• Water for future
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• v.28 no.2
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• pp.169-180
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• 1995

The cost of a looped pipe network is affected by a set of loop flows. The mathematical model for optimizing the looped pipe network is expressed in the optimal set of loop flows to apply to a stochastic optimization method. Because the feasible region of the looped pipe network problem is nonconvex with multiple local optima, the Modified Stochastic Probing Method is suggested to efficiently search the feasible region. The method consists of two phase: i) a global search phase(the stochastic probing method) and ii) a local search phase(the nearest neighbor method). While the global search sequentially improves a local minimum, the local search escapes out of a local minimum trapped in the global search phase and also refines a final solution. In order to test the method, a standard test problem from the literature is considered for the optimal design of the paralled expansion of an existing network. The optimal solutions thus found have significantly smaller costs than the ones reported previously by other researchers.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.6-6
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• 2019

상수관망시스템은 공급원으로부터 수요처까지의 용수공급을 위해 구축된 관수로 기반의 사회기반시설물로서, 주로 생활 및 산업 용수를 공급하므로 대규모 사회 경제적 피해를 방지하기 위해서는 안정적인 용수공급 능력이 요구된다. 네트워크의 다양한 특성에 의해 표현되는 상수관망시스템의 신뢰도(reliability)는 크게 시스템 내 구성요소의 안정성(mechanical reliability)과 용수공급의 기능적 안정성(hydraulic reliability)으로 구분할 수 있다. 특히, 시스템의 용수공급 안정성에 주목한 수리학적 신뢰도 연구는 많은 연구자들에 의해 지속적으로 수행된 바 있으며, 다양한 평가방법 및 지표들이 제시되어 활용 중에 있다. 기존의 수리학적 신뢰도 지표들은 주로 수요절점(demand node)에서의 공급가능 수량 및 수압을 바탕으로 산정되었다. 그러나, 절점(node)에서의 공급 상태는 결과에 해당하며, 원인 분석을 위해서는 관로(pipe)의 배치 및 규격을 분석해야 하는 번거로움이 존재한다. 이러한 단점을 보완하기 위해, 본 연구에서는 직접 관로(pipe)의 공급 특성을 분석하여 네트워크의 신뢰도를 평가함으로써, 신뢰도 저하의 원인 분석 및 시스템 개선에 효율적으로 활용할 수 있는 신뢰도 지표를 산정하고자 하였다. 본 연구에서는 상수관로 내 수리학적 기울기가 전반적으로 균등할수록 설계 비용대비 공급 신뢰도, 즉 용수공급 효율이 개선되는 특징을 바탕으로, 네트워크 내 총 에너지 손실로부터 각 관로의 길이, 유량 등의 특성을 고려한 등가 수리경사(Equivalent hydraulic gradient)를 유도하여 모든 관로의 적정 수리경사로 제안하였다. 따라서 각 관로의 실제 수리경사를 대상으로 관로별 수리학적 균등성 지수(pipe hydraulic uniformity index)를 산정하였으며, 더 나아가 전체 시스템의 균등성 지수(system hydraulic uniformity index)를 산정하였다. 제안된 신뢰도 지표는 가상의 네트워크에서 지역 내 용수 사용량이 증가하는 등 용수공급 안정성을 저해하는 몇 가지 시나리오를 바탕으로 검증하였으며, 또한 기존 지표들의 신뢰도 평가 결과와 비교, 분석하였다. 본 연구는 향후 네트워크 최적 설계의 목적함수로 활용하거나, 네트워크의 보강계획 수립에 기여할 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.429-429
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• 2021

2019년 발생한 인천광역시 붉은 수돗물 사태로 급수구역에 포함된 26만 1천 세대, 63만 5천 명이 직·간접적인 피해를 입은 바 있다. 경제적 피해액으로 추정할 경우 최소 1,280억 원 이상으로 보고된 바 있으며, 이와 같은 상수관망의 수질사고 확산은 장기간 동안 시민의 건강과 생활환경 수준을 저하시킨다. 따라서 상수도시스템의 수질사고확산 모델링 및 방지기술을 통한 수질안전성의 재확인이 필요하며, 이것은 상수도시스템의 지속가능성을 높여 국민이 체감하는 물 환경 수준 제고에 기여가 가능하다. 관망 내 수질해석을 직접적으로 수행하는 모델은 국외적으로 다양하게 개발(PODDS, EPANET-MSX, EPANET2.2 등)된 바 있으나 검·보정을 위한 수질측정 자료 부족 등으로 적용이 제한적이라는 한계가 현재에도 존재한다. 이를 보완하기 위해 수질자료에 비해 그 양이 많고 획득방법이 상대적으로 수월한 수리학적 계측자료 및 해석결과를 활용한 관로 내 체류시간 등을 활용한 연구가 수행된 바 있다. 그러나 이와 같은 수리학적 해석 결과를 활용하는 경우에도 계측자료를 기반으로 한 수리학적 검·보정은 필수적이라 할 수 있다. 본 연구에서는 관로 내 체류시간에 직접적인 영향을 미치는 유량 및 유속자료를 중심으로 수리학적 관망해석의 결과를 최적 검·보정하는 방법론을 제안하였다. 기존 상수관망 수리해석의 검·보정은 일부 지점에서 수압을 측정하고, 수리해석 결과로 도출되는 해당 지점의 수압이 실측된 결과와 유사하도록 관로의 유속계수를 적절히 보정하는 형태로 진행되었다. 그러나 본 연구에서는 관로유량과 유속자료의 목적함수 내 가중치를 수압자료보다 크게 설정하여 체류시간 중심의 검·보정이 수행될 수 있도록 하였으며, 검·보정 대상인자 역시 대수용가의 수요량, 수요패턴, 그리고 관로유속계수로 확장된 모형을 구축하였다. 최적화 기법으로는 메타휴리스틱 기법중 하나인 화음탐색법을 활용하였다. EPANET 2.2 Toolkit과 Visual Basic .Net을 연계하여 프로그래밍하였으며, 개발된 모형을 실제 지방상수도 시스템에 적용하여 분석하였다.

• The Research Journal of the Costume Culture
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• v.25 no.5
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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.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.9-18
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• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.447-459
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• 2022

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.27-34
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• 2024

The direct and indirect damages caused by fires in underground utility tunnels have a great impact on society as a whole, so efforts are needed to prevent and manage them in advance. To this end, research is ongoing to prevent disasters such as fire flooding by applying digital twin technology to underground utility tunnels. A network is required to transmit the sensed signals from each sensor to the platform. In essence, it is necessary to analyze the application of wireless networks in the underground utility tunnel environments because the tunnel lacks the reception range of external wireless communication systems. Within the underground utility tunnels, electromagnetic interference caused by transmission and distribution cables, and diffuse reflection of signals from internal structures, obstacles, and metallic pipes such as water pipes can cause distortion or size reduction of wireless signals. To ensure real-time connectivity for remote surveillance and monitoring tasks through sensing, it is necessary to measure and analyze the wireless coverage in underground utility tunnels. Therefore, in order to build a wireless network environment in the underground utility tunnels. this study minimized the shaded area and measured the actual cavity environment so that there is no problem in connecting to the wireless environment inside the underground utility tunnels. We analyzed the data transmission rate, signal strength, and signal-to-noise ratio for each section of the terrain of the underground utility tunnels. The obtained results provide an appropriate wireless planning approach for installing wireless networks in underground utility tunnels.

• Korean Journal of Remote Sensing
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• v.13 no.1
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• pp.47-56
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• 1997

In this paper, remote sensing is used to estimate trophic state which is primary concern in a lake. In using remote sensing, this study estimated trophic state not with conventional method such as regression equations but with classification methods. As europhication is caused by the extraodinary proliferation of the algae, chlorophyll a and transparency are applied to remote sensing data.. Maximum Likelihood Classification and Minimum Distance Classification which are kinds of classification methods enabled trophic state to be confirmed in a lake. These are obtained as the result of applying remote sensing to classify trophic state in a lake. Firest, when we evaluate tropic state in a large area of water body, the application of remote sensing data can obtain more than 70% accuracies just in using basic classification methods. Second, in the aspect of classification, the accuracy of Minimum Distance Classification is usually better than that of Maximum Likelihood Classification. This result is caused that samples have normal distribution, but their numbers are a few to apply statistical method. Therefore, classification method is required such as artificial neural networks which are not influenced by statistical distribution. Third, this study enables the trophic state of water body to be analyzed and evaluated rapidly, periodically and visibly. Also, this study is good for forming proper countermeasure accompanying with trophic state progress extent in a lake and is useful for basic-data.