• International Journal of Industrial Entomology and Biomaterials
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• v.7 no.2
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• pp.191-195
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• 2003

The impact of antibiotic (Norfloxacin) administration ,at different concentration (50 ppm and 100 ppm) on commercial characters of bivoltine $({CSR_2}{\times}{CR_4})$ and the cross breed race $({BL_67}{\times}{CSR_101})$ was studied. The result indicated that antibiotic administration with different concentrations significantly improved the rearing and cocoon parameters like larval duration, larval weight, growth index, single cocoon weight, single shell weight and shell ratio. The post cocoon parameters like average filament length, non-breakable filament length, raw silk percentage, raw silk recovery percentage, denier, reelability and neatness were recorded significantly higher in antibiotic treated batches. The better performances of these parameters were recorded with the increase of antibiotic concentration.

• Journal of the Korean Physical Society
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• v.73 no.10
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• pp.1486-1494
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• 2018

Based on the semiclassical theory of chaotic scattering, Tanner et al. [J. Phys. B 40, F157 (2007)] proposed the fluctuation in the partial photoionization cross section of helium below the double-ionization threshold would show the same characteristics as in the total cross section, predicting that the Fourier spectrum of the fluctuation reveals peaks at the classical actions of closed triple collision orbits and the amplitude of the fluctuation decreases algebraically as the energy approaches the double-ionization threshold. In that paper, however, the predictions were not clearly confirmed due to the lack of experimental data with sufficient accuracy. So instead, we calculate the partial photoionization cross sections of collinear eZe helium for the energy range from the single-ionization threshold $I_{20}$ to $I_{32}$ in order to numerically confirm the predictions. Analysis of the fluctuation in the partial cross section shows that the predictions are indeed valid. Our findings mean that the fluctuation in the partial photoionization cross section can be described by classical triple collision orbits in the semiclassical limit. Thus it explains in a natural way the mirroring and mimicking structures observed in cross section signals for different ionization channels.

• Journal of the Korean Society of Physical Medicine
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• v.19 no.1
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• pp.1-10
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• 2024

PURPOSE: This pre-post intervention study aimed to examine the effects of a 12-week supervised modified Taekwondo exercise program on the functional fitness of community-dwelling older adults. METHODS: 10 participants (mean age: 72.3 ± 4.6 years) completed the program over a 12-week period. The intervention consisted of Taekwondo-based exercises modified for older persons. Changes to functional fitness were measured using the finger-to-nose test, functional reach test, timed up and go test, 30-second sit-to-stand test, 30-second arm curl test, Chester step test, chair sit-and-reach test, and back scratch test. Participants were assessed prior to the training and after the final training session, changes were measured using descriptive statistics and paired sample 't' tests. RESULTS: Effect sizes ranged from small to large (Cohen's d = .22 to 1.23). The exercise program was well- tolerated by participants, with a high level of engagement and no attrition for the duration of the program. Results showed significant improvements in most measures of functional fitness (p < .05) except for the back scratch test (p = .051). CONCLUSION: These findings suggest a well-designed, supervised, modified Taekwondo exercise program can significantly improve functional fitness in older adults.

• The Journal of Korean Physical Therapy
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• v.27 no.6
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• pp.430-433
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• 2015

Purpose: The purpose of this study was to investigate the effects of a four week unilateral isokinetic exercise program applied to ankle on the one-leg stance balance performance of ipsilateral and contralateral lower-limbs. Methods: Subjects were randomly assigned to either a right ankle training program (n=12) or a control group (n=12). The training group received unilateral ankle isokinetic exercise of the dominant side for 4 weeks, whereas control group did not. Ipsilateral and contralateral one-leg balance were measured before and after intervention using the Biodex Balance System. Results: Improvements of stability scores, such as APSI, MLSI, and OSI, from pre-test to post-test were significantly different greater for the training group when the control. Conclusion: The results of this study suggest unilateral ankle strengthening exercise transfers benefit to the untrained limb by a cross-education effect, and that this type of exercise should be considered to improve one-leg standing balance of trained and untrained lower-limbs.

• The Journal of Korean Physical Therapy
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• v.20 no.4
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• pp.15-20
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• 2008

Purpose: We investigated the effects of physical practice, mental practice, and cross education using serial reaction time (SRT). We recruited 21 right-handed healthy males and females who gave consent and had no clinical history for their upper limbs. Methods: The subjects were divided into three groups; actual practice (n=7), mental practice (n=7), and controls (n=7), who performed actual training, mental training, or no intervention respectively for three weeks. Super lab 4.0 displayed four symbols on the monitor and subjects pushed on the matching button, with reaction time assessed pre- and post-intervention. Results: Reaction time was significantly lower after actual or mental practice (p<0.05). Actual practice also decreased left hand reaction time. Conclusion: Actual practice and mental practice can improve motor learning, but mental practice is not sufficient for cross education.

• Korean Journal of Adult Nursing
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• v.25 no.2
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• pp.170-182
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• 2013

Purpose: This study investigated the effectiveness of simulation training with a hybrid model of student nurses' performance ability and reported self confidence. Methods: A nonequivalent control group with pre-posttest was designed. Data collection was done during the first semester in 2012 at a college of nursing in Seoul. Nursing performance ability and reported self confidence related to taking care of patients with urinary problems were evaluated. The treatment group (n=96) received simulation training of a catheterization procedure with a hybrid model involving standardized patients and a mannequin. Nursing students in the comparison group (n=84) did not receive the simulation training but would receive it prior to their next clinical practicum's. Results: The treatment group showed a significantly higher performance ability and reported self confidence than that of the comparison group. The perceived helpfulness and contentment of the simulation training in experimental group was high. Conclusion: The findings of this study demonstrated that simulation with a hybrid model was effective in teaching skills prior to the clinical experience which suggests that skill development is not dependent on the actual clinical situation. Nurse educators should consider simulation training as a tool beyond that of clinical practicum.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.2
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• pp.7-13
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• 2022

As the demand for simulated flight training and interest in new technology training increase, this study analyzed the performance of flight simulator training and actual flight training subjects to confirm efficient flight simulator training curriculum. Summarizing the results of the study, found that flight simulator training had a significant positive effect on the actual flight training performance and in particular had a relatively large effect on the air maneuver, traffic pattern, cross country flight subjects. As a result of analyzing theoretical major classes that affect flight simulator training to verify the correlation, found that principle of air navigation, air traffic service, and helicopter flight theory were affected in order. The significance of this study was to identify the curriculum and ground lesson that should be focused on effectively performing flight simulator training in the helicopter private pilot course.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• ETRI Journal
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• v.17 no.1
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.