• 산업경영시스템학회지
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• 제44권4호
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• 한국산업융합학회 논문집
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• 제24권6_2호
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• pp.699-706
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• 2021

Existing license plate recognition system is used as an optical character recognition method, but a method of using deep learning has been proposed in recent studies because it has problems with image quality and Korean misrecognition. This requires a lot of data collection, but the collection of license plates is not easy to collect due to the problem of the Personal Information Protection Act, and labeling work to designate the location of individual license plates is required, but it also requires a lot of time. Therefore, in this paper, to solve this problem, five types of license plates were created using a virtual Korean license plate generation program according to the notice of the Ministry of Land, Infrastructure and Transport. And the generated license plate is synthesized in the license plate part of collectable vehicle images to construct 10,147 learning data to be used in deep learning. The learning data classifies license plates, Korean, and numbers into individual classes and learn using YOLOv5. Since the proposed method recognizes letters and numbers individually, if the font does not change, it can be recognized even if the license plate standard changes or the number of characters increases. As a result of the experiment, an accuracy of 96.82% was obtained, and it can be applied not only to the learned license plate but also to new types of license plates such as new license plates and eco-friendly license plates.

• 정보화정책
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• 제29권2호
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• pp.66-90
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• 2022

급격한 디지털 전환과 코로나19 대유행에 따른 비대면 디지털 경제의 발전은 시장참여자들 간의 이해 충돌, 관련 법·제도의 지체 등 다양한 문제의 해결 필요성을 높이고 있다. 본 연구에서는 비대면 디지털 경제의 정의와 특징을 이론적 고찰을 통해 명확히 하고, 이의 발전을 위해 개선이 필요한 규제쟁점과 개선방안을 뉴스 기사 분석과 전문가 인터뷰를 통해 도출하였다. 이론적 고찰에서는 비대면 디지털 경제가 기존의 디지털 경제가 비대면·비접촉 활동 중심으로 전환되는 과정이며, 초지능화, 초연결화, 초융합화, 초개인화, 초자동화, 초정밀화, 초격차 및 초신뢰라는 여덟 가지 초(超)혁신(8 hypers)의 특성을 지니는 것을 확인하였다. 한편, 뉴스 기사분석과 전문가 인터뷰를 통해 비대면 디지털 경제로의 전환에 따른 기존·신규 사업자의 충돌, 기본권이나 법적 권리 침해, 사회적 가치나 윤리관과의 대립, 시장참여자 간의 갈등, 제도·규제의 부재, 시장 지배력 남용 등과 같은 규제 문제를 확인하고, 이의 해소를 위한 다양한 개선방안을 도출하였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권5호
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• pp.221-228
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• 2022

인공지능 기술의 적용으로 로봇이 실생활에서 효율성 높은 서비스를 제공할 수 있게 되었다. 본 연구에서는 단순 반복적 작업을 하는 산업용 매니퓰레이터와 달리 서비스 로봇 분야에서 장소의 제약 없이 단독으로 또는 협업하여 사용하기 위한 6자유도 로봇 팔의 설계방법과 지능적인 물체 검출 및 이동 방법을 제시하고 성능을 검증하였다. 로봇 팔에 포함된 임베디드 보드의 ROS 환경에서 깊이 카메라와 딥러닝을 이용하여 로봇팔은 물체를 검출하고, 역기구학 해석을 통해 물체 영역으로 이동한다. 또한 물체와 접촉 시 힘센서 값의 분석을 통해 물체를 정확히 잡고 이동하는 동작이 가능하게 하였다. 제작한 로봇 팔에 대한 성능검증을 위하여 딥러닝과 영상처리를 통한 물체의 정확한 위치 산출, 모터 제어 및 물체 분리에 대한 실험을 하였으며, 실제 동작 여부를 확인하기 위하여 카페에서 흔히 사용하는 다양한 컵들을 분리하는 실험을 수행하였다.

• 한국음향학회지
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• 제41권5호
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• pp.534-544
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• 2022

초음파진단기는 실시간으로 손쉽고 안전하게 환자의 병변을 관찰할 수 있는 등의 다양한 장점으로 인해 병원, 의원 등의 의료현장에서 널리 활용되고 있다. 이 초음파진단기 신호처리 블록 중 하나인 빔포머는 초음파진단기 영상의 화질을 결정하는 부분 중 하나이다. 초음파진단기 기술의 발전과 더불어 이 빔포머와 관련된 기술들도 장기간 많은 발전을 이루어 왔는데, 이 기술들 중 하나의 주요 방법인 합성구경영상법(Synthetic Aperture Imaging method, SAI)은 프로브를 통해 수신한 신호가 운반해 온 환자로부터의 정보를 최대로 활용하기 위한 방법으로, 1990년대 경 최초로 초음파진단기에 도입된 이래 획기적 화질 향상에 기여해 왔고, 수십년동안 다양한 형태의 발전을 거쳐왔다. 이 논문에서는 이러한 진화과정을 살펴보고, 이 기술의 미래의 발전 방향을 예상해 본다.

• 디지털융복합연구
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• 제20권2호
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• pp.261-268
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• 2022

최근 급격한 고령화, 핵가족화 현상에 따라 외로움과 우울증으로 고통 받는 독거노인 인구도 크게 증가하고 있다. 본 논문에서는 이런 고령자에게 주거환경과 건강상태에 따라 IT의 도움으로 최적의 고령 맞춤형 서비스를 제공함으로써 삶의 만족도를 높이는 smart aging 시스템을 제안하고자 한다. 의학의 발달로 건강한 노년층이 증가함에 따라 사회 속에서 활동적으로 생활하고자 하는 고령자뿐 아니라, 시설에서의 돌봄이 필요한 고령자에 대해서도 IoT, AI(인공지능) 기술 및 메타버스 환경을 십분 활용하여 선진적인 고령자 맞춤형 지원시스템을 제공할 수 있다. 제안시스템은 병원(요양) 시설 및 재택으로 외로움으로 고통 받는 고령자에게 주거환경과 건강상태에 맞추어 현실공간과 가상공간에서 사회적 연결(social connection)을 제공하여 인간적인 만족감을 제공한다. 본 논문은 급변하는 사회 환경 변화에 AI와 메타버스 기술을 접목하고 주거환경과 건강상태에 따라 사용자 맞춤형 smart aging 시스템을 제공함으로써 미래 지향적 노인복지정책의 새로운 길을 제시할 수 있다.

• Advances in nano research
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• 제12권2호
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Computers and Concrete
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• 제29권 6호
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• 한국IT서비스학회지
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• 제21권3호
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• 치위생과학회지
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• 제22권4호
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• pp.233-240
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• 2022

Background: In modern society, rapid changes in the medical environment have required medical staff to access various information and be competent in active and effective problem-solving through collegial interactions. In line with these changes, universities are aiming to connect education. This study aimed to provide basic data of connected-convergence education by survey the awareness and needs of college students in health-related fields. Methods: This study included 122 college students from the health field. A survey regarding "the awareness and need of connected-convergence education" was conducted and general characteristics of the participants were collected from June to July 2022. Results: The awareness of connected-convergence education was low at 19.7%, but the intention to participate was high at 74.6%. Subject requirements were 18.0% for medical psychology, 13.5% for communication and counseling, 13.5% for medical artificial intelligence technology convergence, and 10.4% for sports health management. In the group showing high satisfaction with the major curriculum, the demand for connected education was also high. For efficient operation, it was investigated that it was necessary to secure specialized training courses, recognition of liberal arts credits, the right to register for courses equal to those of major students, and secure dedicated classrooms. Conclusion: Although the awareness and experience of connected-convergence education among the participants were low, the intention to participate was high. As such a plan to revitalize the university curriculum was required. It is timely to discuss the nurturing of convergence-type talents and multidisciplinary thinking skills. It is meaningful to provide basic data necessary for connected-convergence education in health-related fields at university. Universities should strive to enhance job competency in the health field by providing connected-convergence education based on student demands.