• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.86-86
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• 2018

본 연구는 한국의 도시재생과 스마트 물 관리의 정책 분석을 위하여 도시재생과 스마트 물 관리의 등장 배경, 주요 현안 및 연계성을 모색하고 도시재생방안으로서 스마트 물 관리체계의 가능성을 검토하는 것을 목적으로 한다. 1950년대의 도시재건(Urban Reconstruction)과 1970~80년대의 도시재개발(Urban Renewal, Urban Redevelopment) 등의 정비 사업은 물리적 환경정비에 초점을 맞추었다. 그러나 1990년대 환경문제가 세계적 이슈로 등장하면서 교외지역 난개발 문제에 대한 대응책이 필요하게 되었고 도시의 물리 환경적, 산업 경제적, 사회 문화적 측면을 부흥시키는 도시재생 접근법이 출현하였다. 한국 정부는 2017년부터 시작한 '도시재생 뉴딜사업'의 일환으로 스마트 기술을 적용한 도시재생사업을 통해 스마트도시 선도국가 도약과 세계적 흐름에 부합하는 도시성장을 기대하고 있다. 1980년대 초 등장한 스마트 기술은 2000년대 들어와 스마트 도시, 스마트 인프라, 스마트 그리드 등의 분야로 확대, 진보하였다. 물 분야의 스마트 기술은 2009년 스마트워터그리드 이니셔티브(Smart Water Grid Initiative)의 발족과 함께 IBM, CISCO, Intel 등의 IT 기반 물 관리 워킹그룹 형성, Suez, Veolia, Siemens 등 수처리 기업의 스마트워터그리드 분야 진출 모색과 함께 발전하기 시작하였다. 이후 2012년 유엔 스마트 물 관리 포커스 그룹(ITU-T SG 5)의 스마트 물 관리 표준화 연구가 착수되었고 한국은 국토교통부 건설교통기술 연구 개발사업 중 하나로 스마트 물 관리 장기 연구 사업을 시작하였다. 스마트 물 관리는 수자원 및 상하수도 관리의 효율성 제고를 위하여 스마트 미터, 센서, 디지털지도제작 등 ICT를 이용한 차세대 물 관리시스템이라고 정의할 수 있다. 구체적인 대상 분야를 고려한다면 하천수, 우수, 지하수, 하폐수처리수, 해수담수 등 다양한 수자원의 관리, 물의 생산과 수송, 사용한 물의 처리 및 재이용 등 물 관리 전 분야를 포함한다. 그러나 스마트 물 관리의 용어와 개념을 처음으로 도입한 미국 등 선진국과 관련기업들은 스마트 물 관리를 '스마트 워터 미터, 센서, 첨단 모델링, 수문 지도제작, 스마트 관개농업, 자동화 로봇 등 다양한 기술을 통합적으로 운영하는 지능적인 수자원 관리를 위한 정보네트워크'로 정의한다. 일찍이 도시재생으로의 패러다임 전환을 실시한 영국 및 일본과 달리 한국의 도시재생은 개념, 구성요소, 범위, 사업방식 등의 여러 가지 측면에서 아직 형성단계에 있다. 또한 한국의 스마트 물 관리 논의는 개념정립 측면에서 심층적 논의가 거의 부재하였다. 기존의 논의들은 수자원 혹은 상하수도서비스 분야에서의 연구결과와 기술개발성과를 기계적으로 적용하고 확대하는 측면만을 부각시켰다. 그러나 이와 같은 스마트 물 관리에 대한 논의는 정보통신기술과 물 관리 서비스를 단편적으로 연결하고 적용범위를 제한할 수도 있다는 점에서 한계성이 있다. 본 연구는 국내외 문헌검토를 바탕으로 한국의 도시재생과 스마트 물 관리의 정책을 분석하고 지금까지 별개로 간주된 두 개념의 장점을 융합하여 향후 지속가능한 도시개발 사업으로서의 가능성을 검토하고자 한다.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.27-35
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• 2021

With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.308-317
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• 2022

Researches are underway to efficiently reduce the size of video data transmitted and stored in the image analysis process using deep learning-based machine vision technology. MPEG (Moving Picture Expert Group) has newly established a standardization project called VCM (Video Coding for Machine) and is conducting research on video encoding for machines rather than video encoding for humans. We are researching a multitask that performs various tasks with one image input. The proposed pipeline does not perform all object detection of each task that should precede object detection, but precedes it only once and uses the result as an input for each task. In this paper, we propose a pipeline for efficient multitasking and perform comparative experiments on compression efficiency, execution time, and result accuracy of the input image to check the efficiency. As a result of the experiment, the capacity of the input image decreased by more than 97.5%, while the accuracy of the result decreased slightly, confirming the possibility of efficient multitasking.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.11-18
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• 2022

Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• Journal of the Korean Institute of Gas
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• v.26 no.6
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• pp.9-15
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• 2022

In order to verify the durability of the high-pressure hydrogen tank in the operating pressure range, a hydraulic rupture test should be performed. However, if the bubbles generated by the initial injection process of water are attached to the inner wall of the tank and remain, a sudden pressure change of the bubbles during the rupture of the pressurized tank may cause shock and noise. Therefore, in this study, the flow velocity required to remove the bubbles remaining on the inner wall of the tank was predicted through simplified formulas, and the shape of the injection nozzle to maintain the flow velocity was determined based on the shape of the hydrogen tank for the hydrogen bus. In addition, a numerical model was developed to predict the change in flow velocity according to the inlet pressure, and an experiment was performed through a model tank to prove the validity of the prediction result. As a result of the experiment, the flow velocity near the tank wall was similar to the predicted value of the analysis model, and when the inlet pressure was 1.5 to 5.5 bar, the minimum size of the removable bubble was predicted to be about 2.2 to 4.6 mm.

• Korean Journal of Culture and Social Issue
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• v.28 no.3
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• pp.513-527
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• 2022

This study was conducted to examining the socio-cultural impact of the COVID-19 pandemic that swept the world around 2020, and the transformation of norms and social problems due to COVID-19. For this, the characteristics of changes in the socio-cultural norms of the 14th century European Black Death, a representative example of the pandemic, were derived, and based on this, the COVID-19 pandemic was analyzed. The Black Death served as an opportunity to change social norms based on the existing religious authority and the power of the feudal system to the Enlightenment. The population declination and labor shortage also promoted commercialization and mechanization. Printing, which spread during this period, led to the popularization of knowledge, which raised the level of thinking and led to epochal scientific development. This became the foundation of the Industrial Revolution. Like the recent Black Death, COVID-19 has triggered changes in social norms. The technological environment of metaverse, a mixture of virtual and reality, has changed the norm of a consistent identity into free and open identities exerting various potentials through alternate characters. In addition, meme, which are about people being friendly to those with the same worldview as him on the metaverse, weakened the sense of isolation in non-face-to-face situations. Artificial intelligence (AI), which developed during the COVID-19 pandemic, has entered the stage of being used for creative activities beyond the function of assisting humans. Discussions were held on what new social problems would be created by the social norms changed due to the COVID-19 pandemic.

• Journal of Christian Education in Korea
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• v.70
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• pp.141-173
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• 2022

The purpose of this study is to illuminate depersonalization in the flow of technological revolution and to present a Christian SARAMDAUM education that aims for a new human image. It represents the Christian SARAMDAUM education that adapts to, mediates, and offers alternatives to the technological and human evolutionary flow of the machine age. The purpose of education for this purpose is to aim for 'Homo Creators', creative human beings presented as a new human image in the age of technological revolution. The educational goal is to nurture creative human beings through creative interpretation, creative integration between disciplines, and personal dialogue in the post-mechanical/ post-conventional paradigm. The content of the education is a conversation with the SARAMDAUM that consiliences the characteristics of post-machine and post-convention. The educational method utilizes Edu-Tech and AIED(Artificial Intelligence in Education) to realize systemic thinking and SARAMDAUM dialogue of technology. In addition, the composition of teachers and learners, educational environment and educational evaluation is presented. The significance of this study is that from the point of view of Christian education, the identity of human beings in the era of the technological revolution has been identified, and research on the creative image of the human being is newly attempted, and the direction of Christian SARAMDAUM education aimed at this is presented. This can be said to be a Christian education that emphasizes the essential characteristics of human beings while accommodating the era of technological revolution.