• Speech Sciences
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• v.7 no.1
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• pp.31-37
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• 2000

This paper concerns effective speaker adaptation methods to solve the over-training problem in speaker verification, which frequently occurs when modeling a speaker with sparse training data. While various speaker adaptations have already been applied to speech recognition, these methods have not yet been formally considered in speaker verification. This paper proposes speaker adaptation methods using a combination of MAP and MLLR adaptations, which are successfully used in speech recognition, and applies to speaker verification. Experimental results show that the speaker verification system using a weighted MAP and MLLR adaptation outperforms that of the conventional speaker models without adaptation by a factor of up to 5 times. From these results, we show that the speaker adaptation method achieves significantly better performance even when only small training data is available for speaker verification.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.6
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• pp.276-283
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• 2002

This Paper Presents the method for detecting a partial discharge(PD) using an electromagnetic wave measured by an antenna. The various electromagnetic waves are measured in the laboratory and wavelet transform, which is provides a direct quantitative measure of spectral content in the time-frequency domain, are applied for identifying the property of electromagnetic waves. Also, the statistical method and self-organizing feature map(SOFM) are applied for the pattern recognition of electromagnetic waves. The proposed method is shown to be useful for detecting electromagnetic waves emitted for PD in test data.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.15 no.4
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• pp.111-118
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• 2012

The purpose of this study is to work out strategy for utilization of Environmental Conservation Value Assessment Map (ECVAM) using the user survey. It surveyed system users of ECVAM about its recognition and satisfaction. The results of the survey, the ECVAM became more popular and were highly satisfied with updated data. Especially, the study found a relationship between the satisfaction of ECVAM and accuracy, utilization, and convenience of the system. However, the satisfaction has a difference between user groups, a government official and a agent for EIA including researchers. The satisfaction of the agent group was affected by the convenience, the accuracy, and the utilization in order. In the other hand, the satisfaction of the government official group was affected by the utilization, the convenience, the accuracy, and recognition in order. Therefore, we need to adopt different strategies for educations of ECVAM and publicity activities depending on user groups. To increase the satisfaction of ECVAM, we should research not only to attain pinpoint accuracy, but also to suggest the guideline to utilize the map for a government official.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.79-85
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• 2018

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.77-86
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• 2014

As raster-based high quality and resolution spatial information has appeared, 1/1,000 digital map lost either its recognition or uses because of insufficient new modified and updated information. Therefore, this study analyzed the linkage between three-dimensional spatial information and 1/1,000 digital map, and also suggested a modification plan of 1/1,000 digital map, made by three-dimensional spatial information. In fact, some area of Incheon and Busan were presented with the modification plan of 1/1,000 digital map as three-dimensional trial models. These trials reflected possibilities of modification by qualitative and quantitative analysis of 1/1,000 digital map, using three-dimensional object model.

• The Journal of Korea Robotics Society
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• v.12 no.4
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• pp.432-440
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• 2017

In this paper, we propose a jellyfish distribution recognition and monitoring system using a UAV (unmanned aerial vehicle). The UAV was designed to satisfy the requirements for flight in ocean environment. The target jellyfish, Aurelia aurita, is recognized through convolutional neural network and its distribution is calculated. The modified deep neural network architecture has been developed to have reliable recognition accuracy and fast operation speed. Recognition speed is about 400 times faster than GoogLeNet by using a lightweight network architecture. We also introduce the method for selecting candidates to be used as inputs to the proposed network. The recognition accuracy of the jellyfish is improved by removing the probability value of the meaningless class among the probability vectors of the evaluated input image and re-evaluating it by normalization. The jellyfish distribution is calculated based on the unit jellyfish image recognized. The distribution level is defined by using the novelty concept of the distribution map buffer.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.3
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• pp.74-82
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• 1998

In this study, system of object recognition independent of translation and rotation using ultrasonic sensor and neural network is presented. The object recognition rate is 92.3[%] in spite of changing output neuron space size of SOFM neural network from$4\times4 to10\times10$and iteration from 10 to 50. The experimental results show that the proposed system of object recognition can be applied to the object recognition field of intelligent robot.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.507-517
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• 2003

Self-organizing map (SOM) has been developed mainly by T. Kohonen and his colleagues as a unsupervised learning neural network. Because of its topological ordering property, SOM is known to be very useful in pattern recognition and text information retrieval areas. Recently, data miners use Kohonen´s mapping method frequently in exploratory analyses of large data sets. One problem facing SOM builder is that there exists no sensible criterion for evaluating goodness-of-fit of the map at hand. In this short communication, we propose valid evaluation procedures for the Kohonen SOM of any size. The methods can be used in selecting the best map among several candidates.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.162-170
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• 2002

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.