방사성물질의 방사선원항 추정을 위한 인공지능 및 드론 적용에 관한 연구

이장희,장승수,이민재,조우성,김주연,한상수,신성균,이윤종,김송현 (사)한국방사선산업학회 2022 방사선산업학회지 Vol.16 No.4

The reliable prediction of air concentrations due to the atmospheric release of radioactivematerials under accidents at nuclear facilities is greatly valuable in guiding an effective and timelyresponse. Those predictions start with quantifying the radiation source terms released to the environmentin nuclear facilities, but a difficulty in their quantification is uncertainties of the results obtainedfrom drones are contained in atmospheric dispersion models and input variables consisting the models. A try to apply the artificial neural network for a quantitative prediction of the radiation source termsis introduced for an acute release under an accident in nuclear facilities in this paper. Deep neuralnetwork (DNN) model is employed for predicting them and two dimensional simulation by advectiondispersion equation using the upwind scheme, of which is a numerical method based on Euleriansystem, is introduced. To testify the capability of predicting source terms, the machine learning byDNN model is then applied to decide the points capable of predicting source terms and radioactiveconcentrations released to the atmosphere with the availability of predictions. The accuracy metrics ofR2 score for regression and F1 score for classification are to be 0.99, respectively and the consumedmemory by DNN model is reduced about 1,800 times compared to the results using database from theacute release scenario. Score solutions by DNN are driven to the result that DNN model can be fast andaccurate in predicting the source terms with the information of meteorological data and contaminateddata. It is concluded the prediction of radioactive materials driven to the atmospheric dispersion modeland the artificial neural network can be faster and more reliable one of source terms released to theenvironment.

대화식 데이터 마이닝 기법을 활용한 자동차 보험사의 인입 콜량 예측 사례

백웅(Baek Woong),김남규(Kim Namgyu) 한국지능정보시스템학회 2010 지능정보연구 Vol.16 No.3

Due to the wide spread of customers’ frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert’s personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert’s subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert’s best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert’s domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

Demographic data is more predictive of component size than digital radiographic templating in total knee arthroplasty

Wallace Stephen J.,Murphy Michael P.,Schiffman Corey J.,Hopkinson William J.,Brown Nicholas M. 대한슬관절학회 2020 대한슬관절학회지 Vol.32 No.-

Preoperative radiographic templating for total knee arthroplasty (TKA) has been shown to be inaccurate. Patient demographic data, such as gender, height, weight, age, and race, may be more predictive of implanted component size in TKA.A multivariate linear regression model was designed to predict implanted femoral and tibial component size using demographic data along a consecutive series of 201 patients undergoing index TKA. Traditional, two-dimensional, radiographic templating was compared to demographic-based regression predictions on a prospective 181 consecutive patients undergoing index TKA in their ability to accurately predict intraoperative implanted sizes. Surgeons were blinded of any predictions. Patient gender, height, weight, age, and ethnicity/race were predictive of implanted TKA component size. The regression model more accurately predicted implanted component size compared to radiographically templated sizes for both the femoral ( P = 0.04) and tibial ( P < 0.01) components. The regression model exactly predicted femoral and tibial component sizes in 43.7 and 43.7% of cases, was within one size 90.1 and 95.6% of the time, and was within two sizes in every case. Radiographic templating exactly predicted 35.4 and 36.5% of cases, was within one size 86.2 and 85.1% of the time, and varied up to four sizes for both the femoral and tibial components. The regression model averaged within 0.66 and 0.61 sizes, versus 0.81 and 0.81 sizes for radiographic templating for femoral and tibial components. A demographic-based regression model was created based on patient-specific demographic data to predict femoral and tibial TKA component sizes. In a prospective patient series, the regression model more accurately and precisely predicted implanted component sizes compared to radiographic templating.Prospective cohort, level II.

실적 자료에 의한 공동주택 하자보수비용 예측모형 개발 방안

김병옥,제영득,송호산,이상범 한국건축시공학회 2011 한국건축시공학회지 Vol.11 No.5

공동주택 건설공사는 많은 기술자들이 참여하여 작성한설계 도서를 토대로 다양한 공종이 연계되어 발생되며, 이로 인해 예기치 못한 설계상 실수나 자재 결함 및 공사 중의잘못이 중첩되어 하자가 발생하게 된다. 건설업체는 준공된 건축물을 일정기간 동안 하자보수를 실시해야 하며, 이를 위해 하자보수비용을 효율적으로 예측하여 사업계획을 수립하게 된다. 하자발생은 정확한 예측이어렵기 때문에 실적자료를 기반으로 예측하게 된다. 국내공동주택의 경우 하자보수비용 관련 자료가 미흡하여 이를예측하는 방안 등이 거의 없는 실정이다. 따라서 본 연구에서는 준공후 10년의 실적자료를 기반으로 공급유형 및 지역별 하자보수비용을 예측할 수 있는 모형을 개발하고자 한다. The work of constructing apartment housing involves various fields of industry that are linked to each other, and is based on a design document prepared by multiple technicians and architects. Consequently, design errors, material flaws or faulty construction works can cause defects, which sometimes overlap with each other. Construction companies should repair any defects found in a completed building within a specified period of time, and to do this, should establish a business plan by efficiently predicting the cost of defect repair. As it is very difficult for companies to accurately predict the occurrence of defects, historical performance data is used as a base. For domestic apartment housing units,data on the cost of defect repair is insufficient, so there are hardly any methods that can be used to make precise predictions. Therefore, the intent of this study is to develop a model that can predict the cost of defect repair by supply type and area, based on historical performance data with ten years worth of post-completion.

기계학습을 이용한 회화 감성 예측 모델에 관한 분석 연구

이태민 차세대컨버전스정보서비스학회 2021 디지털예술공학멀티미디어논문지 Vol.8 No.3

Techniques for predicting emotions in images have been studied a lot. As machine learning and deep learning technologies developed, more studies were conducted. Among the images, artworks in particular are very related to emotions. In general, artists often put their emotions into their works. Emotions are controlled by artistic features such as symmetry and composition, which combine physical elements such as color and texture. In this study, these features are extracted and analyzed from paintings. Features that are expected to affect emotions in paintings are extracted and used to predict emotions. Various machine learning models are built by extracted physical features such as color, line, texture, etc. and artistic features such as symmetry and color combination from a given painting. Through the built machine learning models, this paper analyze which machine learning models are suitable for the most relevant characteristics and emotional extraction in conversation-emotional predictions. Finally, we verify the legitimacy and accuracy of machine learning models by comparing them with predictive models based on deep learning. 이미지에서 감성을 예측하는 기술들은 많이 연구되어 지고 있다. 기계학습 및 딥러닝 기술들이 발전함에 따라서, 더 많은 연구들이 진행되었다. 이미지중에서도 특히 예술작품들은 감성과의 연관이 매우 크다. 일반적으로 예술가들이 자신의 감성을 작품에 넣는 경우가 많기 때문이다. 이런 감성들은 색상, 질감 등의 물리적 요소들이 결합된 대칭성, 구도 등의 예술적 요소들로 제어가 된다. 본 연구에서는 이런 특징들을 회화로부터 추출 및 분석한다. 회화에서 감성에 영향을 미칠 것으로 예상되는 특징들을 추출하여 이를 감성 예측에 활용한다. 주어진 회화로부터 색상, 선, 질감등의 물리적 특징과, 대칭성, 색상조합 등과 같은 예술적 특징을 추출하여, 다양한 기계학습 모델을 제작한다. 제작된 기계학습 모델들을 통해 회화-감성 예측에서 가장 관련이 깊은 특징들 및 감성 추출에 어울리는 기계학습 모델이 무엇인지 분석한다. 최종적으로 딥러닝 기반의 예측 모델과의 비교를 통해 기계학습 모델의 정당성 및 정확도에 대해 검증한다.

Framingham Heart Study의 Heart Age Predictor를 활용한 한국인 심장나이 추이분석

조상옥(Sang Ok Cho) 한국산학기술학회 2019 한국산학기술학회논문지 Vol.20 No.8

본 연구는 Framingham Heart Study의 심장나이 예측 모형을 활용하여 심장나이의 추이를 관찰하여 한국인 심혈관질환 발생 위험을 평가해보고자 하였다. 연구대상은 2005년~2013년 국민건강영양조사 자료를 이용하여 30세~74세 대상자 중 심혈관질환 기왕력이 없고, 모형의 결정요인에 해당하는 자료의 결손이 없는 20,012명을 연구대상으로 하였다. 이들에 대해 Framingham Heart Study 비실험실 자료를 이용하는 심장나이 산정 모형을 적용하여 심장나이를 계산하였으며 성별로 심장나이와 실제나이와의 차이, 연령대별 차이, 10년 이상 초과율, 지역별 차이에 대해 연도별 추이를 관찰하였다. 자료분석은 SAS 9.3으로 수행하였으며 가중치를 적용한 복합표본설계분석을 수행하였다. 연구결과 심장나이와 실제나이의 평균 차이는 남성은 2005년에 7.8세, 2013년 7.7세, 여성은 2005년 1.2세 2013년 1.2세로 남성이 여성보다 컸고, 연령대가 증가할수록 나이차이가 많아졌으며, 연도별로 뚜렷한 추이 변화는 없었다. 심장나이가 실제 나이보다 10년 이상 초과한 비율은 남성은 2005년에 35.0%, 2013년에 34.8%, 여성은 2005년에 17.7%, 2013년에 18.7%로 남성이 여성보다 거의 두 배 정도 높았으며 연령대가 증가할수록 차이가 많이 났다. 지역별로 차이를 보였으며 남녀 차이가 많았다. 본 연구결과로 볼 때 한국인의 10년 내 심혈관질환 발생 가능성은 상당히 높은 수준이었다. 본 연구에서 사용한 심장나이는 미래의 심혈관질환 발병 위험을 간단하고 편리하게 예측할 수 있는 유용한 종합지표로 사용될 수 있으며, 이를 한국인 심혈관질환 예방을 위한 경고효과와 계도목적으로 현장에서 공중보건 관리에 활용되기를 제안한다. 한국형 심장나이 예측 모형의 개발을 위한 심층 연구도 필요하다. The purpose of this study is to observe the trends of heart age of Koreans by using the predictor of heart age of the Framingham Heart Study. The subjects were 20,012 adults aged 30~74 years who were enrolled in the Korean National Health and Nutrition Examination Survey from 2005~2013. They filled in the determinants data and they had no history of cardiovascular disease (CVD). The heart age was calculated using a non-laboratory based model of prediction. The difference of heart age and chronological age, and the rate of excessive heart age over 10 years were calculated. The annual trend, the difference according to gender, the age bracket and geographic region, the heart age were all evaluated. Data analysis performed using the SAS program (version 9.3). Complex designed analysis was done. The heart age showed differences according to gender, age bracket and geographic region. The heart age is a useful comprehensive indicator for predicting the CVD events in the near future. So, it could be used for the purposes of exercising caution and guidance on CVD for administering medical care. It is strongly recommended to use heart age as an indicator for customized medical management to focus efforts on relatively vulnerable subjects and their factors for CVD. Further study on Koreans" customized heart age is needed.

The Evaluation Distribution of Runoff Value on Hydroelectric Potential Change-Based RCPs Scenarios and Soft-Computing: A Case Study

Jin Ge,Hong Rongjing,Lu Yuquan,Gholinia Fatemeh 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4

Severe climate change, caused by the rise of industry and human activities, is one of the world's major issues affecting energy-generating resources. Anticipating hydropower potential is essential for developing, managing, and operating an optimal hydropower plant. The hydropower potential over the next 20 years is estimated in this study based on climate change. In addition, a novel approach for more accurate runoff estimation has been developed in this work, based on the direct influence of runoff on hydropower potential. The Modified Aquila Optimizer (MAO) algorithm was used to optimize this Deep Learning Neural Network (DLNN) model. The runoff is expected to decrease in the following years, according to the improved model's simulation. The rate of change of hydropower potential will fluctuate from a minimum of around − 112.4 MW to a high of about − 171.23 MW, according to predictive potential predictions. Rising temperatures and reduced rainfall in the following years will cause these negative changes in hydropower capacity.

Construction and verification of nonparameterized ship motion model based on deep neural network

Wang Zongkai,Im Nam-kyun 한국항해항만학회 2022 한국항해항만학회 학술대회논문집 Vol.2022 No.2

A ship’s maneuvering motion model is important in a computer simulation, especially under the trend of intelligent navigation. This model is usually constructed by the hydrodynamic parameters of the ship which are generated by the principles of hydrodynamics. Ship’s motion model is a nonlinear function. By using this function, ships’ motion elements can be calculated, then the ship’s trajectory can be predicted. Deeping neural networks can construct any linear or non-linear equation theoretically if there have enough and sufficient training data. This study constructs some kinds of deep Networks and trains this network by real ship motion data, and chooses the best one of the networks, uses real data to train it, then uses it to predict the ship’s trajectory, getting some conclusions and experiences.

빅데이터 군집 분석을 이용한 학습성취도 예측

고수정(Sujeong Ko) 한국디지털콘텐츠학회 2018 한국디지털콘텐츠학회논문지 Vol.19 No.9

As the value of using Big Data is increasing, various researches are being carried out utilizing big data analysis technology in the field of education as well as corporations. In this paper, we propose a method to predict learning achievement using big data cluster analysis. In the proposed method, students in Korea Children and Youth Panel Survey(KCYPS) are classified into groups with similar learning habits using the Kmeans algorithm based on the learning habits of students of the first year at middle school, and group features are extracted. Next, using the extracted features of groups, the first grade students at the middle school in the test group were classified into groups having similar learning habits using the cosine similarity, and then the neighbors were selected and the learning achievement was predicted. The method proposed in this paper has proved that the learning habits at middle school are closely related to at the university, and they make it possible to predict the learning achievement at high school and the satisfaction with university and major.

흉골 골절 환자에서 심혈관계 동반 손상의 예측 인자와 응급 심장 초음파의 적응증

김찬웅,류지영,전영진 梨花女子大學校 醫科大學 醫科學硏究所 2001 EMJ (Ewha medical journal) Vol.24 No.1

Objective : To determine the predicting factors related to cardiovascular injuries and To suggest a clinical indication for emergency echocardiography in sternal fractures. Materials and Results : A total mumber of 40 patients with sternal fractures a over 5-year period were retrospectively assessed on clinical, echocardiographic and biochemical status. We analyzed the following 4 factors as predicting factors for cardiovascular injuries in sternal fractures : 1) presence of restraint, 2) presence of associated injuries, 3) presence of a past medical history involving cardiovascular system, 4) Revised Trauma Score(RTS). We, also, assessed the utility of conventional diagnostic methods for cardiovascular injuries, such as ECG, chest X-ray, and enzyme levels. Based on the methods, we tried to infer an indication for emergency echocardiography in sternal fractures. Results : The presence of a past medical history involving cardiovascular system and abnormal RTS on admission were significant predicting factors. Emergency echocardiography was performed according to the predicting factors and the results from conventional evaluations. These data can suggest that indications for emergency echocardiography in sternal fractures include as 1) if more than two studies reveal abnormality without any significant predicting factors. 2) if more than one study reveal abnormality with any significant predicting factors. Conclusion : The past medical history involving cardiovascular system and initial vital signs imply the presence of associated cardiovascular injuries in sternal fractures. And if possible, emergency echocardiography is recommended.