• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.292-293
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• 2008

In this paper, we propose a face hallucination method based on example-learning. The traditional approach based on example-learning requires alignment of face images. In the proposed method, facial images are segmented into patches and the weights are computed to represent input low resolution facial images into weighted sum of low resolution example images. High resolution facial images are hallucinated by combining the weight vectors with the corresponding high resolution patches in the training set. Experimental results show that the proposed method produces more reliable results of face hallucination than the ones by the traditional approach based on example-learning.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.269-276
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• 2004

This study tried to figure out the framework of safety culture in railway industry to analyze root causes of railway incidents and accidents at Korean National Railway. Through the literature survey, authors found some critical dimensions common to railway safety culture such as employees' belief in the managers' management weight on safety and productivity, recognition of safety importance, risk taking attitude, practice of safety meeting before and after doing work, communication between management and employees and among drivers, traffic managers and infrastructure maintenance workers, safety reporting practice, safety and performance appraisal, effectiveness of safety audit, safety training, work place arrangement, incidents and accidents investigation, and safety knowledge management.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.522-526
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• 1994

There are great needs for checking machine operation status precisely in the iron and steel plants. Rotating machines such as pumps, compressors, and motors are the most important objects in the plant maintenance. In this paper back-propagation neural network is utilized in diagnosing rotating machines. Like the finger print or the voice print of human, the abnormal vibrations due to axis misalignment, shaft bending, rotor unbalance, bolt loosening, and faults in gear and bearing have their own spectra. Like the pattern recognition technique, characteristic. feature vectors are obtained from the power spectra of vibration signals. Then we apply the characteristic feature vectors to a back propagation neural net for the weight training and pattern recognition.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.187-196
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• 1996

This paper introduces the concept of fixed weights and proposes an algorithm for classification by adding this concept to vector space separation method in LVQ. The proposed algorithm is based on competitive learning. It uses fixed weightsfor generality and fast adaptation efficient radius for new weight creation, and L1 distance for fast calcualtion. It can be applied to many fields requiring adaptive learning with the support of generality, real-tiem processing and sufficient training effect using smaller data set. Recognition rate of over 98% for the train set and 94% for the test set was obtained by applying the suggested algorithm to on-line handwritten recognition.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.50 no.2
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• pp.195-206
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• 2017

Joint time-frequency images of the broadband echo signals of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution in controlled environments. Affine transformation and principal component analysis were used to obtain eigenimages that provided species-specific acoustic features for each of the six fish species. The echo images of an unknown fish species, acquired in real time and in a fully automated fashion, were identified by finding the smallest Euclidean or Mahalanobis distance between each combination of weight matrices of the test image of the fish species to be identified and of the eigenimage classes of each of six fish species in the training set. The experimental results showed that the Mahalanobis classifier performed better than the Euclidean classifier in identifying both single- and mixed-species groups of all species assessed.

• Journal of the Korean Society of Costume
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• v.27
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• pp.147-161
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• 1996

This is a report on the fashion education in Itatly. The author looked into the character of education system and the curriculum of the fashion institutes in Italy. This report will be the guideline in the fashion education in Korea. The results of this study are as follows. 1. The curriculum of fashion education should be adjusted to be realistic and to keep the proper balance between theories and skills. 2. The Train for construct tchniques should be given much more weight in the total skill educations. 3. It is demanded that fashion institutes should take efforts to fill the gap between institutes and fashion industries for example field training. 4 It is better to change the sys-tem of fashion education as cultivating the various fashion specialists For this it is necessary to make various cources in the de-partment of clothings.

• MALSORI
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• no.47
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• pp.131-140
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• 2003

We propose a new method of generating client models for HMM based text-dependent speaker verification system with only a small amount of training data. To make a client model, statistical methods such as segmental K-means algorithm are widely used, but they do not guarantee the quality or reliability of a model when only limited data are avaliable. In this paper, we propose a blind speech segmentation based on level building DTW algorithm as an alternative method to make a client model with limited data. In addition, considering the fact that voiced sounds have much more speaker-specific information than unvoiced sounds and energy of the former is higher than that of the latter, we also propose a new score evaluation method using the observation probability raised to the power of weighting factor estimated from the normalized log energy. Our experiment shows that the proposed methods are superior to conventional HMM based speaker verification system.

• The Journal of Korean Physical Therapy
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• v.14 no.4
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• pp.441-453
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• 2002

The purpose of this study was to characterize lesion development, neural plasticity, and motor learing after spinal cord injury. Facilitatory intervention such as weight bearing and locomotor training after SCI may be more effective than compensatory strategies at inducing neuroplasticity and motor recovery. Minimal tissue sparing has a profound impact on segmental systems and recovery of function Spinal animal could functional locomotion when subjected to repetitive stimulation. task-specific learning of isolated lumbar spinal could improve motor performance more then other task learning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.6B
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• pp.629-637
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• 2002

RLS a1gorithm is a kind of the adaptive a1gorithms in smart antennas and adapts the weight vector using the difference between the output signal of array antennas and the known training sequence. In this paper, we propose a new algorithm based on the RLS algorithm. It calculates the error signal with reference signal derived from blind scheme. Simulation results show that the proposed algorithm yields more user capacity by 67∼74% than other blind adaptive algorithms(LS-DRMTA, LS-DRMTCMA) at the same BER and the beamformer forms null beams toward interference signals and the main beam toward desired signal.