• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.37-43
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• 2004

High speed machining is a machining process which cuts materials with the fast movement and rotation of a spindle in a machine tool. It reduces machining time because of the high feed and the high speed of a spindle. In addition it gets rid of post processes for high precision machining. When the high speed machining is applied to especially hardened steel, operators should select the proper parameters of machining. This can produce machining surfaces which is qualified with good surface roughness. This paper presents a method for selecting machining parameters to minimize surface roughness with high speed machining in cutting the hardened steels. Experimental data for surface roughness are collected in a machining shop based on the cutting feed and the spindle rotation. The data fits in hi-cubic polynomial surface of mathematical form. From the model this research minimize the surface roughness to find the optimal values of the feed and the spindle speed. This paper presents a program which automatically generates optimal solutions from the raw data of experiments.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.373-374
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• 2008

This paper proposes an architecture for high-speed data processing of the DF-DPD. The DF-DPD have the architecture feedbacking the detected phase to reduce the noise of the previous symbol as phase reference. However, the feedback of the detected phase results in lower data processing speed than that of the conventional differential phase detection. In this paper, an architecture is proposed for high-speed data processing of the differential phase detectors with decision feedback in the DF-DPD.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.455-458
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• 2011

4:10 deserializer is proposed to recover 1:10 serial data using 1/4-rate clock. And then, 1/4-rate CDR(Clock and Data Recovery) circuit was designed for SERDES of high-speed serial display interface. The reduction of clock frequency using 1/4-rate clocking helps relax the speed limitation when higher data transfer is demanded. This circuit is composed of 1/4-rate sampler, PEL(Phase Error Logic), Majority Voting, Digital Filter, DPC(Digital to Phase Converter) and 4:10 deserializer. The designed CDR has been designed in a standard $0.18{\mu}m$ 1P6M CMOS technology and the recovered data jitter is 14ps in simulation.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.195-210
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• 2007

Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. We all know that the original intention of data cube is to speed up the query performance, so we propose two novel clustering methods for query optimization: the recursion clustering method which clusters the nodes in a recursive manner to speed up point queries and the hierarchical clustering method which clusters the nodes of the same dimension to speed up range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.43-51
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• 2009

This paper investigates the accuracy of vehicle pre-braking speed estimates based upon tire/roadway coefficient of friction (drag factor) measurements and skid mark measurements Data for pre-braking and pre-skidding speeds were collected to determine if there were any correlations between pre-braking speeds and pre-skidding speeds. Braking tests were performed on two vehicles using various measurement devices including a fifth wheel, a speed gun, and a vericom 2000. The vehicle speeds, braking distances, skid mark distances, and deceleration histories were recorded. From these data. coefficients of friction and vehicle pre-skidding speeds for the tested road surface were calculated. The pre-skidding speeds were then compared to the actual pre-braking speeds of the vehicles in order to establish relationships between pre-skidding and pre-braking speed. A correlation between the Pre-skidding speed and the actual pre-braking speed was established for the study data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.75-82
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• 2021

The number of traffic accidents is about 230,000, and due to non-recurring congestion and high driving speed, the number of deaths per traffic accident on freeways is more than twice compared to other roads. Currently, traffic information is provided based on nodes and links using the centerline of the road, but it does not provide detailed speed information. Recently, installing sensors for vehicles to monitor obstacles and measure location is becoming common not only for autonomous vehicles but also for ordinary vehicles as well. The analysis using large-capacity location-based data from such sensors enables real time service according to processing speed. This study presents an mapping method for individual vehicle data analysis based on space. The processing speed of large-capacity data was increased by using method which applied a quaternary notation basis partition method that splits into two directions of longitude and latitude respectively. As the space partition was processed, the average speed was similar, but the speed standard deviation gradually decreased, and decrease range became smaller after 9th partition.

• Proceedings of the KIEE Conference
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• 2003.07e
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• pp.62-69
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• 2003

In recent years, data transmission using power lines has been much highlighted. Power line is known to be cost-effective communication medium because the entire cable infrastructure has been already established and it is entirely connected with any home. Recently, the target of researches is to communicate data reliably over power lines at the speed of at least 1Mbits/s over the frequency range from 1MHz to 10MHz. OFDM communication system has been used for the high speed data transmission. Next, the conventional and adaptive OFDM systems for high speed data transmission over power line channel are investigated. The performance of AOFDM(adaptive OFDM) over the frequency selective channel with impulsive and narrow-band noise are studied to be a nice solution for high speed data transmission over power lines. The simulation results show that data the rates of the AOFDM are improved about 47% more than the ones of the conventional OFDM over the frequency response of case 4. In the results, the data rate has been much improved by the proposed adaptive algorithm in the frequency selective channel.