• Journal of KIISE:Software and Applications
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• v.27 no.9
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• pp.952-960
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• 2000

본 논문에서는 퍼지 자동회귀 이동평균 (Fuzzy Auto-Regressive Moving Average, FARMA)제어기를 전력계통의 비선형 2기 5모선 (Two Machine - Five Bus)모형의 주파수 안정화에 적용한 결과를 제시한다 퍼지 자동회귀 이동 평균 제어기는 기존의 전문가에 의존하였던 퍼지제어 규칙을 실시간으로 형성해 나가는 구조이다. 복잡성, 비선형성 등 전력계통의 일반적인 특징들을 기술하고, 그 특징을 표현할 수 있는 비선형 2기5모선 모형을 제시한다. 제시된 모형을 바탕으로 기존 제어 방식과의 성능 비교를 실시하였고, 이를 통하여 FARMA 제어기의 우수성을 확인하였다.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.85-94
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• 1994

The cutting mechanism of ultrasonic vibration machining is characterized as two phases, that is, an impact at the cutting edge and a reduction of cutting force due to non-contact interval between tool and workpiece. In this paper, in order to identify cutting dynamics of a system with ultrasonically vibrated cutting tool, an ARMA modeling is performed on experimental cutting force signals which have a dominant effect on cutting dynamics. The aim of this study is, through Dynamic Date System methodology, to find the inherent characteristics of an ultrasonic vibration cutting process by considering natural frequency and damping coefficient. Surface roughness and stability of cutting process under ultrasonic vibration are also considered

• Journal of the KSME
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• v.28 no.2
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• pp.169-174
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• 1988

신호처리 및 해석에 대한 교육효과를 증대시키기 위해서는 응용사례의 발굴, 프로젝트 개발, P C를 이용한 그래픽스 교육도 이루어져야 하며, 스펙트럼 분석기술 이외에 시간영역 파라미터 모형에 의한 신호해석기법〔예를 들어 자동회귀-이동평균(ARMA)등의 시계열 모형화〕도 최근 에는 실시간 응용의 가능성이 높아지는 추세에 있으므로 PC를 이용한 신호처리 및 해석 교육에 반영하는 것이 바람직하다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.123-132
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• 2002

In this study, the static and dynamic characteristics of endmilling process was modelled and the analytic realization of chatter mechanism was discussed. In this regard, We have discussed on the comparative assessment of recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental work were performed to show the malfunctional behaviors. For this purpose, new recursive least square method (RLSM) were adopted for the on-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, The stability lobe of chatter was analysed by varying parameter of cutting dynamices in regenerative chatter mechanics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.971-974
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• 2001

In this study, the static and dynamic characteristics of endmilling process was modelled and the analytic realization of chatter mechanism was discussed. In this regard, We have discussed on the comparative assessment of recursive time series modeling algorithms that can represent the machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental work were performed to show the malfunctional behaviors. For this purpose, new recursive(RLSM) were adopted for the on-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, the stability lobe of chatter was analysed by varying parameter of cutting dynamices in regenerative chatter mechanics.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.3
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• pp.73-79
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• 2011

In this study, an analytical realization of end-milling system was introduced using recursive parametric modeling analysis. Also, the numerical mode analysis of end-milling system with different conditions was performed systematically. In this regard, a recursive least square modelling algorithm and the natural mode for real part and imaginary one was discussed. This recursive approach (RLSM) can be adopted for on-line end-milling identification. After experimental practice of the end-milling, the end-milling force was obtained and it was used for the calculation of FRF (Frequency response function) and mode analysis. Also the FRF was analysed for the prediction of a end-milling system using recursive algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.6
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• pp.239-248
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• 2018

Many new technologies are studied with the arrival of the 4th industrial revolution. In particular, emotional intelligence is one of the popular issues. Researchers are focused on emotional analysis studies for music services, based on artificial intelligence and pattern recognition. However, they do not consider how we recommend proper music according to the specific emotion of the user. This is the practical issue for music-related IoT applications. Thus, in this paper, we propose an probability-based music emotion classification technique that makes it possible to classify music with high precision based on the range of emotion, when developing music related services. For user emotion recognition, one of the popular emotional model, Russell model, is referenced. For the features of music, the average amplitude, peak-average, the number of wavelength, average wavelength, and beats per minute were extracted. Multiple regressions were derived using regression analysis based on the collected data, and probability-based emotion classification was carried out. In our 2 different experiments, the emotion matching rate shows 70.94% and 86.21% by the proposed technique, and 66.83% and 76.85% by the survey participants. From the experiment, the proposed technique generates improved results for music classification.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.423-432
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• 2022

This paper addresses the safety concern that the SOH of batteries in electric vehicles decreases sharply when drivers change or their driving patterns change. Such a change can overload the battery, reduce the battery life, and induce safety issues. This paper aims to present the SOH as the changes on a dashboard of an electric vehicle in real-time in response to user pattern changes. As part of the training process I used battery data among the datasets provided by NASA, and built models incorporating linear regression and ARIMA, and predicted new battery data that contained user changes based on previously trained models. Therefore, as a result of the prediction, the linear regression is better at predicting some changes in SOH based on the user's pattern change if we have more battery datasets with a wide range of independent values. The ARIMA model can be used if we only have battery datasets with SOH data.

• Journal of Power System Engineering
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• v.2 no.2
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• pp.73-80
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• 1998

The identification of drilling joint dynamics which consists of drilling and structural dynamics and the on-line time series detection of malfunction process is substantial not only for the investigation of the static and dynamic characteristics but also for the analytic realization of diagnostic and control systems in drilling. Therefore, We have discussed on the comparative assessment of two recursive time series modeling algorithms that can represent the drilling operation and detect the abnormal geometric behaviors in precision roundshape machining such as turning, drilling and boring in precision diemaking. For this purpose, simulation and experimental work were performed to show the malfunctional behaviors for drilling operation. For this purpose, a new two recursive approach (Recursive Extended Instrument Variable Method : REIVM, Recursive Least Square Method : RLSM) may be adopted for the on-line system identification and monitoring of a malfunction behavior of drilling process, such as chipping, wear, chatter and hole lobe waviness.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.160-165
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• 1993

The cutting mechanism of ultrasonic vibration machining is characterized as two phases, that is an impact at the cutting edge and a reduction of cutting force due to non-contact interval between tool and workpiece. In this paper, in order to identfy cutting dynamics of a system with ultrasonically vibrated cutting tool, an ARMA modelling is performed on experimental cutting force signals which have a dominant effect on cutting dynamics. The aim of this study is, through Dynamic Data System methodology, to find the inherent characteristics of an ultrasonic vibration cutting process by considering natural frequencyand damping coefficient. Surface roughness and stability of cutting process under ultrasonic vibration are also considered