• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.83-86
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• 2002

This paper intends to provide convenience to the disabled, losing the use of their limbs, through voice recognition technology. The voice recognition part of this system recognizes voice by DTW (Dynamic Time Warping) Which is most Widely used in Speaker dependent system. Specially, S/N rate was improved through Wiener filter in the pre-treatment phase while considering real environmental conditions; the result values of 12th order feature pattern per frame are extracted by DTW algorithm using LPC and Cepsturm in feature extraction process. Furthermore, miniaturization is pursued using TMS320C32, 71's the floating-point DSP, for the hardware part. Currently, 90% of hardware porting has been completed, but we can confirm that the recognition rate was 96% as a result of performing the DTW algorithm in PC.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.201-208
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• 2014

In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.201-206
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• 2004

This study attempted to develop a speech recognition module applied to a wheelchair for the physically handicapped. In the proposed speech recognition module, TMS320C32 was used as a main processor and Mel-Cepstrum 12 Order was applied to the pro-processor step to increase the recognition rate in a noisy environment. DTW (Dynamic Time Warping) was used and proven to be excellent output for the speaker-dependent recognition part. In order to utilize this algorithm more effectively, the reference data was compressed to 1/12 using vector quantization so as to decrease memory. In this paper, the necessary diverse technology (End-point detection, DMA processing, etc.) was managed so as to utilize the speech recognition system in real time

• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.10a
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• pp.282-285
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• 2004

The main purpose of this study is the vibration suppression of rotating composite blade containing distributed piezoelectric sensors and actuators. The blade is modeled by thin-walled, single cell composite beam including the warping function, centrifugal force, Coriolis acceleration and piezoelectric effect. Further, the numerical study is performed m ing finite element method. The vibration of composite rotor is suppressed by piezocomposite actuators and PVDF sensors that are embedded between composite layers. A velocity feedback control algorithm coupling the direct and converse piezoelectric effect is used to actively control the' dynamic response of an integrated structure through a closed control loop. Responses of the rotating blade are investigated. Newmark time integration method is used to calculate the time response of the model. In the numerical simulation, the effect of parameters such as rotating speed, fiber orientation of the blade and size of actuators are studied in detail.

• The Journal of the Acoustical Society of Korea
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• v.20 no.1
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• pp.50-55
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• 2001

This paper presents a korean isolated words recognition algorithm which used new endpoint detection method, auditory model, 2D-DCT and new distance measure. Advantages of the proposed algorithm are simple hardware construction and fast recognition time than conventional algorithms. For comparison with conventional algorithm, we used DTW method. At result, we got similar recognition rate for speaker dependent korean isolated words and better it for speaker independent korean isolated words. And recognition time of proposed algorithm was 200 times faster than DTW algorithm. Proposed algorithm had a good result in noise environments too.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.119-122
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• 2000

In this paper, we implement an elementary system to correct accent, pronunciation, and intonation in English spoken by non-native English speakers. In case of the accent evaluation, energy and pitch information are used to find stressed syllables, and then we extract the segment information of input patterns using a dynamic time warping method to discriminate and evaluate accent position. For the pronunciation evaluation. we utilize the segment information using the same algorithm as in accent evaluation and calculate the spectral distance measure for each phoneme between input and reference. For the intonation evaluation. we propose nine pattern of slope to estimate pitch contour, then we grade test sentences by accumulated error obtained by the distance measure and estimated slope. Our result shows that 98 percent of accent and 71 percent of pronunciation evaluation agree with perceptual measure. As the result of the intonation evaluation. system represent the similar order of grade for the four sentences having different intonation patterns compared with perceptual evaluation.

• Speech Sciences
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• v.7 no.2
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• pp.245-253
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• 2000

In this paper, we implement an elementary system to correct accents and pronunciations in English words spoken by non-native English speakers. In case of the accent evaluation, energy and pitch information are used to find stressed syllables, and then we extract the segment information of input patterns using a dynamic time warping method to discriminate and evaluate accent position. For the pronunciation evaluation, we utilize the segment information using the same algorithm as in accent evaluation, and perform the spectral distance measure for each phoneme between input patterns and reference patterns. Based on these spectral distances, we decide whether to recommend the pronunciation correction or not. Our results show that 98 percent of accent and 71 percent of pronunciation evaluation agree with the perceptual measure.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.791-797
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• 2008

The officer of the deck(OOD) may sometimes have to carry out lookout as well as handling of auto pilot without a quartermaster at sea. The purpose of this paper is to develop the ship's auto pilot control module using speech recognition in order to reduce the potential risk of one man bridge system. The feature parameters predicting the OOD's intention was extracted from the sample wheel orders written in SMCP(IMO Standard Marine Communication Phrases). We designed a pre-recognition procedure which could make some candidate words using DTW(Dynamic Time Warping) algorithm, a post-recognition procedure which made a final decision from the candidate words using the feature parameters. To evaluate the effectiveness of these procedures the experiment was conducted with 500 wheel orders.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.709-716
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• 2004

This paper describes an intelligent real-time monitoring system of a semiconductor processing equipment, which determines normal or not for a wafer in processing, using multiple time-series pattern recognition. The proposed system consists of three phases, initialization, learning and real-time prediction. The initialization phase sets the weights and tile effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and fathered for processing wafers by the equipment, using LBG algorithm. Each pattern has an ACI which is measured by a tester at the end of a process The real-time prediction phase corresponds a time series entered by real-time with the clustered patterns using Dynamic Time Warping, and finds the best matched pattern. Then it calculates a predicted ACI from a combination of the ACI, the difference and the weights. Finally it determines Spec in or out for the wafer. The proposed system is tested on the data acquired from etching device. The results show that the error between the estimated ACI and the actual measurement ACI is remarkably reduced according to the number of learning increases.