• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.494-501
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• 1999

In this research, ANN modeling method of SMES unit is developed for stability analysis, and the optimal site is selected to maximize stabilization effect of SMES unit. The ANN is trained by learning data which is obtained through the application of complex test function into the traditional mathematical mode. In order to verify the validity of proposed modeling method, fault data of sample power system is applied to both the traditional and the ANN models. When the response of traditional and proposed models are compared, the average error for the active and reactive power are 2.51[%], and 0.24[%], respectively. From the comparison, the relevance of proposed method is validated. For the transient stability analysis, an application method of the proposed model is presented, and the transient stability performance index, which describes system stabilization effect of SMES at disturbance, is also suggested, and optimal site selection method of SMES is presented. In the viewpoint of the voltage stability, system stabilization criterion of local bus is presented from P­V curve, and then optimal site which can maximize the voltage stabilization of the whole power system, is decided from the proposed voltage stability performance index.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.719-739
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• 1996

A common control model used to implement computer integrated manufacturing(CIM) is based on the hierarchical decomposition of the shop floor activities, in which supervisory controllers are responsible for all the interactions among subordinates. Although the hierarchical control philosophy provides for easy understanding of complex systems, an emerging manufacturing paradigm, agile manufacturing, requires a new control structure necessary to accommodate the rapid development of a shop floor controller. This is what is called autonomous agent-based heterarchical control. As computing resources and communication network on the shop floor become increasingly intelligent and powerful, the new control architecture is about to come true in a modern CIM system. In this paper, heterarchical control is adopted and investigated, in which a controller for a unit of device performs three main functions - planning, scheduling and execution. Attention is paid to the planning function and all the detailed planning activities for heterarchical shop floor control are identified. Interactions with other functions are also addressed. In general, planning determines tasks to be scheduled in the future. In other words, planning analyzes process plans and transforms process plans into detailed plans adequate for shop floor control. Planning is also responsible for updating a process plan and identifying/resolving replanning activities whether they come from scheduling or execution.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.40-49
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• 2019

Reinforcement learning has been applied to various problems in robotics. However, it was still hard to train complex robotic manipulation tasks since there is a few models which can be applicable to general tasks. Such general models require a lot of training episodes. In these reasons, deep neural networks which have shown to be good function approximators have not been actively used for robot manipulation task. Recently, some of these challenges are solved by a set of methods, such as Guided Policy Search, which guide or limit search directions while training of a deep neural network based policy model. These frameworks are already applied to a humanoid robot, PR2. However, in robotics, it is not trivial to adjust existing algorithms designed for one robot to another robot. In this paper, we present our implementation of Guided Policy Search to the robotic arms of the Baxter Research Robot. To meet the goals and needs of the project, we build on an existing implementation of Baxter Agent class for the Guided Policy Search algorithm code using the built-in Python interface. This work is expected to play an important role in popularizing robot manipulation reinforcement learning methods on cost-effective robot platforms.

• Progress in Superconductivity and Cryogenics
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• v.8 no.3
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• pp.54-58
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• 2006

In this paper, authors developed a real-time simulation algorithm for the power device application of HTS(High Temperature Superconducting) wire by using Real Time Digital Simulator(RTDS). At present, in order to extend the power capacity of some area where has a serious problem of power quality. especially metropolitan complex city, there are so many problems such as right of way for power line routes. space for downtown substations. and the environmental protection, etc. HTS technology can be useful to overcome this problem. Recently, according to the advanced HTS technology, the power application is being researched well. Simulation is required for safety before installation of HTS power cable, a fabrication model used at the power system simulation. This paper describes a real time digital simulation method for the application of HTS wire to power device. For the simulation analysis, test sample of HTS wire was actually manufactured. And the transient phenomenon of the HTS wire was analysed in the simulated utility power grid. This simulation method is the world first trial in order to obtain much better information for installation of HTS power device into a utility network.

• Smart Structures and Systems
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• v.29 no.5
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.142-142
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• 2021

An arbitrary representation of an urban drainage sewer system was devised using a geographic information system (GIS) tool in order to calculate the surface and subsurface flow interaction for simulating urban flood. The proposed methodology is a mean to supplement the unavailability of systematized drainage system using high-resolution digital elevation(DEM) data in under-developed countries. A modified DEM was also developed to represent the flood propagation through buildings and road system from digital surface models (DSM) and barely visible streams in digital terrain models (DTM). The manhole, sewer pipe and storm drain parameters are obtained through field validation and followed the guidelines from the Plumbing law of the Philippines. The flow discharge from surface to the devised sewer pipes through the storm drains are calculated. The resulting flood simulation using the modified DEM was validated using the observed flood inundation during a rainfall event. The proposed methodology for constructing a hypothetical drainage system allows parameter adjustments such as size, elevation, location, slope, etc. which permits the flood depth prediction for variable factors the Plumbing law. The research can therefore be employed to simulate urban flood forecasts that can be utilized from traffic advisories to early warning procedures during extreme rainfall events.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.637-640
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• 2023

This paper presents multimodal image fusion with human pose for detecting abnormal human behaviors in low illumination conditions. Detecting human behaviors in low illumination conditions is challenging due to its limited visibility of the objects of interest in the scene. Multimodal image fusion simultaneously combines visual information in the visible spectrum and thermal radiation information in the long-wave infrared spectrum. We propose an abnormal event detection scheme based on the multimodal fused image and the human poses using the keypoints to characterize the action of the human body. Our method assumes that human behaviors are well correlated to body keypoints such as shoulders, elbows, wrists, hips. In detail, we extracted the human keypoint coordinates from human targets in multimodal fused videos. The coordinate values are used as inputs to train a multilayer perceptron network to classify human behaviors as normal or abnormal. Our experiment demonstrates a significant result on multimodal imaging dataset. The proposed model can capture the complex distribution pattern for both normal and abnormal behaviors.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.