• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1022-1028
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• 2013

It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.

• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.207-214
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• 2016

In this paper, we propose a radial reference map-based location fingerprinting technique with constant spacing from an access point (AP) to all reference points by considering the minimum dynamic range of the received signal strength indicator (RSSI) obtained through an experiment conducted in an indoor environment. Because the minimum dynamic range, 12 dBm, of the RSSI appeared every 20 cm during the training stage, a cell spacing of 80 cm was applied. Furthermore, by considering the minimum dynamic range of an RSSI in the location estimation stage, when an RSSI exceeding the cumulative average by ${\pm}6dBm$ was received, a previously estimated location was provided. We also compared the location estimation accuracy of the proposed method with that of a conventional fingerprinting technique that uses a grid reference map, and found that the average location estimation accuracy of the conventional method was 21.8%, whereas that of the proposed technique was 90.9%.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.22-35
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• 1992

In highly automated machining centers, intelligent sensor fddeback systems are indispensable on order to monitor their operations, to ensure efficient metal removal, and to initate remedial action in the event of accident. In this study, an on-line tool wear detection system for thrning operations is developed, and experimentally evaluated. The system employs multiple sensors and the signals from these sensors are processed using a multichannel autoegressive (AR) series model. The resulting output from the signal processing block is then fed to a previously tranied artificial neural network (multiayered perceptron) to make a final decision on the state of the cutting tool. To learn the necessary input/output mapping for tool wear detection, the weithts and thresholds of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions, and the ability of the system to detect tool wear is improved due to the generalization, fault-tolearant and self-ofganizing properties of the neural network.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.156-161
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• 2013

This study was aimed to implement the electroglottography (EGG) system for analyzing fundamental frequency of the phonation. EGG was recorded from the conductance between ring electrodes attached to the neck skin area near thyroid cartilage with high frequency carrier electric signals during vocalization, and voice signal was recorded with microphone simultaneously. EGG and voice signals were transmitted to the audio port in PC and recorded with stereo sound recording program. From the digitized data, several parameters such as pitch, jitter, shimmer, CQ and SQ were analyzed from the vowel sounds. For the voice training, sound fundamental frequency was displayed during the vocalization and singing a song using pitches analyzed from the EGG. The system implemented in this study could be used for vocal exercise.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1880-1884
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• 2000

The discrete wavelet transform is utilized as preprocessing of Neural Network(NN) to identify aging state of internal partial discharge in transformer. The discrete traveler transform is used to produce wavelet coefficients which are used for Classification. The statistical parameters (maximum of wavelet coefficients, average value, dispersion, skewness, kurtosis) using the wavelet coefficients are input into an back-propagation neural network. The neurons whose weights have obtained through Result of Cross-Validation. The Neural Network learning stops either when the error rate achieves an appropriate minimum or when the learning time overcomes a constant value. The networks, after training, can decide if the test signal is Early Aging State or Last Aging State or normal state.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.484-489
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• 2011

Some patients can't use their hands because of inherent and acquired paralysis of their fingers. Their fingers can recover with rehabilitative training, and the extent of rehabilitation can be judged by grasping a cylindrical-object with their fingers. At present, the cylindrical-object used in hospitals is only a cylinder which cannot measure grasping force of the fingers. Therefore, doctors must judge the extent of rehabilitation by watching patients' fingers as they grasp the cylinder. A cylindrical-type finger force measuring system which can measure the grasping force of patients' fingers should be developed. This paper looks at the development of a cylindrical-type finger force measuring system with two-axis force/moment sensor which can measure grasping force. The two-axis force/moment sensor was designed and fabricated, and the high-speed force measuring device was designed and manufactured by using DSP (digital signal processing). Also, cylindrical-type finger force measuring system was developed using the developed two-axis force/moment sensor and the high-speed force measuring device, and the grasping force tests of men were performed using the developed system. The tests confirm that the average finger forces of right and left hands for men were about 186N and 172N respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.26-33
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• 2008

Signal detection is a key technique in wireless communication system. Recently, several detection algorithms have been developed for multiple-input multiple-output (MIMO) wireless communication systems. However, most research in this area had assumed a flat-fading channel environment and all these techniques are based on the assumption that the channel state information (CSI) at the receiver side is perfect. But in practical situation, the available CSI may be imperfect because of channel estimation errors and/or outdated training. In this paper, we will compare the performance of several detection algorithms in MIMO frequency selective fading channel environment with imperfect CSI.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.55-64
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• 2010

In this paper, we propose a new compensation scheme for combined channel distortions and RF impairments based on the analysis of the impacts of IQ(In-phase/Quadrature) imbalance and phase noise on the OFDM(Orthogonal Frequency Division Multiplexing) system in the direct conversion transceiver and frequency selective fading channel distortion. The proposed scheme estimates the combined distortion by the use of training symbols and the residual distortion by pilot symbols and compensates the combined distortion, including IQ imbalance, phase noise and multipath fading at the same time. The simulation results show that the proposed scheme compensates the combined distortion of IQ imbalance, phase noise and multipath fading simultaneously.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.8
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• pp.557-564
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system can be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. In addition, the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Signal Processor DS1102 Board (TMS320C31).

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.336-340
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• 2003

The frame synchronization should be acquired before performing other data-aided receiving algorithms, such as data-aided channel equalizing, beam-forming and phase, symbol timing, and frequency synchronizing, since all of them are using preamble or training sequence to estimate the amount of error from the received signal. In this paper, we present a new frame synchronization scheme for underwater ultrasonic image burst transmission system, which computes the correlation between received symbol sequence and one CAZAC sequence, composed of the latter half of the first CAZAC sequence of preamble and the first half of the second CAZAC sequence of preamble and then compares a threshold value. If the correlation value is bigger than the threshold value, the frame detector determines that the frame synchronization is achieved at that sample.