• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.347-348
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.553-554
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• 2011

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.599-608
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• 2018

As natural disasters are increasing due to the unusual weather and the modern society is getting complicated, the rapid change of the urban environment has increased human disasters. Thus, citizens are becoming more anxious about social safety. The importance of preparation for safety has been suggested by providing the disaster safety services such as regional safety index, life safety map, and disaster safety portal application. In this paper, we propose an application framework to predict the urban safety index based on user's location with realtime weather/atmosphere data after creating a predication model based on the machine learning using number of occurrence cases and weather/atmosphere history data. Also, we implement an application to provide traffic safety index with executing preprocessing occurrence cases of traffic and weather/atmosphere data. The existing regional safety index, which is displayed on the Si-gun-gu area, has been mainly utilized to establish safety plans for districts vulnerable to national policies on safety. The proposed system has an advantage to service useful information to citizens by providing urban safety index based on location of interests and current position with realtime related data.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.63-68
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• 2015

This paper proposes a robust multiple audio watermarking algorithm using improved QIM(quantization index modulation) with adaptive stepsize for different signal power and SVM(support vector machine) decoding model. The proposed algorithm embeds watermarks into both frequency magnitude response and frequency phase response using QIM. This multiple embedding method can achieve a complementary robustness. The SVM decoding model can improve detection rate when it is not sure whether the extracted data are the watermarks or not. To evaluate robustness, 11 attacks are employed. Consequently, the proposed algorithm outperforms previous multiple watermarking algorithm, which is identical to the proposed one but without SVM decoding model, in PSNR and BER. It is noticeable that the proposed algorithm achieves improvements of maximum PSNR 7dB and BER 10%.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.5
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• pp.684-690
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• 2010

A drum cam with rotational follower has a cam mechanism and it is mainly used in its application such as index table and ATC of machine tool. Also its use can reduce the backlash in its kinematic movement. To machine the drum cam with rotational follower, 5-axis CNC machine tool is generally used and its kinematic principle is included in it's design. Until now, the commercialized CAM software can't cover the application of the drum cam machining. Even if, some special software was developed for machining a drum cam, the post processing method for finish machining was not developed yet. And to overcome the problem, the form tool is still used on the tool path of rough machining. This study includes the induction of the post processing technique for the finish machining of drum cam on three 5-axis CNC machine tools, type AC, AB and BC. To prove the finishing geometric profile, the result was clearly verified through inspection and geometric measurement after direct machining of the drum cam in AC type 5-axis machine tool in this study.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.384-395
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• 2020

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.3
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• pp.18-26
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• 1991

A study on the measuring and evaluation of LSC(Least Square Center) roundness was carried out. The experimental set-up was made by index head and indicator, and the measuring data were compensated by a developed computer program. The results obtained are as follows : 1) An index head can conveniently be used to measure LSC roundness. 2) A program for calculating LSC roundness is developed. 3) Without a high quality roundnes measuring apparatus, LSC roundness can be measured and calculated by using index head and the developed program in machine shop as well as in a measuring room

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.192-197
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• 2005

Among many amplitude parameters, Kurtosis (4-th normalized moment of probability density function) is recognized to be the sensitive good parameter for machine diagnosis. Kurtosis has a value of 3.0 under normal condition and the value generally goes up as the deterioration proceeds. In this paper, simplified calculation method of kurtosis is introduced for the analysis of impact vibration with one sided affiliated impact vibration which occurs towards the progress of time. That phenomenon is often watched in the failure of such as bearings’ outer race. One sided affiliated impact vibration is approximated by one sided triangle towards the progress of time and simplified calculation method is introduced. Varying the shape of one sided triangle, various models are examined and it is proved that new index is a sensitive good index for machine failure diagnosis. Utilizing this method, the behavior of kurtosis is forecasted and analyzed while watching machine condition and correct diagnosis is executed.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1216-1223
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• 2019

The Double-sided Linear Switched Reluctance Machine (DLSRM) suffers from complex eccentricities in practical operations. A novel sensorless control method for a DLSRM with eccentricities is developed in this paper. The influences of eccentricities on the machine inductance characteristics and the estimated positions in sensorless control systems are discussed. A new position index, which is independent of eccentricities, is proposed according to an analysis of a DLSRM equivalent magnetic circuit. On the basis of this position index, the starting and low-velocity operation of eccentric DLSRMs are achieved. Experimental results obtained in the laboratory validate the proposed method.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.59-65
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• 2023

Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.