• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.2
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• pp.170-175
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• 2016

In order to transmit baseband signals through frequency dividing devices, we studied the transfer function of the device in the term of the baseband signal distortion. From the analysis, it is shown that the magnitude of the envelope signal is related to the mixer gain and the insertion loss of the low pass filter whilst the phase is the additional function with the 1/2 of the phase delay. For the purpose of the verification of the study, we designed a dynamic frequency divider at 1,400 MHz. The operating frequency range of the device is closely related to the conversion gain of mixers and the amplitude of input signal, and becomes wide as the conversion gain of mixers increases. The designed frequency divider operates between 0.9 GHz and 3.2 GHz, for -14.5 dBm input power. The circuit shows 20 mW power dissipation at $V_{DD}=2.5V$, and the simulation result shows that an amplitude modulated signal at 1,400 MHz with the modulation index of 0.9 was successfully downconverted to 700 MHz.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• Journal of Yeungnam Medical Science
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• v.18 no.1
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• pp.39-50
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• 2001

Background: Power spectrum analysis method is a powerful noninvasive tool for quantifying autonomic nervous system activity. In this paper, we developed a data acquistion system for estimating the activity of the autonomic nervous system by the analysis of heart rate and respiratory rate variability using power spectrum analysis. Materials and methods: For the detection of QRS peak and measurement of respiratory rate from patient's ECG, we used low-pass filter and impedence method respectively. This system adopt an isolated power for patient's safety. In this system, two output signals can be obtained: R-R interval heart rate) and respiration rate time series. Experimental ranges are 30-240 BPM for ECG and 15-80 BPM for respiration. Results: The system can acquire two signals accurately both in the experimental test using simulator and in real clinical setting. Conclusion: The system developed in this paper is efficient for the acquisition of heart rate and respiration signals. This system will play a role in research area for improving our understanding of the pathophysiologic involvement of the autonomic nervous system in various disease states.

• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.427-433
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• 2022

Obstacle detection, collision recognition, and avoidance technologies are required the collision avoidance technology for UAVs. In this paper, considering collinear multiple static obstacle, we propose an obstacle detection algorithm using LiDAR and a collision recognition and avoidance algorithm based on CPA. Preprocessing is performed to remove the ground from the LiDAR measurement data before obstacle detection. And we detect and classify obstacles in the preprocessed data using the K-means clustering algorithm. Also, we estimate the absolute positions of detected obstacles using relative navigation and correct the estimated positions using a low-pass filter. For collision avoidance with the detected multiple static obstacle, we use a collision recognition and avoidance algorithm based on CPA. Information of obstacles to be avoided is updated using distance between each obstacle, and collision recognition and avoidance are performed through the updated obstacles information. Finally, through obstacle location estimation, collision recognition, and collision avoidance result analysis in the Gazebo simulation environment, we verified that collision avoidance is performed successfully.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.5 no.4
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• pp.267-275
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• 2000

In an effort to assess the reliability of satellite altimeter system, we conducted a comparative analysis of sea level data that were collected using the TOPEX/POSEIDON (T/P) altimeter and the 10 tide gauge (TG) stations in the satellite passing track. The analysis was made using data sets collected from marginal sea regions surrounding the Korean Peninsula at T/P cycles of 2 to 230, which correspond to October 1992 to December 1998. Because of strong tidal activity in the study area, treatment of tidal errors is a very critical step in data processing. Hence in the computation of dynamic heights from the Tn data, we adapted the procedures of Park and Gamberoni (1995) to reduce errors associated with it. When these T/P data were treated, the alias periods of M$_2$, S$_2$, and K$_1$ constitutions were found at 62.1, 58.7, and 173 days. The compatibility of the T/P and TG data sets were examined at various filtering periods. The results indicate that the low-frequency signal of Tn data can be interpreted more safely with longer filtering periods (such as up to the maximum selected values of 200 days). When RMS errors for 200-day low-pass filter period was compared among the whole 10 tidal stations, the values spanned in the range of 2.8 to 6.7 cm. The results of correlation analysis at this filtering period also showed a strong agreement between the Tn and TG data sets over the whole stations investigated (e.g., P values consistently less than 0.0001). According to our analysis, we conclude that the analysis of surface sea level using satellite altimeter data can be made safely and reasonably long filtering periods such as 200 days.