• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.39.2-39.2
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• 2011

In this study we have examined the occurrence probability of solar proton events (SEPs) and their peak fluxes depending two CME parameters, linear speed and angular width. For this we used the NOAA SPE events and their associated CME data from 1997 to 2006. As a result, the probability strongly depends on two parameters as follows. In the case of halo CME whose speed is equal to and faster than 1500km/s, 36.1% are associated with SPEs but in the case of partial halo CME ($120^{\circ}{\leq}AW$ < $359^{\circ}$) whose speed is $400{\leq}V$ < $1000km/s$, only 0.9% are associated with SPEs. When we consider only front-side CMEs, 45.3% are associated with SPEs in the first case and 1.8% are associated with them in the second case. Both of whole CME data group and front-side CME data group have similar tendencies. The probabilities are different as much as 4.9 to 23 times according to the CME speed and 1.6 to 6.5 times to the angular width. We have also examined the relationship between CME speed and proton peak flux as well as its dependence on angular width (partial halo CME and halo CME), longitude (east, center, and west) and direction parameter (< 0.4 and {\geq} 0.4). Our results show that the relationships strongly depend on longitude as well as direction parameter. In addition, the relationship using the radial CME speed based on a cone model has a higher correlation coefficient than that using the projected CME speed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.3
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• pp.208-215
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• 2011

A full-scale field trial in ice-covered sea is one of the most important tasks in the design of icebreaking ships. The first Korean icebreaking research vessel 'ARAON', after her delivery in late 2009, had a sea ice field trial in the Arctic Sea during July-August, 2010. This paper describes the test procedures and data analysis on the icebreaking performance of the IBRV ARAON. The data gathered from the icebreaking performance test in the Chukchi Sea and the Beaufort Sea during the Arctic voyage of ARAON includes the speed and engine power of the ship as well as sea ice thickness and strength data. The air temperature, wind speed and heading of the ship were also measured during each sea ice trial. The ARAON was designed to break 1 m thick level ice with a flexural strength of 630kPa at a continuous speed of 3knots. She is registered as a KR POLAR 10 class ship. The principal dimensions of ARAON are 110 m, 19 m and 6.8 m in length, breadth and draft respectively. She is equipped with four 3,500kW diesel-electric main engines and two Azipod type propulsion motors. Four sea ice trials were carried out to understand the relationship between the engine power and the ship speed, given the Arctic ice condition. The analysis shows that the ARAON was able to operate at 1.5knots in a 2.5m thick medium ice floe condition with the engine power of 5MW, and the speed reached 3.1 knots at the same ice floe condition when the power increased to 6.6MW. She showed a good performance of speed in medium ice floe compared to the speed performance in level ice. More detailed analysis is summarized in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.657-662
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• 2004

This paper presented is a case study of a real compressor rotor of a refinery plant for high speed balancing of flexible rotor. The rotor was tested in the expert high-speed balancing facility established by KIMM at early 2004. The capability of the facility can reach 40000rpm in rotation speed and 8 ton in rotor weight for high-speed balancing. The facility performs multi-plane at-speed balancing using influence coefficient from the vibration data measured at two pedestals. The test rotor had exceeded permissible criteria of vibration at initial run. But by processing a low-speed balancing at 1000 rpm and six trial run trying to calculate influence coefficient of rotor to the range of operating speed, the final result of high-speed balancing revealed a remarkable reduce of vibration at pedestal of the rotor.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.2
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• pp.185-192
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• 1996

This paper presents a design of vertical axis Darrieus wind turbine combine with Savonius for wind-power generating system to be adapted for variable wind speed. The wind turbine consists of two troposkien- and four Savonius-blades. Darrieus turbine is designed with diameter 9.4[m], chord length 380[mm], tip speed ratio 5. Savonius turbine is designed with diameter 1.8[m], height 2[m], tip speed ratio 0.95. The design of turbine is laid for the main data of rated wind speed 10[m/s], turbine speed 101.4[rpm]. The generating power is estimated to maximum power 20[kW], and this is converted to commercial power line by means of three phase synchronous generator-inverter system. Generating system is designed for operation on VSVF(variable speed variable frequency) condition and constant voltage system.

• Publications of The Korean Astronomical Society
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• v.23 no.2
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• pp.91-107
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• 2008

In this paper, we introduce the performance evaluation and development of Raw VLBI Data Buffer(RVDB) system for the synchronized playback processing of observed data in Korea-Japan Joint VLBI Correlator(KJJVC). The high-speed correlation processing system is under development so that the radio data obtained with 8192 channels and 8 Gbps speed from 16 stations will be able to be processed. When the recorded data of each station are played to correlator, the time synchronization of each station is very important because the correlator should process the data obtained with same time and condition. There are many types of recorder systems in the East Asia VLBI Network (EAVN). Therefore it is required to prepare the special time synchronized playback processing system to synchronize the time tag of observed data. The developed RVDB system consists of Data Input Output(DIO), 10GbE switch, and Disk Data Buffer(DDB). It can record the data with maximum 2 Gbps speed, and can play back the data to correlator with nominal 2 Gbps speed. To enable to play back the data of different playback system to the correlator, we developed the high-speed time synchronized playback processing system. We carried out the experiments of playing back and correlation for gigabit correlator and VCS trial product so as to confirm the performance of developed time synchronized playback processing system. In case of online and offline playing back experiment for gigabit correlator, we confirmed that the online and offline correlation results were the same. In case of playing back experiment for VCS trial product, we verified that the wide band and narrow band correlation results were also the same. Through the playing back experiments of RVDB system, the effectiveness of developed RVDB system was verified. In this paper, the system design, construction and experimental results are shown briefly.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.539-546
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• 2002

This paper presents a new direct seek control scheme that provides fast data access capability and robust performance for high-speed rotational optical disk drives (ODD). When a disk is rotating at a high speed to obtain fast data transfer in ODD, the magnitude and frequency of velocity disturbance caused by eccentric rotation of the disk increase in proportion to the rotational speed of the disk. Such disturbances make it almost impossible for the conventional seek control scheme to achieve stable and satisfactory seek performance. We analyze the problems that may arise when the conventional seek control scheme is applied to the high-speed rotational ODD and propose a new direct seek control scheme that will solve such problems. In the proposed scheme, a seek control system is designed such that its performance is guaranteed for a set of plants with parameter perturbations. The performance of the proposed seek control scheme is shown by experiments using a high-speed rotational ODD.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.5
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• pp.413-421
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• 2012

In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.851-856
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• 2016

Wind speed is heavily fluctuated and quite local than other weather elements. It is difficult to improve the accuracy of prediction only in a numerical prediction model. An MOS (Model Output Statistics) technique is used to correct the systematic errors of the model using a statistical data analysis. The Most of previous MOS has used a linear regression model for weather prediction, but it is hard to manage an irregular nature of prediction of wind speed. In order to solve the problem, a nonlinear regression method using SVR (Support Vector Regression) is introduced for a development of MOS for wind speed prediction. Experiments are performed for KLAPS (Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea. The MLR and SVR based linear and nonlinear methods are compared to each other for prediction accuracy of wind speed. Also, the comparison experiments are executed for the variation in the number of UM elements.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.