• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.4
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• pp.223-230
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• 2014

In this paper, the wind-induced response characteristics of freeform shaped tall building is studied by using FSI analysis. The analytical models are twist shaped ones at representing type of atypical tall building, and this study focused on the relationship between twist angle and wind acceleration. Firstly, 1-way FSI analysis is performed, so maximum lateral displacement of the analytical model for 100 years return period wind speed is calculated, then the elastic modulus of a structure that satisfies the constraints condition is evaluated. And 2-way FSI analysis is carried out. so acceleration of the analytical model for the evaluated modulus of elasticity and arbitrary density is predicted through time history analysis. The basic model is a set of a square shape, height is 400m, slenderness ratio is 8, and twist model is rotated at square model from 0 to 90 degrees at intervals of 15 degrees and from 90 to 360 degrees at intervals of 90 degrees. According to the result of predicting wind acceleration by the shape of each model, the wind vibration effect of square shape model is confirmed to be sensitive more than a twist shape ones.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.06a
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• pp.107-108
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• 2014

As the demand for precise navigation has increased, more focus is put on the precise positioning, RTK(Real Time Kinematics) which has been used in the surveying field. The Position of Single Reference Station RTK or two-way network RTK such as VRS (Virtual Reference Station) is accurate enough to be used as a main technology in land surveying, however its service area and number of users is limited and the users are assumed static. This characteristic is not suitable to the navigation, whose service target is infinite number of users moving over a wide area. One-way network RTK has recently been suggested as a solution for the precise navigation technique for the mobile user. This paper shows the performance prediction of the one-way network RTK such as MAC(Master-Auxiliary Concept), or FKP (Flachenkorrekturparameter). To show the performance variation by the rover position, we constructed a simulation data of users on the grid with 0.1 degree spacing between 36.5 and 37 degree latitude and between 127 and 127.5 degree longitude.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.419-419
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• 2021

수자원 관리 패러다임은 공급 위주에서 수요관리로 전환되고 있다. 가용한 수자원은 한정적이나 급속한 인구증가와 도시화로 인한 물 수요의 증가로 수요관리의 효율성이 중시되고 있기 때문이다. 기존 상수도시스템은 노후화로 가동효율이 점차 낮아지고 있으며, 인력으로 월 또는 격월로 소비자의 물 사용량을 검침해 실시간 관리가 불가능하여 수요와 공급의 불균형을 초래한다. 이러한 문제를 해결할 대안으로 IT 기술과 전통적인 물관리 기술을 접목한 Smart Water Grid는 양방향 통신장치를 이용해 실시간으로 소비자의 물 사용량을 모니터링한다. 물 사용 특성을 잘 파악하면 보다 정확한 물 수요 예측이 가능하다. 특히 소비자들의 시간별, 평일, 주말, 그리고 주별 물 사용 특성을 파악하면 미래 물 수요 예측에 도움이 된다. 예측된 물 수요량에 따라 물 공급 배분 계획을 수립하여 운영 효율성을 높일 수 있다. 물 수요예측 방법 중 k-mean 군집분석은 시간별 물 사용량을 이용해 서로 유사한 여러 개의 부분집합으로 할당하여 분류하는 Machine learing 방법으로 물 사용의 유사성을 파악할 수 있다. SWG 연구단은 2019년 Vietnam Hai Duong province에 SWG Pilot plant를 구축하고 27개의 Smart water meter를 설치하여 운영하고 있다. 이에 본 연구에서는 소비자의 물 사용 특성을 분석하기 위해 27개 SWM로부터 수신된 2019년 11월 14일부터 2020년 12월 3일까지 1시간 단위의 물 사용량 데이터를 수집하였다. 그리고 k-mean 군집 방법을 이용해 시간별, 평일, 주말, 그리고 주별 물 사용 특성을 분석하였다. 이 때 최적의 군집 개수 결정을 위해 Elbow 방법을 적용하였다. 분석 결과 각 소비자의 물 사용량 특성에 따라 평균 물 수요패턴 추정이 가능하며, 향후 물 수요 예측에 도움이 될 것으로 사료된다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.547_548
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• 2009

전력거래소에서 전력계통 및 전력시장 운영을 위해 도입한 핵심시스템은 EMS, MOS, CBP 이다. 이 세개 시스템은 각각 고유의 기능을 가진 별도의 시스템으로 구축되었다. 우리나라의 전력시장이 발전경쟁단계에 머무르면서 당초 양방향전력시장용으로 도입된 MOS시스템이 활용되지 못함에 따라 MOS시스템의 실시간 급전기능을 활용해 전력계통 운영의 안정성 및 경제성 향상을 꾀하고자 이 세 개의 시스템을 2006년 10월부터 연계 운영하고 있다. 이를 위해 CBP입찰값을 MOS의 입찰형식으로 변환하기위한 CBP-MOS 입찰변환시스템, 실시간 수요예측을 위한 수요예측 프로그램, MOS와 EMS를 일괄적으로 연계하기 위한 연계Mode, 각 발전기 운전원들에게 발전기의 급전계획값 및 실시간 운전현황을 전송하기 위한 급전지시시스템(MX) 및 전반적인 시스템 연계운영을 종합적으로 감시하기 위한 종합감시시스템 등을 개발하여 운영하고 있다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.3
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• pp.475-488
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• 2017

Wear prediction of TBM disc cutters is a very important issue for hard rock TBMs as number of cutter head intervention. In this regard, some model such as NTNU, Gehring model, CSM models have been used to predict disc cutter wear and intervention interval. There are some deficiencies in these models. This paper developed a new test method for wear prediction for TBM disc cutter and proposed a new abrasion index. In this regard, different abrasivity indices along with their testing methods are explained. A comparative study is performed to develop the predictability of different cutter life evaluation methods and index. The evaluation of the new methods proposed in this paper shows a very good agreement with the actual cutter life and intervention interval length. The proposed tester and index can be easily used to predict the intervention interval length and cutter wear evaluation in both planning and construction stages of a TBM tunneling project.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.103-111
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• 2011

The speed is one of the significant factors affecting accident occurrence. In particular, freeway accidents are highly associated with the speed because vehicles travel on the freeway at higher speed leading to greater potential of severer injury. Efforts attempting to relating speed with accident occurrence have not been significantly made in Korea. The objective of this study is to model the relationship between speed and accident rate on freeways. Loop detector data and accident data obtained from a stretch of Kyungboo freeway during the recent five years, 2005-2009, were used to establish the model. Multiple linear regression analyses showed that median, minimum and standard deviation of speed were contributing variables in the model. The statistical significance identified by the analyses supports the feasibility of the model in evaluating various transportation policies and operations strategies in terms of traffic safety.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.553-563
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• 2019

As the number of videos uploaded on live streaming platforms rapidly increases, the demand for providing highlight videos is increasing to promote viewer experiences. In this paper, we present novel methods for predicting highlights using chat logs and audio data in videos. The proposed models employ bi-directional LSTMs to understand the contextual flow of a video. We also propose to use the features over various time-intervals to understand the mid-to-long term flows. The proposed Our methods are demonstrated on e-Sports and baseball videos collected from personal broadcasting platforms such as Twitch and Kakao TV. The results show that the information from multiple time-intervals is useful in predicting video highlights.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.356-367
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• 2021

With the recent development of IoT and artificial intelligence technology, research and applications for optimization of real-world problems by collecting and analyzing data in real-time have increased in various fields such as manufacturing and smart city. Representatively, the digital twin platform that supports real-time synchronization in both directions with the virtual world digitized from the real world has been drawing attention. In this paper, we define a digital twin concept and propose a digital twin platform prototype that links real objects and predicted results from the virtual world in real-time by utilizing the oneM2M-based IoT platform. In addition, we implement an application that can predict accidents from object collisions in advance with the prototype. By performing predefined test cases, we present that the proposed digital twin platform could predict the crane's motion in advance, detect the collision risk, perform optimal controls, and that it can be applied in the real environment.