by increasing the content of MWNT in the result of TGA (thermogravimetric analysis). However, they were decreased with increasing the radiation dose. The change of glass transition temperature by the radiation dose was characterized by DMA (dynamic mechanical analysis).

• 철학연구
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• 제117권
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• pp.249-273
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• 2011

큰 틀에서 이 글은 철학적 성찰이 비쥬얼로 무대화되는 극장(劇場 movie theater)과 영화적 상상력이 개념으로 결정(結晶)되는 극장(極場 extreme field)에 관한 담론이다. 들뢰즈와 홍상수는 이 극장의 환유들이다. 이 환유들은 서로 긴밀하게 내통한다. 홍상수의 영화는 들뢰즈의 경험론을 이미지로 전시해주고, 들뢰즈의 유목론은 홍상수의 카메라를 이론으로 갈무리한다. 들뢰즈의 어휘로 말하자면 <돼지가 우물에 빠진 날>에서 <하하하>에 이르는 10편의 필모그래피를 채운 홍상수의 작품들은 어떤 국가장치로도 포획될 수 없는 전쟁기계들이다. 그것들은 영화관 내부에서만이 아니라 외부에까지 '되기'(becomming)의 생성을 확장해나간다. 우리의 관심사는 그 작품들의 '되기'를 계열선 위에 배치하는 게 아니라 탈주선 위로 방면하는 것이다. 차이, 되기, 놀기 등으로 유쾌하고 발랄하게 전개되는 홍상수의 영상들이 기존의 영화문법에 보내는 통렬한 야유를 들뢰즈 유목론의 맥락에서 조명해본다.

• 한국농공학회논문집
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• 제61권6호
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• 산업경영시스템학회지
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• 제44권3호
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• 산업경영시스템학회지
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• 제44권3호
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.

• 한국결정성장학회지
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• 제30권6호
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• pp.271-276
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• 2020

본 연구에서는 유기 규소 폴리머(organosilicon polymer)인 폴리카보실란(polycarbosilane, PCS)을 사용하여 비정질 탄화규소 블록을 제조를 진행하였다. 다양한 형상의 치밀한 탄화규소 블록은 큐어링된 PCS 미세분말을 일축가압성형기를 통해 2~8 ton 하중을 가한 후 1100℃, 1200℃, 1300℃, 1400℃의 열처리 과정을 거쳐 제조되었으며, 물리적 화학적 특성 분석을 위해 열중량분석기(TGA), 주사전자현미경(SEM), 에너지분광분석법(EDS), 만능시험기(UTM)을 이용하였다, 제조된 탄화규소 성형체는 열분해 온도가 증가함에 따라 SiO와 CO 가스로의 분해가 발생하였고, 비정질의 구조에서 β-SiC 결정입자가 성장함을 보였다. 또한, 밀도와 굴곡강도는 1100℃의 열분해 온도에서 제조된 탄화규소 성형체가 1.9038 g/㎤과 6.189 MPa으로 가장 높았다. 제조된 비정질 탄화규소 블록은 이전에 보고된 마이크로파 도움 발열체와 같이 다른 분야에 적용 가능할 것으로 기대된다.

• 응용통계연구
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• 제34권1호
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• pp.99-114
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• 2021

반도체 제조 산업에서는 Big Data에 기초한 Smart Factory 도입과 적용이 가시화되면서 생산 공정의 각 단계에서 수집 가능한 다양한 센서(sensor) 데이터를 활용하여 공정 이상 탐지 및 최종 수율 예측 등에 다양한 분석 방법을 시도하고 있다. 현재 반도체 공정은 원료인 잉곳(ingot)에서 패키징(packaging) 작업 이전의 웨이퍼(wafer) 생산까지 500 600개 이상의 세부 공정과 이와 연계된 수천 개의 계측 공정으로 구성된다. 개별 계측 공정 내의 실제 계측 비율은 대상 제품 대비 0.1%에서 최대 5%를 넘지 못하고 계측 시점별로 일정하게 유지할 수 없다. 이러한 이유로 공정 각 단계의 정상 상태를 간접적으로 판단할 수 있는 장비 센서(sensor) 데이터를 활용하여 관리 여부를 판단하고자 하는 노력이 계속되고 있다. 본 연구에서는 장비 센서 데이터 기반의 공정 이상 탐지 프로세스를 정의하고 현재 적용 되고 있는 기술 통계량 기반 진단 방법의 단점을 보완하기 위해 FDA(Functional Data Analysis)방법을 활용하였다. 실제 현장 사례 데이터에 머신러닝을 이용하여 이상 탐지 정확도 비교를 통해 효과성을 검증하였다.