• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.421-427
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• 2004

In order to exploit underwater resources successfully, the first step would be a marine environmental research and exploration in the seafloor. Generally one sets up a long-term underwater experimental unit in the seafloor and retrieves the unit later after a certain period time. Essential to these applications is the reliable teleoperation and telemetering of the unit. In this paper we presents a robust underwater sound recognition algorithm by which we can identify the sound signal without the influence of disturbances due to underwater environmental changes. The proposed method provides a means suitable for the acoustic releaser which requires low power dissipation and long-time underwater operation. We demonstrate its ability of securing stability and fast sound recognition through simulation methods.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.101-105
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• 2002

Counters for various kinds of bank notes require high-speed distinctive point extraction and recognition for notes. In this paper we propose a new point extraction and data extraction method from specific parts of a bank note representing the same color. The recognition algorithm uses a back-propagation neural network that has coordinate data input. The proposed algorithm is designed to minimize recognition time.

• MALSORI
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• no.46
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• pp.103-115
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• 2003

In this paper, we proposed Background Model Set algorithm used in the speaker verification to improve calculating confidence measure(CM) in speech recognition. CM is to display relative likelihood between recognized models and antiphone models. In previous method calculating of CM, we calculated probability and standard deviation using all phonemes in composition of antiphone models. At this process, antiphone CM brought bad recognition result. Also, recognition time increases. In order to solve this problem, we studied about method to reconstitute average and standard deviation using BMS algorithm in CM calculation.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.83-89
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• 2000

In this paper, we proposed a discriminative adaptation method for PMC algorithm and achieved improved speech recognition rate. For the adaptation, we adopted modified PMC(MPMC) which is a variant of PMC and discriminatively adapted the association factor for each mixture of the HMM in the MPMC. From the recognition experiments, the proposed method showed better recognition rate than the conventional PMC. Also, compared with STAR algorithm which is another model parameter compensation method, the proposed method showed superior performance when the SNR is very low and the adaptation data is not sufficient.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.546-552
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• 2016

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.16-23
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• 2015

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.870-876
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• 1993

This paper presents a binary clustering network (BCN) and a heuristic algorithm to detect pitch for recognition of keywords in continuous speech. In order to classify nonlinear patterns, BCN separates patterns into binary clusters hierarchically and links same patterns at root level by using the supervised learning and the unsupervised learning. BCN has many desirable properties such as flexibility of dynamic structure, high classification accuracy, short learning time, and short recall time. Pitch Detection algorithm is a heuristic model that can solve the difficulties such as scaling invariance, time warping, time-shift invariance, and redundance. This recognition algorithm has shown recognition rates as high as 95% for speaker-dependent as well as multispeaker-dependent tests.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.628-631
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• 1996

This paper proposes a surface recognition algorithm which determines the types of contact surfaces by fusing the information collected by the multisensor system, consisted of the optical tactile and force/torque sensors. Since the image shape measured by the optical tactile sensor system, which is used for determining the surface type, varies depending on the forces provided at the measuring moment, the force information measured by the f/t sensor takes an important role. In this paper, an image contour is represented by the long and short axes and they are fuzzified individually by the membership function formulated by observing the variation of the lengths of the long and short axes depending on the provided force. The fuzzified values of the long and short axes are fused using the average Minkowski's distance. Compared to the case where only the contour information is used, the proposed algorithm has shown about 14% of enhancement in the recognition ratio. Especially, when imposing the optimal force determined by the experiments, the recognition ratio has been measured over 91%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.4
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• pp.356-365
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• 1988

In this paper, we describe an isolated words recognition system for Korean isolated digits based on a voiced -unvoiced decision algorithm and a frequency domain analysis. The algorithm first performs a voiced-unvoiced decision procedure for the begtinning part of each uttered work using the normalized log energy and zero crossing rate as decision parameters. Based on this decision,. each word is assigned to one of two classes. In order to identify the uttered word within each class, a dynamic time warping algorithm is applied using formant frequencies as the basis for the distance measure. We exploit a pole-zero analysis to measure formant frequencies in each frame. We have observed that pole-zero analysis can provide more accurate estimation of formant frequencies than analysis based on poles only. Experimental recognition rates of 97.3% illustrating the performance of the recognition system was achieved.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4E
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• pp.21-27
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• 1996

In this paper, we propose a discriminative training algorithm for the stochastic segment model (SSM) in continuous speech recognition. As the SSM is usually trained by maximum likelihood estimation (MLE), a discriminative training algorithm is required to improve the recognition performance. Since the SSM does not assume the conditional independence of observation sequence as is done in hidden Markov models (HMMs), the search space for decoding an unknown input utterance is increased considerably. To reduce the computational complexity and starch space amount in an iterative training algorithm for discriminative SSMs, a hybrid architecture of SSMs and HMMs is programming using HMMs. Given the segment boundaries, the parameters of the SSM are discriminatively trained by the minimum error classification criterion based on a generalized probabilistic descent (GPD) method. With the discriminative training of the SSM, the word error rate is reduced by 17% compared with the MLE-trained SSM in speaker-independent continuous speech recognition.