• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.81-90
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• 2001

Several seismic traces of earthquakes observed from the digital new type seismograph instruments of KMA in 2000 were scanned. From these, good quality data which have high signal/noise ratio were selected and they were transformed into ascii data from binary data(min-seed format). The hypo71 program and P-S was applied in order to determine the location of epicenter, origin time and the magnitude. From these data, the 29 earthquakes, 358 seismic records consist of 587 directional components were calculated. Using these, ground acceleration data, acceleration, velocity, and displacemnet response spectrums of the structures were calculated and they could be represented in a picture by the form of tripartite response spectrum. In the result, response spectrums of the 587 directional components of the above seismic data records were obtained respectively.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.411-413
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• 1992

This paper proposes a simple and high performance hybrid position/force control of robots based on disturbance compensation by using the disturbance observer in task coordinate space. The disturbance observer linealizes system of robot manipulators in task coordinate space and realizes acceleration control. To realize the strict acceleration control, the disturbance observer whose input is a position signal by simple computation, works as if it were a disturbance detector. The inverse kinematics can be simplified, because the disturbance observer in task coordinate space compensates not only the disturbance but also the error due to the simplification of the inverse kinematics. The new strategy is applied to a three-degrees-of freedom direct drive robot. The robust and simple hybrid position/force control is realized experimentally.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.330-337
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• 2010

This paper shows an algorithm for identifying track irregularities using wavelet transfer function along the railway. An equivalent SISO wavelet transfer function is defined using continuous wavelet transform by the measured track geometry and acceleration at a bogie of a train. The estimated track geometry is made by inverse continuous wavelet transform from the regressed signals of measured acceleration signal and the pre-defined wavelet transfer function. The estimated rail irregularity geometry is evaluated by the coherence function and comparison of FRF(Frequency Response Function). As a result of evaluated outcome, This algorithm is regarded as appropriate for estimation of rail irregularity.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.33-39
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• 1995

This paper presents a method of detecting localized defects on tapered roller bearing in main spindle of machine tool system. The statistical parameters in time-domain processing technique have been calculated to extract useful features from bearing vibration signals. These features are used by the input feature of an artificial neural network to detect and diagnose bearing defects. As a results, the detection of bearing defect conditions could be successfully performed by using an artificial neural network with statistical parameters of acceleration signals.

• Computational Structural Engineering
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• v.11 no.4
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• pp.135-146
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• 1998

대형 시스템의 건전성 평가를 위한 동적 재하시험에 있어서 변위를 측정하는 것보다 가속도를 측정하는 것이 수월하나 대부분의 공학적 기준은 응력과 비례관계를 지니는 변화를 기준으로 하고 있다. 본 연구에서는 시스템의 재하시험시 측정된 가속도신호를 이용하여 변위응답을 산정하는데 그 목적을 두고 적분을 위한 신호처리시 발생되는 문제점을 정상상태 및 천이영역에 대하여 규명하였다. 기존의 연구에서 고려하지 못하였던 초기조건의 항을 도입함으로써 시간영역의 적분과 주파수영역의 적분결과가 일치함을 해석적으로 입증하였으며 이동하중을 받는 보의 동적거동에 대하여 제시된 타당성을 검증하였다.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.377-380
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• 2001

The vestibular system located in the inner ear controls reflex body posture and movement. It has the semicircular canals sensing an angular acceleration and the otolith organs sensing a linear acceleration. With this organic signal, medical doctor decide if a person has disease or not. To obtain this data, a precision stimular system is considered. Robust control is needed to obtain eye signals induced by off-vertical axis rotation because of an unbalanced load produced by tilting the axis of the system upto 30 degrees. In this study, off-vertical axis rotatory system with visual stimulation system are developed. This system is consisted of head mounted display for generating horizontal, vertical, and three dimensional stimulus patterns. Furthermore wireless recording system using RF modem is considered for noiseless data transmission.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.238-246
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• 2003

By using simulated ground motions, which is sum of earthquake signals and noise, we measured the distortion of response spectra due to noise. We found that the distortion is more closely related to the signal-to-noise (S/N) ratio of root-mean-square (RMS) measurement than that of conventional peak measurement. Given a S/M ratio, the distortion of absolute acceleration response spectra is independent on the earthquake magnitude, while that of relative displacement response spectra has a strong dependence on the earthquake magnitude. This means that, when we calculate response spectra from time histories, we can efficiently predict the distortion of acceleration response spectra simply by measuring the RMS SJN ratios, or the distortion of displacement response spectra by combining the RMS S/N ratios and the earthquake magnitudes.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.25-32
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• 1995

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.