• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.82-89
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• 2014

Recently, inertial sensors are popularly used for the location and position recognition of small devices for M2M/IoT. In this paper, we designed low power, low noise, small sized 6-axis inertial sensor IC for mobile applications, which uses a 3-axis piezo-electric gyroscope sensor and a 3-axis piezo-resistive accelerometer sensor. Proposed IC is composed of 3-axis gyroscope readout circuit, two gyroscope sensor driving circuits, 3-axis accelerometer readout circuit, 16bit sigma-delta ADC, digital filter and control circuit and memory. TSMC $0.18{\mu}m$ mixed signal CMOS process was used. Proposed IC reduces 27% of the current consumption of LSM330.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.103-107
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• 2006

At present, most efforts tend to develop a INS which is only based linear accelerometers, because of the low cost micro-machining gyroscopes lack of the accuracy needed for precise navigation application and possible achieving the required levels of precise for micro-machining accelerometer. Although it was known in theory that a minimum of six accelerometers are required for a complete description of a rigid body motion, and any configuration of six accelerometers (except for a "measure zero " set of six-accelerometer schemes) will work. Studies on the feasible configuration of GF-INS indicate that the errors of angular velocity resolved from the six accelerometers scheme are diverged with time or have multi solutions. The angular velocity errors are induced by the biases together with the position vectors of the accelerometers, therefore, in order to treat with the problem just mentioned, researchers have been doing many efforts, such as the extra three accelerometers or the magnetometers may be taken as the reference information, the extended Kalman filter (EKF) involved to make the angular velocity errors bound and be estimated, and so on. In this paper, the typical configurations of GF-INS are introduced; for each type GF-INS described, the solutions to the angular velocity and the specific force are derived and the characteristic is indicated; one of the corresponding extend Kalman filters are introduced to estimate the angular errors; parts of the simulation results are presented to verify the validity of the equations of angular velocity and specific force and the performance of extend Kalman filter.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.27-34
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• 2016

A new algorithm for determining optimal accelerometer locations is proposed by using a frequency-domain Hankel matrix which is much simpler to construct than a time-domain Hankel matrix. The algorithm was examined through simulation studies by comparing the outcomes with those from other available methods. To compare and analyze the results from different methods, a dynamic analysis was carried out under seismic excitation and acceleration data were obtained at the selected optimal sensor locations. Vibrational amplitudes at the selected sensor locations were determined and those of all the other degrees of freedom were determined by using a spline function. MAC index of each method was calculated and compared to look at which method could determine more effective locations of accelerometers. The proposed frequency-domain Hankel matrix could determine reasonable selection of accelerometer locations compared with the others.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.263-264
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• 2010

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2001.07a
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• pp.73-78
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• 2001

• Proceedings of the KWS Conference
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• 2008.05a
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• pp.37-37
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• 2008

• Proceedings of the KWS Conference
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• 2007.11a
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• pp.76-78
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• 2007

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.254-259
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• 2013

A newly developed fall recognition algorithm using gravity weighted 3-axis accelerometer data as the input of HMM (Hidden Markov Model) is introduced. Five types of fall feature parameters including the sum vector magnitude(SVM) and a newly-defined gravity-weighted sum vector magnitude(GSVM) are applied to a HMM to evaluate the accuracy of fall recognition. A GSVM parameter shows the best accuracy of falls which is 100% of sensitivity and 97.96% of specificity, and comparing with SVM, the results archive more improved recognition rate, 5.2% of sensitivity and 4.5% of specificity. GSVM shows higher recognition rate than SVM due to expressing falls characteristics well, whereas SVM expresses the only momentum.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.934-937
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• 2010

In this paper, we did pursue the embodiment of a mouse pointing system which help the handicapped and people of not familiar with using electronics use electronic devices easily. Speech Recognition and 3-axis acceleration sensors in conjunction with a headset, a new mouse pointing system is constructed. We used speaker dependent system module which are generating the BCD code by recognizing human voices because it has high recognition rate rather than speaker independent system. Head-set mouse system is organized by 3-axis accelerometer, sound recognition module and TMS320F2812 processor. The main controller, TMS320F2812 DSP-processor is communicated with main computer by using SCI communications. The system is operated by Visual Basic in PC.

• Imaging Science in Dentistry
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• v.32 no.3
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• pp.159-165
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• 2002

Purpose: The aim of this study was to introduce a method of obtaining the oscillation graphs of the dental x-ray tube heads relative to time using an accelerometer. Materials and Methods: An Accelerometer, Piezotron type 8704B25 (Kistler Instrument Co., Amherst, NY, USA) was utilized to measure the horizontal oscillation of the x-ray tube head immediately after positioning the tube head for an intraoral radiograph. The signal from the sensor was transferred to a dynamic signal analyzer, which displayed the magnitude of the acceleration on the Y-axis and time lapse on the X -axis. The horizontal oscillation of the tube head was measured relative to time, and the settling time was also determined on the basis of the acceleration graphs for 6 wall type, 5 floor-fixed type, and 4 mobile type dental x-ray machines. Results : The oscillation graphs showed that tube head movement decreased rapidly over time. The settling time varied with x-ray machine types. Wall-type x-ray machines had a settling time of up to 6 seconds, 5 seconds for fixed floor-types, and 1 I seconds for the mobile-types. Conclusion: Using an accelerometer, we obtained the oscillation graphs of the dental x-ray tube head relative to time. The oscillation graph with time can guide the operator to decide upon the optimum exposure moment after x-ray tube head positioning for better radiographic resolution.