• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.1
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• pp.28-33
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• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. The conventional methods for speech endpoint detection are based on two simple time-domain measurements-short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Accordingly, this algorithm is low complexity and suitable for Digital Isolated Word Recognition System.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.79-86
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• 2001

The CTD(Cumulative Trauma Disorder) as a new kind of occupational disease occurs mainly to workers on handling line under the highly-specialized industrial environments. This study took into account their exposure to Cumulative Trauma Disorders(CTD) by the utilization of EMG system, with respect to worker's muscle fatigue test according to fulfillment of iterative and simple task. The findings of this study were as follows : From the result of AEMG test analysis, worker's fatigue extent according to elapsed time of task was inclined to be increased continually. On the other hand, after its task ending, their fatigue extent was inclined to be decreased than before-circumstance of refractory brick lifting. The transference of MF(Median Frequency) and MPF(Mean Power Frequency) had highly significant difference between muscle fatigue and the elapsed time of work. Especially, their fatigue extent to erectorspinae and multifidus to lift firebrick was increased in the mean time. The transference of ZCR(Zero Crossing Rate) had considerable significant difference between muscle fatigue and the elapsed time of work. In short, as the work went of the muscle fatigue extent increased gradually. Thus, it can be concluded that the fatigue of erectorspinae and multifidus extent according to fulfillment of iterative and simple task is gradually being increased.

• The KIPS Transactions:PartC
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• v.18C no.1
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• pp.7-14
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• 2011

Power analysis attack on low-power consumed security devices such as smart cards is very powerful, but it is required that the correlation between the measured power signal and the mid-term estimated signal should be consistent in a time instant while running encryption algorithm. The power signals measured from the security device applying the random clock do not match the timing point of analysis, therefore random clock is used as counter measures against power analysis attacks. This paper propose a new constant pitch based time alignment for power analysis with random clock power trace. The proposed method neutralize the effects of random clock used to counter measure by aligning the irregular power signals with the time location and size using the constant pitch. Finally, we apply the proposed one to AES algorithm within randomly clocked environments to evaluate our method.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.157-165
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• 2007

The Work-Related Musculoskeletal Disorders (WMSDs) can be occurred by various factors such as repetition, forceful exertions and awkward postures. Especially, occurrences of the WMSDs on the waist and lower limb are reported in workplaces, demanded standing postures for a long time, in service and manufacturing industry. The static and standing postures without movement for a long time increase work loads to the lower limb and the waist. Accordingly, anti-fatigue mat or anti-fatigue insole is used as a preventing device of the WMSDs. However anti-fatigue mats are limited in space and movement. In this study, multi-elastic insoles are designed and shown the effects of the workload reduction for a long time under the standing work. The foot pressures and EMG (Electromyography) are measured at 0 hour and after 2 hours by 6 health students in their twenties. The 6 prototype insoles are designed with three elastic (Low, Medium and High). These insoles are compared with no insole (insole type 7) as control group. The EMG measurement was conducted to waist (erector spinae muscle), thigh (vastus lateralis muscle) and calf (gastrocnemius muscle). The foot pressure is analyzed by mean pressure value and the EMG analysis is investigated through MF (Median Frequency), MPF (Mean Power Frequency) and ZCR (Zero Crossing Rate). The results of the foot pressure show that the multi-elastic insoles had smaller foot pressure value than that of no-insole. Moreover, Insole 2 and Insole 3 have the smallest increasing rate in foot pressure. The EMG results show that the multi-elastic insoles had smaller EMG shift value than that of no-insole in 2 hour, and then shift value shows the smallest value in Insole 2. Therefore, this study presents that the multi-elastic insoles have reducing effects of the work load for a long time standing work in both side of foot pressure and EMG.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.31-39
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• 1995

In this paper, we have constructed phoneme discriminant filters [PDF] according to the linear discriminant function. These discriminant filters do not follow the heuristic rules by the experts but the mathematical methods in iterative learning. Proposed system. is based on the piecewise linear classifier and error correction learning method. The segmentation of speech and the classification of phoneme are carried out simutaneously by the PDF. Because each of them operates independently, some speech intervals may have multiple outputs. Therefore, we introduce the unified coefficients by the output unification process. But sometimes the output has a region which shows no response, or insensitive. So we propose time windows and median filters to remove such problems. We have trained this system with the 549 monosyllables uttered 3 times by 3 male speakers. After we detect the endpoint of speech signal using threshold value and zero crossing rate, the vowels and consonants are separated by the PDF, and then selected phoneme passes through the following PDF. Finally this system unifies the outputs for competitive region or insensitive area using time window and median filter.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.176-183
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• 1998

A new recognition technique using neural network coupled with Genetic Algorithms (GAs) was proposed. This technique concentrate on efficient Electromyography signal recognition through out improving neural network's several demerits. GAs paly a role of selecting Multilayer Perceptron's optimized initial connection weights by its typical global search. Electro Myography signal was pre-processed with Hidden Markov Model (HMM) in order to refect its time-varying property into input pattern except other features such as Zero Crossing Number(ZCN) and Integral Absolute Value (IAV). Results for 6 primitive motions show that the suggested technique has better performance in learning time and recognition rates than already established ordinary methods. Moreover, it performed stable recognition without convergence into a local minimum.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.82-88
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• 2002

In this paper, we proposed a audio indexing method that is used wavelet transform for audio data retrieval. It is difficult for audio data to make a efficient audio data index because of its own particular properties, such as requirement of large storage, real time to transfer and wide bandwidth. An audio data in del using wavelet transform make it possible to index and retrieval by using the particular wavelet transform properties. Our proposed indexing method doesn't separate data to several blocks. Therefore we use both high-pass and low-pass parts of last level coefficient of wavelet transform. Audio data indexing is made by applying the string matching algorithm to high-pass part and zero-crossing histogram to low-pass part. These are transformed to the continued strings, Through this method, we described a retrieval efficiency. The retrieval method is done by comparing the database index string to the query string and then data of minimum values is chosen to the result. Our simulation decided proper comparative coefficient and made known changing of retrieval efficiency versus audio data length. The results show that the proposed method improves retrieval efficiency compared to conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.691-700
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• 1994

A power line in not so good in characteristics for communication, because it is a media to transfer the commercial electrical power, and its load noise and high frequency noise are so much. Thus, a simple method to transfer a remote control signal on the power line is studied. The already-existing method is that two signals with upper part eliminated is transmitted every N step. But the method is investigated which the transmitter sends a period signal eliminated in arbitrary phase. Thus the transmission power loss due to elimination of signal can be reduced to the minimum. To implement it, a timer calculating the time from zero-crossing point to the phase is required. The micro-controller, 87C51, precisely calculates the phase using one of two built-in timers. As a result, a remote control signal tramsmitter and receiver using a partially eliminated signal, which is better than the conventional technique using half-eliminated signal in a efficiency of power transmission, is realized, and its characteristics are analyzed.

• Physical Therapy Korea
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• v.28 no.2
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• pp.123-131
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• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.