• International Journal of Industrial Entomology and Biomaterials
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• v.15 no.1
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• pp.17-21
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• 2007

An indigenous multivoltine silkworm, Nistari was evaluated for their thermo tolerance by exposing the larvae to various temperature regimes for eight hours. Among different temperature exposed, this strain has significant tolerance at $32^{\circ}C$. Analysis of heat shock protein revealed the expression of 70 kDa and 64 kDa polypeptides in fat body and midgut tissues. Interestingly esterase isozyme pattern in midgut showed characteristic expression of Est-1 and Est-3 at different temperatures signifying role in heat and cold shock.

• Asian Journal of Innovation and Policy
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• v.4 no.3
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• pp.307-327
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• 2015

This study attempts to develop a methodology that analyzes patent applications to identify future skills, in particular in the sector of information security, recently into the spotlight. Matching skill elements from the International Patent Classification (IPC) with skill units from job analysis, the study tries to track trends in the skills needs based on IPC time-pattern. It then verifies the validity of the outlook for future skills needs by addressing the situation through the use of patents. The research assesses the usability of patent information for this type of analysis. While this study is limited to the information security sector by using Korean patent information, it can be expanded in the future to other areas and patents in the United States and Europe.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.797-799
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• 2000

Most neural network learning schemes are derived from learning systems which are generally iterative in nature. But, when the given input-output training vector pairs satisfy a PLI condition, the training and the application of a hard-limited neural network can be achieved non-iteratively with very short training time and very robust recognition when it is applied to recognize any untrained patterns. In this paper, a method of expanding the dimension of training pattern data is suggested. The proposed method demonstrates better performance and robustness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.204-210
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• 2011

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.481-492
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• 1997

This paper presents an algorithm for recognizing surface mount device(SMD) IC pattern based on the error back propoagation(EBP) neural network and discrete cosine transform(DCT). In this approach, we chose such parameters as frequency, angle, translation and amplitude for the shape informantion of SMD IC, which are calculated from the coefficient matrix of DCT. These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Learning of EBP neural network is carried out until maximum error of the output layer is less then 0.020 and consequently, after the learning of forty thousand times, the maximum error have got to this value. Experimental results show that the rate of recognition is 100% in case of the random pattern taken at a similar circumstance as well as normalized training pattern. It also show that proposed method is not only relatively relatively simple compare with the traditional space domain method in extracting the feature parameter but also able to re recognize the pattern's class, position, and existence.

• International Journal of Industrial Entomology and Biomaterials
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• v.8 no.1
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• pp.33-41
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• 2004

The type of trichomes, their density and pattern of distribution on leaves of 16 genotypes of mulberry, belonging to both diploid and polyploid categories, were studied by scanning electron microscope. The present investigation was undertaken to find out the relationship of physical attributes, especially the density and trichome types with higher acceptability and better rearing performance by the silkworm Bombyx-mori L. Two types of trichomes glandular and non-glandular types were observed on both the leaf surfaces of all the mulberry genotypes studied. In general, greater densities of trichomes were observed on the abaxial surface than the adaxial surface of leaves in most of the genotypes. Distribution of glandular trichomes were more in abaxial surface and non-glandular trichomes were more in adaxial surface. Overall, distribution of glandular and non-glandular trichomes per unit area of leaf did not follow any regular pattern. When leaves of those genotypes were fed to silkworms, trichome density was found to be significantly negatively correlated with the survival of larvae i.e., effective rate of rearing, but trichome density did not influence the economic characters of rearing. As the distribution of glandular trichomes (GT) and non-glandular trichomes (NGT) did not follow any definite pattern, no relation could be established between the GT and NGT densities with silkworm rearing performance. However, the ratio of GT and NGT in a particular genotype influenced the rearing parameters, higher the ratios better the rearing performance. High GT and NGT ratio (＞1.00) was found positively significant when correlated with economic parameters viz., larval weight, single cocoon weight and single shell weight. The study is useful in screening different mulberry genotypes for their better acceptability to silk-worm and higher rearing performance at the early stage of selection without actually conducting the rearing.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.636-646
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• 2007

Adequate postural control depends on the spatial and temporal integration of vestibular, visual, and somatosensory information. Especially, the musculoskeletal function is essential to maintain the postural control. The experimental studies was performed on the muscular activities in the lower extremities during maintaining and moving exercises on an unstable platform with Magneto Rheological(MR) dampers. The unstable platform of the developed system was controlled by electric currents to the MR dampers. A subject executed the maintaining and moving exercises which are presented through the display monitor. The electromyographies of the eight muscles in lower extremities were recorded and analyzed in the time and the frequency domain: the muscles of interest were rectus femoris(RF), biceps femoris(BF), tensor fasciae latae(TFL), vastus lateralis(VL), vastus medialis(VM), gastrocnemius(Ga), tibialis anterior(TA), Soleus(So). The experimental results showed that the muscular activities differed in the four moving exercises and the nine maintaining exercises. For the anterior-posterior pattern, the TA showed highest activities; for the left-right pattern, the TFL; for the 45, $-45^{\circ}$ pattern, the TFL and TA. Also, the rate of the increase in the muscular activities were affected by the condition of the unstable platform with MR dampers for the maintaining and moving exercises. The experimental results suggest that the choice of different maintaining and moving exercises could selectively train different muscles in various intensity. Futhermore, the findings suggested that the training using this system can improve the ability of postural control.

• PNF and Movement
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• v.14 no.2
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• pp.67-74
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• 2016

Purpose: The purpose of this study was to prove the effects of coordinative locomotion training (CLT) on walking speed, walking endurance, and balance for incomplete spinal cord injury patients. Methods: Ten subjects were randomly assigned to the CLT group (n = 5) and the treadmill (TM) group (n = 5). The CLT group performed PNF pattern exercise using the motions of the sprinter and skater for 30 minutes, while the TM group performed using a treadmill for 30 minutes. Both groups performed these therapeutic interventions for five days per week, for a period of four weeks. A 10 meter walking test, Berg Balance Scale (BBS), and 6 meter walking test were used for the assessment of gait speed, balance, and gait endurance. The SPSS Ver. 18.0 statistical program was used for data processing. A Wilcoxon signed rank test was used for the comparison of pre- and post-intervention performance and a Mann-Whitney test was used for comparison between the groups. The significance level for the statistical inspection was set at 0.05. Results: Both groups showed significant improvements in the 10 meter walking test, Berg Balance Scale, and 6 meter walking test (P < 0.05). Conclusion: CLT had an effect on the improvement of walking speed, walking endurance, and the balance of incomplete spinal cord injury patients. Thus, we suggest that CLT is a therapeutic intervention for incomplete spinal cord injury patients.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.173-180
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• 2002

The virtual cell loss rate was introduced for the training pattern of the neural network in the VOB(Virtual Output Buffer) model. The VOB model shows that the neural network can find the connection admission boundary without the real cell loss rate. But the VOB model tends to overestimate the cell loss rate, so the utilization of network is low. In this paper, we uses the reference curve of the cell loss rate, which contains the information about the cell loss rate at the connection admission boundary. We process the patterns of the virtual cell loss rate using the reference curve, We performed the simulation with two major ATM traffic classes. One is On-Off traffic class that has the traffic characteristic of LAN data and other is Auto-Regressive traffic class that has the traffic characteristic of a video image communication.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.1
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• pp.103-112
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• 2007

Recently structures become longer and higher because of the developments of new materials and construction techniques. However, such modern structures are susceptible to excessive structural vibrations, which may induce problems of serviceability and structural damages. In this paper we attempt to control structural vibration using the probabilistic neural network(PNN) and the artificial neural network(ANN) based on the training pattern that consist of only the structural state vector and the control force. The state vectors of the structure and control forces made by linear quadratic regulator(LQR) algorithm are used for training pattern of PNN and ANN. The proposed algorithm is applied for the vibration control of the three story shear building under Northridge earthquake. Control results by the proposed PNN and ANN are compared with each other.