• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.1
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• pp.78-84
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• 2014

The rotation of a spindle unit must be accurate for high-quality machining and to improve the quality of the machine tools.Therefore, the proper measurement of the rotation accuracy and ensuring a proper analysis are very important. Separate processes are necessary because spindle errors and roundness errors associated with the test balls can both factor into the measured rotation error values. We used three methods to discern test ball errors and analyzed which could be deemed as the most proper technique in a test of the rotation accuracy of the main spindle of a machine tool.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.49-54
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• 2016

In this paper, we propose a vocal separation method using weighted ${\beta}$-order minimum mean wquare error estimation (WbE) based on kernel back-fitting algorithm. In spoken speech enhancement, it is well-known that the WbE outperforms the existing Bayesian estimators such as the minimum mean square error (MMSE) of the short-time spectral amplitude (STSA) and the MMSE of the logarithm of the STSA (LSA), in terms of both objective and subjective measures. In the proposed method, WbE is applied to a basic iterative kernel back-fitting algorithm for improving the vocal separation performance from monaural music signal. The experimental results show that the proposed method achieves better separation performance than other existing methods.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1051-1059
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• 1992

In this paper, two general methods using time-series analysis in the functional separation of the myoelectric signal of human arm movements are developed. Autocorrelation, covariance method and sequential least squares algorithm were used to determine the model parameters and the order of signal model to describe six arm movement patterns` the forearm flexion and extension, the wrist pronation and supination, rotation-in and rotation-out. The confidence interval to classify the functions of arm movement was defined by the mean and standard deviation of total squared error. With the error signals of autoregressive(AR) model, the result showed that the highest success rate was obtained in the case of 4th order, and success rate was decreased with increase of order. Autocorrelation was the method of choice for better success rate. This technique might be applied to biomedical and rehabilitation engineering.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.261-266
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• 2017

In this paper, we propose a vocal and nonvocal separation method which uses a combination of kernel model and LSTM (Long-Short Term Memory) networks. Conventional vocal and nonvocal separation methods estimate the vocal component even in sections where only non-vocal components exist. This causes a problem of the source estimation error. Therefore we combine the existing kernel based separation method with the vocal/nonvocal classification based on LSTM networks in order to overcome the limitation of the existing separation methods. We propose a parallel combined separation algorithm and series combined separation algorithm as combination structures. The experimental results verify that the proposed method achieves better separation performance than the conventional approaches.

• Journal of Mechanical Science and Technology
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• v.17 no.6
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• pp.825-835
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• 2003

Previous studies have demonstrated the importance of joint angle errors mainly due to skin artifact and measurement errors during gait analysis. Joint angle errors lead to unreliable kinematics and kinetic analyses in the investigation of human motion. The purpose of this paper is to present the Joint Averaging Coordinate System (JACS) method for human gait analysis. The JACS method is based on the concept of statistical data reduction of anatomically referenced marker data. Since markers are not attached to rigid bodies, different marker combinations lead to slightly different predictions of joint angles. These different combinations can be averaged in order to provide a "best" estimate of joint angle. Results of a gait analysis are presented using clinically meaningful terminology to provide better communication with clinical personal. In order to verify the developed JACS method, a simple three-dimensional knee joint contact model was developed, employing an absolute coordinate system without using any kinematics constraint in which thigh and shank segments can be derived independently. In the experimental data recovery, the separation and penetration distance of the knee joint is supposed to be zero during one gait cycle if there are no errors in the experimental data. Using the JACS method, the separation and penetration error was reduced compared to well-developed existing methods such as ACRS and Spoor & Veldpaus method. The separation and penetration distance ranged up to 15 mm and 12 mm using the Spoor & Veldpaus and ACRS method, respectively, compared to 9 mm using JACS method. Statistical methods like the JACS can be applied in conjunction with existing techniques that reduce systematic errors in marker location, leading to an improved assessment of human gait.

• Proceedings of the Acoustical Society of Korea Conference
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• 1984.12a
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• pp.100-106
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• 1984

Traditional methods for estimating the location of underwater target, i.e. the triangulation method and the wavefront curvature method, have been utilized. The location of a target is defined by the range and the bearing, which estimates can be obtained by evaluating the time delay between neighboring sensors. Many components of error occur in estimating the target range, among which the error due to the fluctuation of heading angle is outstanding. In this paper, the wavefront curvature method was used. We considered the error due to the heading fluctuation as the $\beta$-density process, from which we analized the range estimates with $\beta$-density function exist in some finite limits, and its mean value and variation are depicted as a function of true range and heading fluctuation. Given heading angles and sensor separation, maximum estimated heading errors are presented as a function of true range.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.242-247
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• 2009

In this paper, we investigate interference between short-range radiocommunication devices (SRDs) with frequency hopping spread spectrum (FHSS) and direct sequence spread spectrum (DSSS) methods when they are in the same frequency bands. In order to analyze interference from unwanted emission of SRD with DSSS to that of FHSS, Monte-Carlo (MC) simulation method is employed and interference probabilities are calculated. We simulate interference scenarios in accordance with several duty cycles and bandwidths. It is also assumed that the propagation model is free space The effect of distance between interfering transmitter and victim receiver is analyzed and bit error rate (BER) is simulated. From the interference analysis results, it is shown that duty cycle affects compatibility more than bandwidth does. Also, we can make sure of the separation distance which satisfies BER criterion when there are only one interfering transmitter and multiple interfering transmitters.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.2
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• pp.689-696
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• 1996

A comparison is made of the heat loss from a hollow cylinder, computed using an one-dimensional analytic method and a two-dimensional separation of variables scheme. For a two-dimensional analysis, the temperature of the inner surface as a boundary condition can be varied along the length of the cylinder by varing the temperature variation factor, b. Comparisons of the heat loss from the hollow cylinder using these two methods are given as a function of non-dimensional cylinder length, the ratio of the outer radius to the inner radius, temperature variation factor and Biot number. The result shows that the value of the heat loss from the hollow cylinder obtained using the one-dimensional analytic method becomes close to the value given by the two-dimensional separation of variables scheme as the value of Biot number and the non-dimensional hollow cylinder length increase and as the ratio of the outer radius to the inner radius decreases.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1498-1502
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• 2007

There have been many different definitions of energy functions as the second statistics of a signal. In this paper, using the higher order differential energy function, we propose an algorithm separating the amplitude and frequency components in a discrete sinusoidal signal. The proposed amplitude and frequency estimation methods have less computational requirement than the existing methods. It also shows large computational advantage over the root mean square (RMS) calculation of a signal. The proposed methods can be used in the detection of abnormal events in signals on the power line. Computer simulations show that proposed frequency estimation method can detect the presence of voltage increase or decrease for a short period of time. Also, the proposed estimation methods have been compared with existing methods in terms of estimation error variance.