• Smart Structures and Systems
• /
• v.24 no.5
• /
• pp.587-595
• /
• 2019

Deformation measurement of large scale structures, such as the ground beds of high-rise buildings, tunnels, bridge, and railways, are important for insuring service quality and safety. The pose-relay videometrics method and displacement-relay videometrics method have already presented to measure the pose of non-intervisible objects and vertical subsidence of unstable areas, respectively. Both methods combine the cameras and cooperative markers to form the camera series networks. Based on these two networks, we propose two novel videometrics methods with closed-loop camera series network for deformation measurement of large scale structures. The closed-loop camera series network offers "closed-loop constraints" for the camera series network: the deformation of the reference points observed by different measurement stations is identical. The closed-loop constraints improve the measurement accuracy using camera series network. Furthermore, multiple closed-loops and the flexible combination of camera series network are introduced to facilitate more complex deformation measurement tasks. Simulated results show that the closed-loop constraints can enhance the measurement accuracy of camera series network effectively.

• KEPCO Journal on Electric Power and Energy
• /
• v.5 no.3
• /
• pp.229-233
• /
• 2019

Large-scale High Temperature Superconducting (HTS) wind power generators suffer from high electromagnetic force and high torque due to their high current density and low rotational speed. Therefore, the torque and Lorentz force of HTS wind power generators should be carefully investigated. In this paper, we proposed a Performance Evaluation System (PES) to physically test the structural stability of HTS coils with high torque before fabricating the generator. The PES is composed of the part of a pole-pair of the HTS generator for estimating the characteristic of the HTS coil. The 10 MW HTS generator and PES were analyzed using a 3D finite element method software. The performance of the HTS coil was evaluated by comparing the magnetic field distributions, the output power, and torque values of the 10 MW HTS generator and the PES. The magnetic flux densities, output power, and torque values of the HTS coils in the PES were the same as a pole-pair of the 10 MW HTS generator. Therefore, the PES-based evaluation method proposed in this paper can be used to estimate the critical characteristics of the HTS generator under high magnetic field and high torque before manufacturing the HTS wind turbines. These results will be used effectively to research and manufacture large-scale HTS wind turbine generators.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.537-537
• /
• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• Genomics & Informatics
• /
• v.19 no.3
• /
• pp.24.1-24.7
• /
• 2021

Tracking the most recent advances in Coronavirus disease 2019 (COVID-19)-related research is essential, given the disease's novelty and its impact on society. However, with the publication pace speeding up, researchers and clinicians require automatic approaches to keep up with the incoming information regarding this disease. A solution to this problem requires the development of text mining pipelines; the efficiency of which strongly depends on the availability of curated corpora. However, there is a lack of COVID-19-related corpora, even more, if considering other languages besides English. This project's main contribution was the annotation of a multilingual parallel corpus and the generation of a recommendation dataset (EN-PT and EN-ES) regarding relevant entities, their relations, and recommendation, providing this resource to the community to improve the text mining research on COVID-19-related literature. This work was developed during the 7th Biomedical Linked Annotation Hackathon (BLAH7).

• Journal of the Korean Geotechnical Society
• /
• v.24 no.2
• /
• pp.5-14
• /
• 2008

Shear characteristics of quarry blasted rocks were compared using large scale direct shear tests and triaxial tests. For comparison purpose, similar test conditions were simulated as much as possible and three types of relative density (50%, 70%, 90%) were employed for the test. Results indicate that stress-strain behavior shows the same trend for two tests, but the measured shear strengths differ for the different test ms and depends on the relative density. At low relative density, the internal friction angles from direct shear test are smaller than those from triaxial tests. However, at high relative density, this phenomenon is reversed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.3
• /
• pp.551-569
• /
• 2024

With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.

• New & Renewable Energy
• /
• v.20 no.2
• /
• pp.65-70
• /
• 2024

The major challenge in promoting large-scale offshore wind power projects is securing local acceptance. Several recent studies have emphasized the crucial role of local governments in addressing this problem. However, local governments have difficulty in achieving clear results because of the lack of expertise and manpower in offshore wind power projects, making thempassive in promoting these initiatives. In this context, we briefly introduce the case of Shinan-gun, which recently successfully conducted a pre-feasibility study on a large-scale offshore wind power complex led by the local government.

• Geomechanics and Engineering
• /
• v.37 no.4
• /
• pp.359-370
• /
• 2024

The primary goal of this study is to evaluate the migration of fine granular materials into overlying layers under cyclic loading using a modified large-scale triaxial system as a physical model test. Samples prepared for the modified large-scale triaxial system comprised a 60 mm thick gravel layer overlying a 120 mm thick subgrade layer, which could be either tailings or railway sand. A quantitative analysis of the migration of fine granular materials was based on the mass percentage and grain size of migrated materials collected in the gravel. In addition, the cyclic characteristics, i.e., accumulated axial strain and excess pore water pressure, were evaluated. As a result, the total migration rate of the railway sand sample was found to be small. However, the total migration rate of the sample containing tailings in the subgrade layer was much higher than that of the railway sand sample. In addition, the migration analysis revealed that finer tailings particles tended to be migrated into the upper gravel layer easier than coarser tailings particles under cyclic loading. This could be involved in significant increases in excess pore water pressure at the last cycles of the physical model test.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.8
• /
• pp.818-822
• /
• 2009

In this paper, we consider a decentralized observer design problem for fault detection in large-scaled linear time-invariant systems. Since the fault detection residual is desired to be sensitive on the fault, we use the H_ index performance criterion. Sufficient conditions for the existence of such an observer is presented in terms of linear matrix inequalities. Simulation results show the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.10
• /
• pp.1098-1107
• /
• 1990

Decentralized iterative learning control methods are presented for a class of large scale interconnected linear dynamic systems, in which iterative learning controller in each subsystem operates on its local subsystem exclusively with no exchange of information between subsystems. Suffcient conditions for convergence of the algorithms are given and numerical examples are illustrated to show the validity of the algorithms. In particular, the algorithms are useful for the systems having large uncertainty of inter-connected terms.