• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.68-71
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• 1988

The purpose of this paper is to compare with the simulated results of two control algorithms control algorithm in the real time, based upon the model. These control schemes are "Computed-torque" and "Feedforward-Dynamics compensation", and have been simulated on the CMU DD Arm II.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.53.3-53
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• 2002

1. Introdition : The necessity of DGPS data compensation. 2. Configuration of the GPS and coordinates conversion 2-1. Coordinates conversion of CCD 3. Vehicle Model and Evaluation 4. Accurate error position algorithm. 5. Experiment and result. 6. Conclusion: It was possible that we converted the CCD data into the GPS coordinates data.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.293-297
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• 1998

A method for the evaluation and the compensation of the vertical milling machine is presented. The method used a mathmatical model of thermal deformation based on temperatur variations of the machine and the environment. It follows an empirical approach and requires low cost equipment to be applied. According to this study, machine error caused by thermal deformation will be reduced to about 1/6.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.100-105
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• 2013

In this study, we analyze the balance of quadruped walking robots. For this purpose, a simplified polygonal model of a quadruped walking configuration is considered. A boundary-range-based balance margin is used for determining the system stability of the polygonal walking configuration considered herein. The balance margin enables the estimation of the walking configuration's balance for effective walking. The usefulness of the balance margin is demonstrated through exemplary simulations. Furthermore, balance compensation by means of foot stepping is addressed.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.218-221
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• 2001

This paper presents a new control scheme for a Dynamic Voltage Restorer(DVR) system consisting of series voltage source PWM converters. The control system is designed using differential controllers and digital filters to transfer the faulted ac source voltage to a d-q model and to separate the positive and negative sequence component for individual compensation. The performance of the presented controller and scheme are confirmed through simulation and actual experiment.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.865-870
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• 1998

We propose a multifunctional power line compensator (PLC) which can individually compensate multiple impediments at the same time. The PLC has the flexibility to share power to each compensation according to commands, this improving the working rate. We constructed a 100kVA PLC model including a controller with digital signal processor (DSP) to realize a multifunctional compensation were obtained.

• Smart Structures and Systems
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• v.24 no.5
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• pp.649-657
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• 2019

Recently, an advanced video deflectometer based on the principle of off-axis digital image correlation was presented and advocated for remote and real-time deflection monitoring of large engineering structures. In engineering practice, measurement accuracy is one of the most important technical indicators of the video deflectometer. However, it has been observed in many outdoor experiments that data drift often presents in the measured deflection-time curves, which is caused by the instability of imaging system and the unavoidable influences of ambient interferences (e.g., ambient light changes, ambient temperature variations as well as ambient vibrations) in non-laboratory conditions. The non-ideal unstable imaging conditions seriously deteriorate the measurement accuracy of the video deflectometer. In this work, to perform high-accuracy deflection monitoring, potential sources for the drift error are analyzed, and a drift error model is established by considering these error sources. Based on this model, a simple, easy-to-implement yet effective reference point compensation method is proposed for real-time removal of the drift error in measured deflections. The practicality and effectiveness of the proposed method are demonstrated by in-situ deflection monitoring of railway and highway bridges.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.117-124
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• 2002

This research was conducted to develop a method to remove the effect of surface temperature of Shingo pears for sugar content measurement. Sugar content was measured by a near-infrared spectrum analysis technique. Reflected spectrum and sugar content of a pear were used for developing regression models. For the model development, reflected spectrums having wavelengths in the range of 654 to 1,052nm were used. To remove the effect of surface temperature, special sample preparation techniques and partial least square (PLS) regression models were proposed and tested. 71 Shingo pears stored in a cold storage, which had 2$^{\circ}C$ inside temperature, were taken out and left in a room temperature for a while. Temperature and reflected spectrum of each pear was measured. To increase the temperature distribution of samples, temperature and reflected spectrum of each pear was measured four times with one hour twenty minutes interval. During the experiment, temperature of pears increased up to 17 $^{\circ}C$. The total number of measured spectrum was 284. Three groups of spectrum data were formed according to temperature distribution. First group had surface temperature of 14$^{\circ}C$ and total number of 51. Second group consisted of the first and the fourth experiment data which contained the minimum and the maximum temperatures. Third group consisted of 155 data with normal temperature-distribution. The rest data set were used for model evaluation. Results shelved that PLS model I, which was developed by using the first data group, was inadequate for measuring sugar content of pears which had different surface temperatures from 14$^{\circ}C$. After temperature compensation, sugar content predictions became close to the measured values. Since using many data which had wide range of surface temperatures, PLS model II and III were able to predict sugar content of pears without additional temperature compensation. PLS model IV, which included the surface temperatures as an independent variable. showed slightly improved performance(R$^2$=0.73). Performance of the model could be enhanced by using samples with more wide range of temperatures and sugar contents.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.754-761
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• 2013

Thermal displacement is an important issue in machine tool systems. During the last several decades, thermal error compensation technology has significantly reduced thermal distortion error; this success has been attributed to the development of a precise, robust thermal error model. A major advantage of using the thermal error model is instant compensation for the control variables during the modeling process. However, successful application of thermal error modeling requires correct determination of the temperature sensor placement. In this paper, a procedure for predicting thermal-mode-based thermal error is introduced. Based on this thermal analysis, temperature sensors were positioned for multiple heat-source models. The performance of the sensors based on thermal-mode error analysis, was compared with conventional methods through simulation and experiments, for the case of a slide table in a transient state. Our results show that for predicting thermal error the proposed thermal model is more accurate than the conventional model.

• Journal of Korea Multimedia Society
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• v.16 no.8
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• pp.905-913
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• 2013

The Parallel Model Compensation Using Frequency-variant: FV-PMC for noise-robust speech recognition is a method to classify the noises, which are expected to be intermixed with input speech when recognized, into several groups of noises by setting average frequency variant as a threshold value; and to recognize the noises depending on the classified groups. This demonstrates the excellent performance considering noisy speech categorized as good using the standard threshold value. However, it also holds a problem to decrease the average speech recognition rate with regard to unclassified noisy speech, for it conducts the process of speech recognition, combined with noiseless model as in the existing PMC. To solve this problem, this paper suggests a enhanced method of recognition to prevent the unclassified through improving the extent of rating scales with use of maximum likelihood so that the noise groups, including input noisy speech, can be classified into more specific groups, which leads to improvement of the recognition rate. The findings from recognition experiments using Aurora 2.0 database showed the improved results compared with those from the method of the previous FV-PMC.