• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.465-469
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• 2004

We propose a new training system for the improvement of equilibrium sense using unstable platform. This system consists of unstable platform, computer interface and various softwares. The unstable platform was a simple structure of elliptical-type which included tilt sensor and wireless RF module. To evaluate the effort of balance training, we measured the parameters such as the moving time to the target and duration to maintain cursor in the target of screen. Balance training was carried out for two weeks and we classified the subjects into two groups by the training program. As a result, the moving time was reduced and duration time was lengthened through the repeating training of equilibrium sense using training program of sine curve trace(SCT) and Block game. Especially, there was remarkable improvement at direction which was too difficult for the subjects to balance their body. It was showed that this system had an effort on improving equilibrium sense and might be applied to clinical use as an effective balance training system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2037-2040
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• 2004

In this paper we introduce a new prototype based classifiers for overlapping data, where training pattern can be overlap on the feature space. The proposed classifier is based on the prototype from neural network classifier (NNC)[1] for overlap data. The method automatically chooses the initial center and two radiuses for each class. The center is used as a mean representative of training data for each class. The unclassified pattern is classified by measure distance from the class center. If the distance is in the lower (shorter radius) the unknown pattern has the high percentage of being in this class. If the distance is between the lower and upper (further radius), the pattern has the probability of being in this class or others. But if the distance is outside the upper, the pattern is not in this class. We borrow the words upper and lower from the rough set to represent the region of certainty [3]. The training algorithm to find number of cluster and their parameters (center, lower, upper) is presented. The clustering result is tested using patterns from Thai handwritten letter and the clustering result is very similar to human eyes clustering.

• International Journal of Industrial Entomology and Biomaterials
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• v.9 no.1
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• pp.111-115
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• 2004

Nutrition plays an important role in maintaining the larval health, cocoon quality and reproductive potential in Antheraea mylitta D. Nutritional efficiency greatly influenced if food is not adequate and of quality. A. mylitta silkworms were subjected to food deprivation for the period of 0 hr to 12 hrs /day to assess its effect on various nutritional parameters and indices, and its manifestation at different levels. Food ingesta, digesta, gain in body weight declined significantly at each level of deprivation, so also food utilization efficiency like consumption index (CI), growth rate (GR), approximate digestibility (AD), and efficiency of conversion of ingested food (ECI). This stress leads to decline in mean daily food ingesta by 16.73% to 39.76% and digesta by 28.98% to 54.01 % following a significant reduction in average daily body weight gain (27.68% to 55.09%). Food deprivation a1so caused significant loss in the silk gland weight, cocoon and shell weight (14.37% to 53.69%), lowered the fecundity (35.86 % to 83.59%) and in number of eggs laid per gram body weight, but simultaneously the number of non-chorionated eggs increased significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.308-320
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• 2016

Although the face almost always has an axisymmetric structure, it is generally not symmetrical image for the face image. However, the mirror image of the face image can reflect possible variation of the poses and illumination opposite to that of the original face image. A robust minimum squared error classification (RMSEC) algorithm is proposed in this paper. Concretely speaking, the original training samples and the mirror images of the original samples are taken to form a new training set, and the generated training set is used to perform the modified minimum sqreared error classification(MMSEC) algorithm. The extensive experiments show that the accuracy rate of the proposed RMSEC is greatly increased, and the the proposed RMSEC is not sensitive to the variations of the parameters.

• Speech Sciences
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• v.7 no.2
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• pp.113-126
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• 2000

A comparative phonetic analysis of Yodel singing and Belcanto singing by the electroglottographic(EGG) measurement was done in three singers. One professional tenor singer(SDI) who is also well trained in Yodel singing, another yodler(KWS) who is not so trained in Belcanto singing, and the other training tenor singer(CSK) who is not well trained both yodel and Belcanto singing. Closed quotient(CQ), speed quotient(SQ) and fundamental frequency (F0) at the initial modal part(I) , middle falsetto part(M), and final modal part(F) of the same phrase were measured by EGG machine and program(Kay model 4338). In the middle part, not only CQ but also SQ of the Yodel singing were much smaller than that of Belcanto singing in all three singers. However, accuracy of parameters in Belcanto singing of the yodler(KWS) and both Yodel singing and Belcanto singing of the training singer(CSK) were inferior to that of trained tenor singer(SDI). Possible advantages of utilizing Yodel singing training under the guidance of feedback control by the EGG for hyperfunctional voice disorders such as vocal nodules were discussed.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.388-390
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• 2005

In this paper, a new rehabilitation training system was developed to improve equilibrium sense by combining virtual reality technology with a fixed exercise bicycle. The subjects consisted of two groups. A group of young people, was compared against a group of elderly. We measured three different running modes of virtual bicycle system with two successive sets. The parameters measured were running time, velocity, the weight movement, the degree of the deviation from the road, and the variables about the center of pressure. The repeated training, our results showed that the running capability of the elderly improve compared, In addition, it was found out that the ability of postural control and the equilibrium sense was improved with the presentation of the visual feedback information of the distribution of weight. From the results of this experiment, we showed that our newly developed system might be useful in the diagnosis of equilibrium sense or in the improvement of the sense of sight and, somatic, and vestibular sense of the elderly in the field of rehabilitation training.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.526-531
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• 1997

To control nonlinear chemical processes, a new neural linearizing control scheme is proposed. This is a hybrid of a radial basis function(RBF) network and a linear controller, thus the control action applied to the process is the sum of both control actions. Firstly, to train the RBF newtork a linear reference model is determined by analyzing the past operating data of the process. Then, the training of the RBF newtork is iteratively performed to minimize the difference between outputs of the process and the linear reference model. As a result, the apparent dynamics of the process added by the RBF newtork becomes similar to that of the linear reference model. After training, the original nonlinear control problem changes to a linear one, and the closed-loop control performance is improved by using the optimum tuning parameters of the linear controller for the linear dynamics. The proposed control scheme performs control and training simultaneously, and shows a good control performance for nonlinear chemical processes.

• Advances in Computational Design
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• v.3 no.3
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• pp.289-302
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• 2018

The use of optimum content of supplementary cementing materials (SCMs) such as limestone filler (LF) to blend with Portland cement has been resulted in many environmental and technical advantages, such as increase in physical properties, enhancement of sustainability in concrete industry and reducing $CO_2$ emission are well known. Artificial neural networks (ANNs) have been already applied in civil engineering to solve a wide variety of problems such as the prediction of concrete compressive strength. The feed forward back propagation (FFBP) algorithm and Tan-sigmoid transfer function were used for the ANNs training in this study. The training, testing and validation of data during the backpropagation training process yielded good correlations exceeding 97%. A parametric study was conducted to study the sensitivity of the developed model to certain essential parameters affecting the compressive strength of concrete. The effects and benefits of limestone filler on hardened properties of the concrete such as compressive strength were well established endorsing previous results in the literature. The results of this study revealed that the proposed ANNs model showed a high performance as a feasible and highly efficient tool for simulating the LF concrete compressive strength prediction.

• Phonetics and Speech Sciences
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• v.4 no.4
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• pp.147-154
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• 2012

The purpose of this study is to examine the effect of voice improvement when vocal training, which relaxes the vocal contact, is applied to children with vocal nodules. Subjects included 20 5- to 12-year-old boys with vocal nodules in Otolaryngology and for whom voice therapy had been advised. The vocal therapy was conducted for 40 minutes per a week for a total of eight times. Results were evaluated by videostroboscopy, auditory-perceptual evaluation of GRBAS Scale, aerodynamic test, and acoustic analysis before and after therapy. As a result, first, the size of vocal nodules was reduced and the unstable pattern of vocal contact was improved. Glottic closure was increased and Phase symmetry was decreased during vocal vibration. Mucosal wave was increased and muscle tension of the larynx was reduced. Second, auditory-perceptual evaluation showed that subjects' overall quality of voice improved. GRBAS Scale Evaluation showed that the characteristics of the subjects' voice which were rough, breathy, and strained and breathy were reduced after therapy. Third, the measurements of acoustic parameters showed a statistically significant improvement. The fundamental frequency of the subejects' voice was increased and values of Jitter and Shimmer, NHR, [H1-H2] decreased. Fourth, the maximum phonation time of children was increased. These results imply that vocal relaxation training conducted in this study has a very positive effect to improve the voice of children with vocal nodules.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.88-95
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• 1994

This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration. Two neural networks were developed to forecast daily evapotranspiration of the rice crop with back-propagation and counter-propagation algorithm. The neural network trained by back-propagation algorithm with delta learning rule is a three-layer network with input, hidden, and output layers. The other network with counter-propagation algorithm is a four-layer network with input, normalizing, competitive, and output layers. Training neural networks was conducted using daily actual evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity, and pan evaporation. During the training, neural network parameters were calibrated. The trained networks were applied to a set of field data not used in the training. The created response of the back-propagation network was in good agreement with desired values and showed better performances than the counter-propagation network did. Evaluating the neural network performance indicates that the back-propagation neural network may be applied to the estimation of evapotranspiration of the rice crop. This study does not provide with a conclusive statement as to the ability of a neural network to evapotranspiration estimating. More detailed study is required for better understanding and evaluating the behavior of neural networks.