• Asian Journal of Business Environment
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• 제14권2호
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• pp.1-10
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• 2024

Purpose: The purpose of this study is to investigate the essential factors contributing to the growth and success of smart cities, providing a comprehensive analysis of key elements that are crucial in fostering the development of smart cities. This study explored the impacts of technology-driven applications, corporate involvement, the role of experts, citizen co-creation, city-led strategy governance, and sustainable urban practices on overall attitudes towards smart cities. Additionally, the study examined the impact of overall attitude on the growth trajectory of the smart cities and satisfaction. Research design, data and methodology: To collect data, this study employed an online survey conducted by a reputable research organization. Data analysis involved the use of factor analysis, ANOVA, and regression analysis. Results: This study unveiled significant impacts of technology-driven applications, corporate involvement, the role of experts, citizen co-creation, city-led strategy governance, and sustainable urban practices on the overall attitudes. Furthermore, it demonstrated that the overall attitude significantly influences the growth trajectory of smart cities. Conclusions: This study identified key driving factors for smart city development, suggesting that the consideration of sustainable urban practices emerges as the most significant factor influencing the growth of the smart cities.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.113-122
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• 2022

At the point when it is check out ourselves, it might track down various information in each turn or part of our lives. Truth be told, information is the new main thrust of our advanced civilization and in this every day, "information-driven" world, security is the significant angle to consider to guarantee dependability and accessibility of our organization frameworks. This paper includes a new cryptographic algorithm for safe route traversal for data of smart cities which is a contemporary, non-hash, non-straight, 3D encryption execution intended for having information securely scrambled in the interim having a subsequent theoretical layer of safety over it. Encryption generally takes an information string and creates encryption keys, which is the way to unscramble as well. In the interim in another strategy, on the off chance that one can sort out the encryption key, there are opportunities to unravel the information scrambled inside the information string. Be that as it may, in this encryption framework, the work over an encryption key (which is created naturally, henceforth no pre-assurance or uncertainty) just as the calculation produces a "state" in a way where characters are directed into the Rubik block design to disregard the information organization.

• 정보화정책
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• 제26권3호
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• pp.69-89
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• 2019

제4차 산업혁명시대의 거주와 삶의 공간이 될 스마트 도시의 구현을 위해서는 거주민의 생활 정보, 건축물 및 시설물 정보 등 구체적이고 민감한 정보가 실시간으로 수집 처리 될 수밖에 없다. 발전하는 도시 기능과 개인 삶의 편의성이 높아지는 반면 개인정보의 노출 및 유출의 위협성도 동시에 높아 질 수밖에 없는 상황이다. 따라서 스마트 도시의 설계단계, 지능정보 기계설비의 기술개발 및 운용 기획단계에서부터 사전적 개인정보보호 설계 개념이 반영 되어야 한다. 이러한 측면에서 본 연구는 '개인정보보호 적용설계(Privacy by design)'개념의 정책화와 적용을 위한 연구를 수행 하였다. 연구 분석 결과를 보면 제도적인 측면, 산업적 측면 그리고 기술적 측면에서 이미 개인정보보호 적용설계(PbD)의 개념을 도입할 수밖에 없는 상황에 이르렀다. 실제로 이러한 현상은 유럽과 미국 등의 사례를 통해 확인할 수 있었다. 이에 우리나라 역시 스마트 도시의 경쟁력을 강화하기 위해서 스마트 도시의 핵심전략인 데이터 기반 경제를 확보하기 위한 전략으로 개인정보보호 적용설계(PbD)를 적극도입 해야함을 본 연구는 강조하였다. 이에 본 연구는 개인정보보호 적용설계(PbD)의 기본 속성이 반영된 법제 개선과 기술개발지원이 필요함을 정책제안으로 제시하였다.

• 디지털융복합연구
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• 제10권7호
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• pp.185-192
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• 2012

본 논문은 유비쿼터스 환경의 도시통합관제를 위한 실시간 데이터 처리 방안을 제시한다. 도시내 다양한 정보수집센서로부터 발생하는 데이터와 이벤트에 대해 분석하고 가공하여, 실시간으로 처리하는 것이 도시통합관제의 핵심이다. 본 논문은 다양하게 발생하는 이벤트 정보를 분석하고, 상황에 맞는 조치를 취하기 위한 판단모듈에 Rete 알고리즘을 이용한 추론엔진을 적용한다. 마지막으로, 본 논문에서 제안한 방법의 효율성을 평가하기 위해 시뮬레이터를 구성하여 성능을 테스트한다.

• Journal of Advanced Marine Engineering and Technology
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• 제32권5호
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• pp.663-669
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• 2008

A rapid growth of automobile industry has become a major cause for the environmental pollution of big cities, which has driven the emission regulation into extreme. The study of alternative fuel is one of the many researches for improving car emissions. In this study, the effect of an ethanol mixing rate on the engine performance of exhaust emissions, fuel consumption and a maximum torque is assessed for a gasoline engine without any retrofit. The result shows that maximum torque is not reduced in the range of ethanol mixing rate of 10 to 15%. CO and NOx is reduced with the increase of ethanol mixing rate and the fuel consumption remains in similar level.

• Wind and Structures
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• 제39권4호
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• pp.271-285
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• 2024

The multiple-fan wind tunnel is an important facility for reproducing target wind field. Existing control methods for the multiple-fan wind tunnel can generate wind speeds that satisfy the target statistical characteristics of a wind field (e.g., power spectrum). However, the frequency-domain features cannot well represent the nonstationary winds of extreme storms (e.g., downburst). Therefore, this study proposes a multiple-fan wind tunnel control scheme based on Deep Reinforcement Learning (DRL), which will completely transform into a data-driven closed-loop control problem, to reproduce the target wind field in the time domain. Specifically, the control scheme adopts the Deep Deterministic Policy Gradient (DDPG) paradigm in which the strong fitting ability of Deep Neural Networks (DNN) is utilized. It can establish the complex relationship between the target wind speed time series and the current control strategy in the DRL-agent. To address the fluid memory effect of the wind field, this study innovatively designs the system state and control reward to improve the reproduction performance based on historical data. To validate the performance of the model, we established a simplified flow field based on Navier Stokes equations to simulate a two-dimensional numerical multiple-fan wind tunnel environment. Using the strategy of DRL decision maker, we generated a wind speed time series with minor error from the target under low Reynolds number conditions. This is the first time that the AI methods have been used to generate target wind speed time series in a multiple-fan wind tunnel environment. The hyperparameters in the DDPG paradigm are analyzed to identify a set of optimal parameters. With these efforts, the trained DRL-agent can simultaneously reproduce the wind speed time series in multiple positions.

• 복식문화연구
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• 제25권5호
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• pp.708-717
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• 2017

Industrial innovation in Britain, during the eighteenth and nineteenth centuries, stimulated the introduction of the factory system and the migration of people from rural agricultural communities to urban industrial societies. The factory system brought elevated levels of economic growth to the purveyors of capitalism, but forced people to migrate into cities where working conditions in factories were, in general, harsh and brutal, and living conditions were cramped, overcrowded and unsanitary. Industrial developments, known collectively as the 'Industrial Revolution', were driven initially by the harnessing of water and steam power, and the widespread construction of rail, shipping and road networks. Parallel with these changes, came the development of purchasing 'middle class', consumers. Various technological ripples (or waves of innovative activity) continued (worldwide) up to the early-twenty-first century. Of recent note are innovations in digital technology, with associated developments, for example, in artificial intelligence, robotics, 3-D printing, materials technology, computing, energy storage, nano-technology, data storage, biotechnology, 'smart textiles' and the introduction of what has become known as 'e-commerce'. This paper identifies the more important early technological innovations, their influence on textile manufacture, distribution and consumption, and the changed role of the designer and craftsperson over the course of these technological ripples. The implications of non-ethical production, globalisation and so-called 'fast fashion' and non-sustainability of manufacture are examined, and the potential benefits and opportunities offered by new and developing forms of social media are considered. The message is that hand-crafted products are ethical, sustainable and durable.

• 한국산학기술학회논문지
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• 제22권1호
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• pp.415-423
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• 2021

스마트시티의 성공을 위한 주요한 전략 중 하나로 시민참여와 리빙랩이 주목받고 있다. 리빙랩은 기술의 최종 사용자인 시민들이 문제를 정의하고 해결하기 위한 대안의 탐색에 참여하며, 순환적 과정으로 대안을 검증하기 위한 실험을 반복한다. 본 연구의 목적은 도시문제 개선을 위한 시민참여형 온라인 커뮤니티 플랫폼의 운영모델을 제시하고, 이를 구현 및 테스트하여 활용 가능성을 확인하는 것이다. 이를 위해 도시문제 개선을 위한 시민참여형 온라인 커뮤니티 플랫폼의 운영모델을 제시하고, 운영모델이 추구하는 기능이 반영될 수 있도록 온라인 플랫폼을 설계 및 구현하였다. 최종적으로 경기도 고양시 일산서구 소재 오마초등학교를 사례로 하여 기능에 대한 파일럿 테스트를 수행하였다. 본 연구에서 제시한 운영모델은 도시의 여러 문제가 밀집될 수 있는 보행환경과 위험 환경에 취약한 어린이를 염두에 두고 계획되었다. 결과적으로 참여자들 간에 도시문제 사례의 공유와 이에 대한 의사소통이 이루어지는 것을 통해 커뮤니티 플랫폼의 활용 가능성을 확인하였다. 커뮤니티 플랫폼의 관리자는 위치정보를 기반으로 사용자가 게시한 도시문제 사례를 지도로 가시화하여 볼 수 있었으며, 이를 히트 맵 형식으로 가시화함으로써 도시문제가 밀집된 위치들을 파악할 수 있었다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 추계학술대회
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• pp.156-159
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• 2022

최근 데이터 기반의 스마트시티를 이룩하고자 하는 다양한 시도가 발생하고 있다. 데이터 기반의 스마트시티를 위해서는 국내 108개 지자체에 보급된 스마트시티 통합플랫폼 활용을 통한 시너지를 발휘하는 것이 필수적이다. 본 논문에서는 도시 플랫폼 간 상호운용성을 향상하기 위해 도시 데이터 플랫폼으로 연구 중인 스마트시티 데이터허브와 스마트시티 통합플랫폼 간의 연동 구현 사례로써, 스마트시티 통합플랫폼의 도시 상황 이벤트를 스마트시티 데이터허브로 연계하고, 스마트시티 데이터허브에서의 데이터 융·복합 분석 결과로써 도시 예측 이벤트를 스마트시티 통합플랫폼으로 연계한 구현 사례를 기술한다.

• 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.