• Proceedings of the KSR Conference
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• 2005.11a
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• pp.152-157
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• 2005

Recently, higher speeds have been promoted in old overhead-lines. For high-speed operation in electric railways, increasing contact loss of the pantograph is one of the most important subjects. The contact loss causes power interruption and increases wear of contact strips and contact wires. In order to investigate the causes of contact loss and to work out effective improvement methods, theoretical analyses, experiments have been carried out. First of all, the wave propagating velocity (phase velocity) and characteristics of contact wire are one of the important indices for the current collecting performance. In this paper, mathematical formula arc derived for the prediction of the traveling wave velocity. The measured values in the experiment agree well with the theoretical predictions.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.323-323
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• 2000

In the tandem cold wiling mill, the quality is very in portant, and requirements for thickness accuracy become more strict. However, the mathematical model for prediction of rolling force was not considered an elastic deformation at the entry and delivery side of the contacted area between the worked roll and rolling strip so that there was so difficult to control of the thickness. To overcome this problem, the mathematical model included an elastic deformation of strip has been developed and applied to the field in order to predict the rolling force. The simulated results showed that the end of elastic recovery should be included the model, even if the effect of elastic compression was not important.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.125-130
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• 1994

Recently,manufacturing work been transformed to small acale production from with various items to act up to user's expectation from mass production with a little items required in the past. The FMS using NC type mother machinaries has been applied actively also in domestic manufacturing line to meet thus tendancy, but there are many machining troubles occured in work process not be settled yet. Nowdays high efficiency has been required no less than high precision in grinding work for the improvement of productivity. In this study, to represent more advanced FMS can be adapted to thus situation In-process type monitoring method using AE and Current sensors is suggested to investigatethe machining condition in grinding process. As results from this experimental study, is is recoqnized well that grinding conditions and dressing point of in time can be estimated effectively using monitoring method suggested. Furthermore, surface shape of grinding wheel on voluntary point of in time can be predicted indirectly through the observation and comparison of AE signal waveform obtained as performance of continuous dressing work.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.607-610
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• 2002

Even though digital video coding techniques have been developed very rapidly, rate controlling is still an unsolved problem. This paper presents a new scene adaptive video encoding scheme for MPEG-1 and MPEG-2 video encoders. Degradation of picture quality at scene changes due to poor prediction of global complexity measure is addressed. The proposed scheme determines the picture types adaptively based on scene contents of each video frame. The effective placement of reference and inter-reference frames is studied at sudden scene changes and gradual scene changes respectively. Results show that the proposed scheme demonstrates a significant improvement in performance compared to TM5〔1〕.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.249-261
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• 2010

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.3
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• pp.79-84
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• 2007

Recently it is strongly required to develop the better fuel economy as well as basic power performance based on strict emission legislation. This paper focuses on studies of the engine cooling and ancillaries system among fuel economy factors in the developing stage. Firstly through the analysis of the current specifications, it is assessed whether each components may be designed properly, not overdesigned. Secondly, it is predicted how the fuel economy of each components can be improved. Finally the results are confirmed by vehicle field test equppted with the updatedcomponents. This study found good agreementbetween the prediction and the field test on the vehicle fuel economy improvements of the heavy duty engine vehicle with updated components such as engine cooling and ancilliaries.

• Wind and Structures
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• v.14 no.6
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• pp.517-536
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• 2011

The assessment of wind-induced motion plays an important role in the development and design of the majority of today's structures that push the limits of engineering knowledge. A vital part of the design is the prediction of wind-induced tall building motion and the assessment of its effects on occupant comfort. Little of the research that has led to the development of the various international standards for occupant comfort criteria have considered the effects of the low-frequency motion on task performance and interference with building occupants' daily activities. It has only recently become more widely recognized that it is no longer reasonable to assume that the level of motion that a tall building undergoes in a windstorm will fall below an occupants' level of perception and little is known about how this motion perception could also impact on task performance. Experimental research was conducted to evaluate the performance of individuals engaged in a manual tracking task while subjected to low level vibration in the frequency range of 0.125 Hz-0.50 Hz. The investigations were carried out under narrow-band random vibration with accelerations ranging from 2 milli-g to 30 milli-g (where 1 milli-g = 0.0098 $m/s^2$) and included a control condition. The frequencies and accelerations simulated are representative of the level of motion expected to occur in a tall building (heights in the range of 100 m -350 m) once every few months to once every few years. Performance of the test subjects with and without vibration was determined for 15 separate test conditions and evaluated in terms of time taken to complete a task and accuracy per trial. Overall, the performance under the vibration conditions did not vary significantly from that of the control condition, nor was there a statistically significant degradation or improvement trend in performance ability as a function of increasing frequency or acceleration.

• ETRI Journal
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• v.34 no.3
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• pp.410-420
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• 2012

A series of reversible watermarking technologies have been proposed to increase embedding capacity and the quality of the watermarked image simultaneously. The major skills include difference expansion, histogram shifting, and optimizing embedding order. In this paper, an accurate predictor is proposed to enhance the difference expansion. An efficient sorter is also suggested to find a more desirable embedding order. The payload is differently distributed into two sub-images, split like a chessboard pattern, for better watermarked image quality. Simulation results of the accurate prediction and sorter based on the payload balancing method yield generally better performance over previous methods. The gap is wide, in particular, in low payload for natural images. The peak signal-to-noise ratio improvement is around 2 dB in low payload ranges.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.4
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• pp.369-376
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• 2005

In recent years, AGVs(Automated Guided Vehicles) have received growing attention as a subsystem of the integrated container operating system which enables unmanned control. improvement of job reliability, accuracy and productivity. Therefore, a number of works have been done to enhance the performance AGVs. In this paper. job deployment and a dynamic routing control system composed of supervisor, traffic controller. motion controller and routing table are discussed. A simple job deployment scheme and an efficient dynamic routing algorithm incorporating with the deadlock prediction and avoidance algorithm are investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3855-3867
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• 2022

Name separation (splitting full names into surnames and given names) is not a tedious task in a multiethnic country because the procedure for splitting surnames and given names is ethnicity-specific. Malaysia has multiple main ethnic groups; therefore, separating Malaysian full names into surnames and given names proves a challenge. In this study, we develop a two-phase framework for Malaysian name separation using deep learning. In the initial phase, we predict the ethnicity of full names. We propose a recurrent neural network with long short-term memory network-based model with character embeddings for prediction. Based on the predicted ethnicity, we use a rule-based algorithm for splitting full names into surnames and given names in the second phase. We evaluate the performance of the proposed model against various machine learning models and demonstrate that it outperforms them by an average of 9%. Moreover, transfer learning and fine-tuning of the proposed model with an additional dataset results in an improvement of up to 7% on average.