• Title/Summary/Keyword: 정보공학 방법론

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A Study on the Calculation of Stormwater Utility Fee Using GIS based Impervious Surface Ratio Estimation Methodology (GIS 기반 불투수율 산정방법론을 활용한 강우유출수 부담금 모의산정 방안 연구)

  • Yoo, Jae Hyun;Kim, Kye Hyun;Choi, Ji Yong;Lee, Chol Young
    • Journal of Korean Society on Water Environment
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    • v.37 no.3
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    • pp.157-167
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    • 2021
  • Korea needs to develop a rational system to separate stormwater utility fee from current sewerage fee. In this study, the scenario for calculating stormwater utility fee of Bupyeong-gu was suggested and the results were considered. For this purpose, the application of stormwater utility fee overseas and current domestic system were analyzed. A three step calculating scenario considering suitable domestic situation and impervious surface area was suggested. Water, sewerage usage, and hydrant data were collected. The total amount of water and sewerage fees for land use were calculated. The sewerage fee of Bupyeong-gu for the year 2014 was 21,685,446,578 won. Assuming that 40% of this amount was the cost associated to stormwater, the result showed that the fees for residential area in third step decreased by 0.77% compared to that of the first step. For commercial area, the stormwater utility fee decreased by 36.87%. For industrial area, although the consumption of water was similar to that of commercial area, the stormwater utility fee increased by 8.35%. For green area, the fee increased by 37.46%. This study demonstrated that the calculation of actual stormwater utility fee using impervious surface map and impervious Surface Ratio Estimation Methodology developed in previous studies is feasible.

Component-Z: A Formal Specification Language Extended Object-Z for Designing Components (Component-Z: Object-Z를 확장한 컴포넌트 정형 명세 언어)

  • 이종국;신숙경;김수동
    • Journal of KIISE:Software and Applications
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    • v.31 no.5
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    • pp.677-696
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    • 2004
  • Component-based software engineering (CBSE) composes reusable components and develops applications with the components. CBSE is admitted to be a new paradigm that reduces the costs and times to develop software systems. The high quality of component designs can be assured if the consistency and correctness among the elements of a component are verified with formal specifications. Current formal languages for components include only some parts of contracts between interfaces, structural aspects and behavioral aspects of component, component-based system, component composition and variability. Therefore, it is not adequate to use current formal languages in all steps of a component design process. In this paper, we suggest a formal language to specify component designs Component-Z. Component-Z extends Object-Z, adds new notations to specify components. It can be possible to specify interfaces, the inner structure of a component, inner workflows, and workflows among interfaces with Component-Z. In addition, Component-Z provides the notations and semantics to specify variability with variation points, variants and required interfaces. The relation between interfaces and components is defined with mapping schemas. Parallel operator is used to specify component composition. It can be possible to describe deployed components with the specifications of component-based systems. Therefore, the formal specification language proposed in this paper can represent all elements to design components. In the case study, we specify an account management system in a bank so that we show that Component-Z can be used in all steps of component design.

Development of Reliability-Based Design Program based on the MATLAB GUI Environment (MATLAB GUI 환경기반 신뢰성 설계기법의 개발)

  • Jeong, Shin-Taek;Ko, Dong-Hui;Park, Tae-Hun;Kim, Jeong-Dae;Cho, Hong-Yeon
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.22 no.6
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    • pp.415-422
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    • 2010
  • Development of the reliability-based design program in the GUI environment is inadequate for engineers familiar with the deterministic design to deal with the international design criterion based on the probabilistic design. In this study, the design program based on the GUI environment is developed in order to more efficiently input the design factor and more easily carry out the design works. The GUI environment is the GUIDE (Graphic User Interface Development Environment) tool supported by the latest MATALB version 7.1. In order to test the model reliability, the probabilities of failure (POF) on the breakwater armor block (AB) and gravity quay-wall (QW) in the sliding mode are computed using the model in the Level II and Level III. The POF are 55.4~55.7% for breakwater AB and 0.0006~0.0007% for gravity QW. A non-GUI environment program results of the POF are 55.6% for breakwater AB and 0.0018% for gravity QW. In comparison, the POF difference is negligible for breakwater AB because the exact input design parameters are available, whereas the large POF difference, but within the same order, for gravity QW can be explained by the difference of the input design factors because of the poor input data information.

A Deep Learning Based Approach to Recognizing Accompanying Status of Smartphone Users Using Multimodal Data (스마트폰 다종 데이터를 활용한 딥러닝 기반의 사용자 동행 상태 인식)

  • Kim, Kilho;Choi, Sangwoo;Chae, Moon-jung;Park, Heewoong;Lee, Jaehong;Park, Jonghun
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.163-177
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    • 2019
  • As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

Review of Soil Vulnerability Assessment Tools in Korea and other developed countries (국내외 토양 취약성 평가 연구 동향)

  • Ki, Seo Jin;Kim, Kyoung-Ho;Lee, Hyeon Gyu;Shin, Kyung Hee
    • Journal of Korean Society of Environmental Engineers
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    • v.39 no.12
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    • pp.741-749
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    • 2017
  • This study aims to provide the technical considerations and implications for the development of soil vulnerability assesment tool based on the review of existing tools and case studies applied both domestically and internationally. For this study, we specifically investigated the basic theories and major features implemented in the screening models abroad. In contrast, one case study of prioritizing the vulnerable districts was presented to identify the research trends in Korea. Our literature review suggested that the characteristic of target areas and contaminants needed to be properly incorporated into soil vulnerability assessment because the current tools in Korea neglected these properties which prevented this tool from being used as a correct measure of soil management and prevention. We also reached the conclusion that in terms of technical aspect, the soil vulnerability assessment tool should be developed based on the physical theory and environmental data that were varied over space and time so that the end-users were able to readily and effectively screen soil vulnerability over large areas. In parallel with technical improvement, great effort needed to be devoted to develop an integrated environmental information system that increased the availability of data and shared various types of environmental data through enhanced multi-agency collaboration.

A Distributed Intelligent System for Multidisciplinary Design Optimization (다분야통합최적설계를 위한 지능형 분산 시스템)

  • 이재호;홍은지
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2000.11a
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    • pp.257-266
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    • 2000
  • 산업 및 가정용 기기들이 점차 복잡해짐에 따라 다양한 공학 분야의 해석 기술을 동시에 고려하면서 이들 원리를 적용한 최적의 설계를 결정하는 방법론의 필요성이 대두되고 있다. 다분야통합최적설계 또는 MDO(Multidisciplinary Design Optimization)라 일컫는 새로운 기술은 이러한 필요에 대응하는 기술로서 국내외적으로 활발한 연구가 진행되고 있다. 이러한 MDO 기술을 구현하는 소프트웨어와 하드웨어 복합 체계를 MDO 프레임웍(framework)이라 한다. 일반적으로 프레임웍이란 실제 응용프로그램의 용도에 맞는 주문제작(customization)이 가능한 일종의 전단계 프로그램이라 할 수 있다 MDO 프레임웍은 설계 및 해석 도구들간의 인터페이스를 제공하고, 이들 도구들이 사용하는 설계 데이터를 효율적으로 공유할 수 있도록 지원하여, 설계 작업을 정의, 실행, 관리하는 역할을 한다. 이러한 MDO 프레임웍은 설계 작업을 통합적으로 관리하고 자동화하여 설계 도구간의 데이터 전달과 변환에 소묘되는 설계자의 부담을 경감시키며 다분야 전문가가 참여하는 공통 작업 환경을 제공함으로써 설계 효율성을 증진시킨다. 본 논문에서는 이러한 효용을 달성하기 위한 MDO 프레임웍(framework)을 제시하고 프레임웍 설계의 논리적 근저와 타당성을 밝힌다. 본 논문에서 제안하는 다분야 통합 최적화를 위한 분산형 지능 시스템인 DisMDO는 사용자가 GUI를 동해서 편리하게 다분야통합최적화 문제를 해결할 수 있도록 지원하며, 제공되는 스크립트 언어를 동해서도 이를 정의할 수 있도록 지원하여 일괄처리도 가능하도록 한다. 또한, 집중화된 데이터베이스를 관리하여 다분야 전문가들이 공통의 데이터를 안전하게 공유할 수 있도록 지원하며, 외부에서 제공되는 해석 도구나 최적화 모듈을 손쉽게 프레임웍에 통합시킬 수 있도록 하는 인터페이스 제작기(factory) 기능을 제공한다.ackscattering spectroscopy, X-ray diffraction, secondary electron microscopy, atomic force microscoy, $\alpha$-step, Raman scattering spectroscopu, Fourier transform infrared spectroscopy 및 micro hardness tester를 이용하여 기판 bias 전압이 DLC 박막의 특성에 미치는 영향을 조사하였다. 분석결과 본 연구에서 제작된 DLC 박막은 탄소와 수소만으로 구성되어 있으며, 비정질 상태임을 알 수 있었다. 기판 bias 전압의 증가에 따라 박막의 두께가 감소됨을 알 수 있었고, -150V에서는 박막이 거의 만들어지지 않았으며, -200V에서는 기판 표면이 식각되었다. 이것은 기판 bias 전압과 ECR 플라즈마에 의한 이온충돌 효과 때문으로 판단되며, 150V 이하에서는 증착되는 양보다 re-sputtering 되는 양이 더 많을 것으로 생각된다. 기판 bias 전압을 증가시킬수록 플라즈마에 의한 이온충돌 현상이 두드러져 탄소와 결합하고 있던 수소원자들이 떨어져 나가는 탈수소화 (dehydrogenation) 현상을 확인할 수 있었으며, 이것은 C-H 결합에너지가 C-C 결합이나 C=C 결합보다 약하여 수소 원자가 비교적 해리가 잘되므로 이러한 현상이 일어난다고 판단된다. 결합이 끊어진 탄소 원자들은 다른 탄소원자들과 결합하여 3차원적 cross-link를 형성시켜 나가면서 내부 압축응력을 증가시키는 것으로 알려져 있으며, hardness 시험 결과로 이것을 확인할 수 있었다. 그리고 표면거칠기는 기판 bias 전압을 증가시킬수록 더 smooth 해짐을 확인하였다.인하였다.을 알 수 있었다. 즉 계면에서의 반응에 의해 편석되는 Ga에 의해 박막의 strain이 이완되면, pinhole 등의 박막결함

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Spatial Replicability Assessment of Land Cover Classification Using Unmanned Aerial Vehicle and Artificial Intelligence in Urban Area (무인항공기 및 인공지능을 활용한 도시지역 토지피복 분류 기법의 공간적 재현성 평가)

  • Geon-Ung, PARK;Bong-Geun, SONG;Kyung-Hun, PARK;Hung-Kyu, LEE
    • Journal of the Korean Association of Geographic Information Studies
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    • v.25 no.4
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    • pp.63-80
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    • 2022
  • As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

Podiatric Clinical Diagnosis using Decision Tree Data Mining (결정트리 데이터마이닝을 이용한 족부 임상 진단)

  • Kim, Jin-Ho;Park, In-Sik;Kim, Bong-Ok;Yang, Yoon-Seok;Won, Yong-Gwan;Kim, Jung-Ja
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.48 no.2
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    • pp.28-37
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    • 2011
  • With growing concerns about healthy life recently, although the podiatry which deals with the whole area for diagnosis, treatment of foot and leg, and prevention has been widely interested, research in our country is not active. Also, because most of the previous researches in data analysis performed the quantitative approaches, the reasonable level of reliability for clinical application could not be guaranteed. Clinical data mining utilizes various data mining analysis methods for clinical data, which provides decision support for expert's diagnosis and treatment for the patients. Because the decision tree can provide good explanation and description for the analysis procedure and is easy to interpret the results, it is simple to apply for clinical problems. This study investigate rules of item of diagnosis in disease types for adapting decision tree after collecting diagnosed data patients who are 2620 feet of 1310(males:633, females:677) in shoes clinic (department of rehabilitation medicine, Chungnam National University Hospital). and we classified 15 foot diseases followed factor of 22 foot diseases, which investigated diagnosis of 64 rules. Also, we analyzed and compared correlation relationship of characteristic of disease and factor in types through made decision tree from 5 class types(infants, child, adolescent, adult, total). Investigated results can be used qualitative and useful knowledge for clinical expert`s, also can be used tool for taking effective and accurate diagnosis.

Application Examples of Daecheong Dam for Efficient Water Management Based on Integrated Water Management (통합물관리 기반 효율적 물관리를 위한 대청댐 실무적용 사례)

  • Kang, Kwon-Su;Heo, Jun-Haeng
    • Proceedings of the Korea Water Resources Association Conference
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    • 2017.05a
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    • pp.85-85
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    • 2017
  • 효율적 물관리란 거대한 물순환 과정에서 인간이 편안한 삶을 사는데 필요한 물의 이용효율을 극대화하는 것이다. 과거의 물관리는 이원화된 수량과 수질관리, 수량중심에서는 용수공급과 홍수조절이 주요한 관심사였다. 현재는 과거의 물관리에 친수와 환경을 더한 복잡한 분야로 확대되고 있다. 통합물관리란 물을 최적으로 관리하기 위해 물관리 이해당사자간의 소통과 물 기술의 고도화를 기반으로 기존에 분산된 물관리 구성요소들(시설 정보, 수량 수질 등)을 권역적으로 관리하는 것을 말한다. 본 연구에서는 대청댐 방류에 따른 금강 하류부의 홍수추적을 위해 수행한 댐하류 소유역별 강우량 빈도분석 과정, 용담댐 방류를 고려한 대청댐 홍수도달시간 검토, Poincare Section과 신경망기법을 이용한 수문자료 예측, 추계학적 다변량 해석과 다변량 신경망해석에 의한 대청댐 유입량 산정과정, 보조여수로 건설에 따른 주여수로와 보조여수로간의 연계운영방안, 단계(관심, 주의, 경계, 심각)를 고려한 대청댐 확보수위 산정, 저수지 중장기 운영계획 수립과 댐 운영 기준수위를 결정하기 위해 누가차분방식으로 적용되는 갈수기 유입량 빈도분석에 대한 실무적용 사례를 소개하고자 한다. 강우량 빈도분석 과정은 L-모멘트방법(Hosking과 Wallis, 1993)을 적용하였고, 홍수도달시간 검토는 평균유속, 하류 수위상승 기점 영향검토, 수리학적 모형(FLDWAV, Progressive lag method 등)을 활용하였다. 카오스 이론을 도입하여 대청댐 수문자료의 상관성 검토 및 추계학적 모형을 이용한 모의발생을 유도하여 수문자료 예측을 시행하였다. 추계학적 모형과 신경망모형 연구의 대상은 대청댐으로, 시계열 자료는 댐의 월강우량, 월유입량, 최고기온, 평균기온, 최소기온, 습도, 증발량 등의 자료를 기반으로 하였다. 적용기간은 1981~2009년의 자료를 이용하여 2010년 1월부터 12월까지 12개월 동안의 월유입량을 예측하였다. 수문자료 해석의 기본이 되는 약 30년간의 자료를 이용하여 분석을 실시하였다. 대청댐의 유입량 예측을 위해 적용된 모형으로는 추계학적 모형인 ARMA모형, TF모형, TFN 모형 등이 적용되었고, 또한 신경망 모형의 종류인 다층 퍼셉트론, PCA모형 등을 활용하여 실측치와 가장 가깝게 근사화시키는 방법론을 찾고자 하였다. 또한, 기존여수로와 보조여수로 연계운영을 위해 3차원 수치해석을 통한 댐하류 안정성 검토 및 확보수위 산정을 통해 단계(관심, 주의, 경계, 심각)별로 대처가 가능한 수위를 산정하였다.

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Improving Efficiency of Food Hygiene Surveillance System by Using Machine Learning-Based Approaches (기계학습을 이용한 식품위생점검 체계의 효율성 개선 연구)

  • Cho, Sanggoo;Cho, Seung Yong
    • The Journal of Bigdata
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    • v.5 no.2
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    • pp.53-67
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    • 2020
  • This study employees a supervised learning prediction model to detect nonconformity in advance of processed food manufacturing and processing businesses. The study was conducted according to the standard procedure of machine learning, such as definition of objective function, data preprocessing and feature engineering and model selection and evaluation. The dependent variable was set as the number of supervised inspection detections over the past five years from 2014 to 2018, and the objective function was to maximize the probability of detecting the nonconforming companies. The data was preprocessed by reflecting not only basic attributes such as revenues, operating duration, number of employees, but also the inspections track records and extraneous climate data. After applying the feature variable extraction method, the machine learning algorithm was applied to the data by deriving the company's risk, item risk, environmental risk, and past violation history as feature variables that affect the determination of nonconformity. The f1-score of the decision tree, one of ensemble models, was much higher than those of other models. Based on the results of this study, it is expected that the official food control for food safety management will be enhanced and geared into the data-evidence based management as well as scientific administrative system.