• Journal of Digital Convergence
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• v.18 no.2
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• pp.483-487
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• 2020

In this study, the effects of suspension training according to the types of ground. Fourteen healthy male college students measured for the characteristics of core muscle activity in suspension training on two different types of grounds, normal flat and unstable ground using a gym ball. EMG (Electromyography) was exploited to measure the activity of the core muscles according to the types of the ground. Muscle activity of the abdominal muscles, external oblique muscles, internal oblique muscles, and lower lumbar standing muscles was measured. The variables in analyses were measured by the means of % MVC method to standardize the EMG signal according to the ground type for each core muscle. In order to verify the differences in core muscles according to the type of ground the paired t-tests were performed at the significance level of 0.05 (p<.05). As a result of measuring the activity of the core muscles according to the various types of grounds, the difference between muscle characteristics obtained in two different grounds did not appear to be statistically significant. However, the result is an important clue to reconsider the notion that the training effect on the unstable ground is generally superior to the effect on the stable ground in the core muscle training. The type of ground in the core muscle training has been found not to significantly affect the muscle activation according to the results of this study. Regardless of the type of exercise program, hence, the difference in muscle activation will not be insignificant even with the standardized program strengthening core muscles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.20-37
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• 2023

Although traffic signal installations are continuously expanding, the effect of preventing traffic accidents remains unverified. Totally, 7,045 traffic accident data (such as signal violations) registered with TCS were manually searched for a 7-year period from 2013 to 2019 for 1,602 traffic signals in Daejeon Metropolitan City. The top 20 traffic accident intersections were identified, the traffic accident investigation records and field maps were viewed to compare the driving direction and signal phase of the violated vehicle, and the cause of the traffic accident was divided into insufficient signal operation design (operation) and driver negligence (intentional). Results of the analysis revealed that 75% of traffic accidents occurred in thru-left-turn traffic signals and overlap; moreover, extending the yellow time or operating all red signals due to countermeasures against traffic accidents occurring in yellow signals resulted in reduced traffic accidents. Data indicated that Permissive Left Turn requires improvement with the signal operation. In addition, since The Korean National Police Agency is not computerized for traffic accident sites and signal-related data, the lack of manpower necessitates improvement and utilization of TCS when establishing traffic accident prevention measures. It is believed that it will contribute to signal operation by analyzing vast amounts of data collected in the field and presenting improvement measures.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1251-1264
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• 2018

The systematic and effective design method of a Class-D current-source resonant inverter for use in an induction cooker with zero-ripple line current is presented. The design procedure is based on the principle of the Class-D current-source resonant inverter with a simplified load network model that is a parallel equivalent circuit. An induction load characterization is obtained from a large-signal excitation test-bench based on parallel load network, which is the key to an accurate design for the induction cooker system. Accordingly, the proposed scheme provides a systematic, precise, and feasible solution than the existing design method based on series-parallel load network under low-signal excitation. Moreover, a zero-ripple condition of utility-line input current is naturally preserved without any extra circuit or control. Meanwhile, a differential-mode input electromagnetic interference (EMI) filter can be eliminated, high power quality in utility-line can be obtained, and a standard-recovery diode of bridge-rectifier can be employed. The step-by-step design procedure explained with design example. The devices stress and power loss analysis of induction cooker with a parallel load network under large-signal excitation are described. A 2,500-W laboratory prototype was developed for $220-V_{rms}/50-Hz$ utility-line to verify the theoretical analysis. An efficiency of the prototype is 96% at full load.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.433-441
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• 2019

In this paper, a method of estimating speech parameters for ultrasonic Doppler signals reflected from the articulatory muscles using LSTM (Long Short Term Memory) RNN (Recurrent Neural Networks) was introduced and compared with the method using MLP (Multi-Layer Perceptrons). LSTM RNN were used to estimate the Fourier transform coefficients of speech signals from the ultrasonic Doppler signals. The log energy value of the Mel frequency band and the Fourier transform coefficients, which were extracted respectively from the ultrasonic Doppler signal and the speech signal, were used as the input and reference for training LSTM RNN. The performance of LSTM RNN and MLP was evaluated and compared by experiments using test data, and the RMSE (Root Mean Squared Error) was used as a measure. The RMSE of each experiment was 0.5810 and 0.7380, respectively. The difference was about 0.1570, so that it confirmed that the performance of the method using the LSTM RNN was better.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.2
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• pp.85-93
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• 2012

In this paper, we proposed Wireless LAN (WLAN) localization method that enhances database construction based on weighting factor and analyse the characteristic of the WLAN received signals. The weighting factor plays a key role as it determines the importance of Received Signal Strength Indication (RSSI) value from number of received signals (frequency). The fingerprint method is the most widely used method in WLAN-based positioning methods because it has high location accuracy compare to other indoor positioning methods. The fingerprint method has different location accuracies which depend on training phase and positioning phase. In training phase, intensity of RSSI is measured under the various. Conventional systems adapt average of RSSI samples in a database construction, which is not quite accurate due to variety of RSSI samples. In this paper, we analyse WLAN RSSI characteristic from anechoic chamber test, and analyze the causes of various distributions of RSSI and its influence on location accuracy in indoor environments. In addition, we proposed enhanced weighting factor algorithm for accurate database construction and compare location accuracy of proposed algorithm with conventional algorithm by computer simulations and tests.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.238-242
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.46-49
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• 1998

In this paper, we introduce the notion of intelligent communication by introducing a novel intelligent receiver model. This receiver is continually evolving and learns and improves in performance as it compiles its experience over time. In digital communication context, in a typical training mode, it jearns the concept of "1" as is deteriorated by arbitrary (not necessarily additive as is typically assumed) disturbance and /or modulation. After learning "1", in test mode, it classifies the received signal "1" and "0" almost completely. The intelligent receiver as implemented is grounded on the recently introduced Stochastic Morphological Sampling Theorem(SMST), a distribution-free result which gives theoretical bounds on the sample complexity(training size) needed for the required performance parameters such as accuracy($\varepsilon$) and confidence($\delta$). Based on this theorem, we demonstrate --almost irrespective of channel and modulation model-- the number of samples needed to learn the concept of "1" is not too "large" and the resulting universal receiver structure, that corresponding to classical Nearest Neighbor rule in Pattern Recognition Theory, is trivial. We check the surprising efficiency and validity of this model through some simple simulations. and validity of this model through some simple simulations.

• ETRI Journal
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• v.34 no.4
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• pp.511-517
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• 2012

The best linear unbiased estimator (BLUE) is most suitable for practical application and can be determined with knowledge of only the first and second moments of the probability density function. Although the BLUE is an existing algorithm, it is still largely unexplored and has not yet been applied to channel estimation in amplify and forward (AF)-based wireless relay networks (WRNs). In this paper, a BLUE-based algorithm is proposed to estimate the overall channel impulse response between the source and destination of AF strategy-based WRNs. Theoretical mean square error (MSE) performance for the BLUE is derived to show the accuracy of the proposed channel estimation algorithm. In addition, the Cram$\acute{e}$r-Rao lower bound (CRLB) is derived to validate the MSE performance. The proposed BLUE channel estimation algorithm approaches the CRLB as the length of the training sequence and number of relays increases. Further, the BLUE performs better than the linear minimum MSE estimator due to the minimum variance characteristic exhibited by the BLUE, which happens to be a function of signal-to-noise ratio.

• Speech Sciences
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• v.9 no.4
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• pp.237-252
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• 2002

MIT has been applied for nonfluent aphasic patients on the basis of lateralization of brain hemisphere. However, its applications for different languages have some inquiry for aphasic patients because of prosodic and rhythmic differences. The purpose of this study is to develop the Korean Melodic Intonation Therapy program using personal computer and its clinical effects for nonfluent aphasic patients. The algorithm was composed to voice analog signal, PCM, AMDF, Short-time autocorrelation function and center clipping. The main menu contains pitch, waveform, sound intensity and speech files on window. Aphasic patients' intonation patterns overlay on selected kMIT patterns. Three aphasic patients with or without kMIT training participated in this study. Four affirmative sentences and two interrogative sentences were uttered on CSL by stimulus of ST. VOT, VD, Hold and TD were measured on Spectrogram. In addition, articulation disorders and intonation patterns were evaluated objectively on spectrogram. The results indicated that nonfluent aphasic patients with kMIT training group showed some clinical effects of speech intelligibility based on VOT, TD values, articulation evaluation and prosodic pattern changes.