• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.412-415
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• 2011

본 논문에서는 정적 무선 센서 네트워크상의 클러스터링 기법인 EECS(Energy Efficient Clustering Scheme)의 노드와 Base Station간의 거리를 고려한 head 선출 과정에 노드의 이동성 및 미래 위치 예측을 융합하여 확장한 새로운 동적환경상의 클러스터링 기법 EECS-M(Energy Efficient Clustering Scheme in Mobile wireless sensor networks)을 제안한다. 실험을 통하여 EECS-M이 동적 환경상의 LEACH-M, WCA 및 정적 환경상의 EECS, LEACH 클러스터링 알고리즘들에 비해 life time 및 life time 대비 네트워크의 잔여 에너지 측면에서 성능향상을 가진다는 것을 보인다.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1103-1120
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• 2015

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

• The Journal of Information Systems
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• v.18 no.1
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• pp.145-157
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• 2009

If industrial disasters happen at workplace, work's desires of workers and business activities may be dispirited. Especially, industrial disasters frequently happen in the field of heavy and chemical industry companies. According to report of the korea occupational safety and health agency(KOSHA), damage due to industrial disasters is more 5 times than losses due to industrial strifes. Thus, in these manufacturing companies, a safety management field for preventing industrial disasters is emerging as an important factor of business activities. This paper proposes an industrial disaster prediction model for safety management and also implements industrial safety management system(after this, is called as u-Safety system) based on the proposed model using ubiquitous computing technologies like USN(ubiquitous sensor networks) which are given much attention among recent information technologies. The implemented system is successfully operating in the shipbuilding division of 'H' companies.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.791-796
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• 2001

Welding using lasers can be mass-produced in high speed. In the laser welding, performing real-time evaluation of the welding quality is very important in enhancing the efficiency of welding. In this study, the plasma and molten metal which are generated during laser welding were measured using the UV sensor and IR sensor. The results of laser welding were classified into five categories such as optimal heat input, little low heat input, low heat input, focus off, and nozzle change. Also, a system was formulated which uses the measured signals with a fuzzy pattern recognition method which is used to perform real-time evaluation of the welding quality and the defects which can occur in laser welding. Weld quality prediction program was developed using previous weld results and statistical program which could show the trend of weld quality and signal was developed.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.4
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• pp.157-162
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• 2009

This study's purpose is to forecast the market demand of UVS (u-Transportation Vehicle Sensor) OBU (On-board Unit) of the ubiquitous Transportation. Bass model, Logistic model, and Gompertz model were used for the forecasting market demand. Firstly, this research focused on the market size for the u-T OBU. All three models were used for the market size prediction and the average values were used. The Bass model were calibrated and the market demand for the UVS OBU of the u-Transportation system were estimated using this model.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.485-489
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• 2004

Cutting force is important to understand cutting process in milling. To measure cutting force, tool dynamometer is widely used but it is hard to apply in workshop condition. Cutting force measurement which doesn't affect cutting process is needed. Using relations between cutting force and spindle displacement, cutting force can be predicted. Cylindrical capacitive sensor was used to measure spindle displacement during cutting. And signals from tool dynamometer collected to compare with spindle displacement. The result shows spindle displacement has a linear relation with cutting force. Using this result, a simple method to predict cutting force could be applied at workshop condition.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.28-33
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• 2003

Achievements in the real-time soil spectro-photometer are: an improved soil penetrator to ensure a uniform soil surface under high speed conditions, real-time collecting of underground soil reflectance, getting underground soil color images, use of a RTK-GPS, and all units are arranged for compactness. With the soil spectrophotometer, field experiments were conducted in a 0.5 ha paddy field. With the original reflectance, averaging and multiple scatter correction, Kubelka-Munk (KM) transformation as soil absorption, its 1st and 2nd derivatives were calculated. When the spectra was highly correlated with the soil parameters, stepwise regression analysis was conducted. Results include the best prediction models for moisture, soil organic matter (SOM), nitrate nitrogen (NO$_3$-N), pH and electric conductivity (EC), and soil maps obtained by block kriging analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.428-428
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• 2000

A robust intelligent algorithm for AGV navigation control is presented here based on both magnetic and gyro sensors to track a reference trajectory. Since the proposed system uses an intermittent array of short magnetic tape strips, it lends itself to a very easy installation and maintenance compared to other types of positioning references such as electric wire, magnets, RF and laser beacons. The neural network is to predict the lateral deviation of the AGV in the intervals where no magnetic tape references are available. Further, the use of intelligent control ensures a robust and flexible control performance. Computer simulation of AGV control demonstrates its adequate tracking performances even where the sensor information is not available. Real experiments using Samsung AGV are also on the way for real verification

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.244-250
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• 1997

The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent Property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability's or components are combined, which results in the dynamic MTTF or system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1014-1020
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• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.