• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.565-569
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• 2000

We compose the two-stage recurrent neural network that returns both signals of a hidden and an output layer to the hidden layer. It is tested on the basis of syllables for Korean spoken digit from /gong/to /gu. For these experiments, we adjust the neuron number of the hidden layer, the predictive order of input data and self-recurrent coefficient of the decision state layer. By the experimental results, the recognition rate of this neural network is between 91% and 97.5% in the speaker-dependent case and between 80.75% and 92% in the speaker-independent case. In the speaker-dependent case, this network shows an equivalent recognition performance to Jordan and Elman network but in the speaker-independent case, it does improved performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.1
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• pp.79-85
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• 1998

The 9-digit MMSI(Maritime Mobile Service Identity) in the NBDP is converted to the 7-digit identity signal, used to identify to the phase of and to synchronize to the clock of the ISS(Information Sending Station) automatically for phasing procedure between radio stations. In this paper, algorithms for the automatic identification phasing procedures and for conversions, calculations between 7-signals and 9-identity, those check-numbers in the phasing procedures for calling of the NBDP are discussed and designed. And a method of the faster locking and phasing for calling than that of recommended by ITU-R is suggested and implemented.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.503-506
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• 2001

본 논문에서는 획의 일부분이 손상된 숫자(broken digit)나 붙은 숫자(touching digits)와 같은 비정형 숫자들이 포함된 필기체 숫자 열을 인식할 수 있는 방법에 대하여 제안하였다. 비정형 숫자들은 분류(pre-segmentation) 단계에서 숫자들의 구조적인 특징 정보를 이용하여 정형인 개별 숫자(isolated digit)로부터 획의 일부분이 손상된 숫자 또는 붙은 숫자들로 분류된다. 획의 일부분이 분리된 숫자의 결합 및 붙은 숫자들의 분할 단계를 거쳐 인식을 시도하였다. 제안된 방법의 타당성을 증명하기 위하여 NIST SDl9 데이터베이스를 이용하여 시뮬레이션 해 보았다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2932-2934
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• 1999

In this paper, a scheme for recognition of handwritten digits using a multilayer neural network trained with the back-propagation algorithm using generalized delta rule is proposed. The neural network is trained with hand written digit data of different writers and different styles. One of the purpose of the work with neural networks is the minimization of the mean square error(MSE) between actual output and desired one. The back-propagation algorithm is an efficient and very classical method. The back-propagation algorithm for training the weights in a multilayer net uses the steepest descent minimization procedure and the sigmoid threshold function. As an error rate is reduced, recognition rate is improved. Therefore we propose a method that is reduced an error rate.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.329-331
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• 1994

In this paper, we present a speaker-dependent Korean Isolated digit recognition system. At the preprocessing step, LPC cepstral coefficients are extracted from speech signal, and are used as the input of a Frequency Sensitive Competitive Learning(FSCL) neural network. We carried out the postprocessing based on the winning-neuron histogram. Experimetal results Indicate the possibility of commercial auto-dial telephones.

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.119-122
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• 2003

In general, triangular shape filters are used in the filter bank when we get the MFCCs from the spectrum of speech signal. In [1], a new feature extraction approach is proposed, which uses specific filter shapes in the filter bank that are obtained from the spectrum of training speech data. In this approach, principal component analysis technique is applied to the spectrum of the training data to get the filter coefficients. In this paper, we carry out speech recognition experiments, using the new approach given in [1], for a large amount of telephone speech data, that is, the telephone speech database of Korean connected digit released by SITEC. Experimental results are discussed with our findings.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.79-90
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• 1998

The current increases in fuel price contribute to reducing level of congestion and air pollution. In this paper, this measure is compared with the congestion toll charge and the 10 'buje'(a $10\%$ demand control measure of registered vehicle i.e. each vehicle can not operate when its plate's last digit matches the last digit of the date). The purpose of our study is to give a priority order to various measures and to propose the win-win solution of Seoul for policy makers. We recommend the congestion toll charge coupled with some metering measures is the win-win solution for Seoul. Also we conclude a priority order such as metering; congestion toll charge; increase in parking fee; increase in fuel price; the 10 'buje' must be acceptable for implementation. Various ITS subsystems needed to support the proposed win-win solution are identified and field operational tests for them are introduced in this paper.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1128-1133
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• 2008

Cellulose based electroactive paper(EAPap) has been developed as a new smart material due to its advantages of piezoelectricity, large displacement, low power consumption, low cost and flexibility. EAPap can be used for a surface acoustic wave (SAW) device using the piezoelectric property of EAPap, resulting in the cost effective and flexible SAW device. In this paper, inter digit transducer(IDT) structure using lift-off technique with a finger gap of 10mm was used for micro fabrication of the cellulose EAPap SAW devices. The performance of IDT patterned SAW device was characterized by a Network Analyzer. The feasibility of cellulose EAPap as a potential acoustic device was presented and explained.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1015-1020
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• 1994

This paper presents the Korean digit recognition method under noise environment using the spectral mapping training based on static supervised adaptation algorithm. In the presented recognition method, as a result of spectral mapping from one space of noisy speech spectrum to another space of speech spectrum without noise, spectral distortion of noisy speech is improved, and the recognition rate is higher than that of the conventional method using VQ and DTW without noise processing, and even when SNR level is 0 dB, the recognition rate is 10 times of that using the conventional method. It has been confirmed that the spectral mapping training has an ability to improve the recognition performance for speech in noise environment.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.15-19
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• 2021

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.