• The Journal of Korean society of community based occupational therapy
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• v.3 no.2
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• pp.57-65
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• 2013

Objective : The purpose of this study was to identify the effect and difference of abdominal muscle strengthening exercise, back muscle stretching and multi-training on the lumbar flexibility of 20s adults. Method : The abdominal muscle strengthening exercise, back muscle stretching and multi-training were conducted 9 times targeting 21 subjects who attended K University from 2013 May 29 to June 14. Sit and Reach Tests were conducted 2 times(before and after exercise program) for flexibility test and measured data were processed with SPSS program WIN 12.0K. By the Wilcoxon signed rank test, the effectiveness of exercises are verified. By the Kruskal-Wallis test and Mann-Whitney test, the difference of effectiveness among the exercise groups are verified. Result : The results of this study were summerized below 1. Before and after exercise, in abdominal muscle strengthening exercise group, back muscle stretching group and multi-training group are showed statistically significant differences(p<.05), 2. There were statistically significant difference in the improvement of the flexibility between each group(p>.05). Conclusion : These data suggests that all of the 3 exercise programs are brought positive influences on the improvement of flexibility, and abdominal muscle strengthening exercises and flexibility multi-training are effective on the flexibility more than, back muscle stretching in 20s adults.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.3
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• pp.10-18
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• 2022

A survey was conducted on the awareness and demand for the Multi-crew Pilot License (MPL) to prepare the legal institutional basis for the MPL certification system. A total of 288 airmen were asked questions about the awareness and demand of the MPL certification system, and factors affecting the establishment and participation of MPL training programs. The survey results show the understanding of the MPL certification system is significantly lower than that of the current pilot certification system. The demand for the MPL training program was found to be significant, trainees and low-skilled airmen was greater demand. The factor that has the greatest influence on the establishment and participation of the MPL training program was identified as employment connection (airline recruiting). It is expected that the result of this study will be used as basic data necessary for establishing MPL certification system policy.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.30-35
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• 2011

2D pollutant transport model was applied to the simulation of contaminant transport in the channel. At first, two kinds of virtual channels having different slopes were designed. The distribution of contaminant, which flows from one of the three drainages to the main channel, was simulated by each 2D model. Concentrations of 745 nodes were converted to input data of neural network model (Multi-perceptron) for training and verification using matrix. The first three cases (Case A-1, A-2, A-3) were used for training Multi-perceptron, the other three cases (Case B-1, B-2, B-3) were used for verification. As a result, Multi-perceptron reasonably divided the cases into the three characteristics which have different contaminant distributions due to the different input point of water pollution source. It can be a useful methodology for the water quality monitoring and backtracking.

• Speech Sciences
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• v.15 no.2
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• pp.55-65
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• 2008

Recently, the multi-model based speech recognizer has been used quite successfully for noisy speech recognition. For the selection of the reference HMM (hidden Markov model) which best matches the noise type and SNR (signal to noise ratio) of the input testing speech, the estimation of the SNR value using the VAD (voice activity detection) algorithm and the classification of the noise type based on the GMM (Gaussian mixture model) have been done separately in the multi-model framework. As the SNR estimation process is vulnerable to errors, we propose an efficient method which can classify simultaneously the SNR values and noise types. The KL (Kullback-Leibler) distance between the single Gaussian distributions for the noise signal during the training and testing is utilized for the classification. The recognition experiments have been done on the Aurora 2 database showing the usefulness of the model compensation method in the multi-model based speech recognizer. We could also see that further performance improvement was achievable by combining the probability density function of the MCT (multi-condition training) with that of the reference HMM compensated by the D-JA (data-driven Jacobian adaptation) in the multi-model based speech recognizer.

• Journal of Home Health Care Nursing
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• v.23 no.1
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• pp.5-15
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• 2016

Purpose: This study was conducted to evaluate health service delivery and attitudes, toward multi-cultural clients amongst community health practitioners (CHPs). Methods: A survey was conducted among 242 CHPs from December 10-22, 2015. The collected data were analyzed using chi-square test, t-test, and ANOVA using SPSS 18.0. Results: General awareness of multi-culturalism varied significantly by CHPs age and language ability. Additionally, utilization of services in accordance with the location of community health centers (CHCs) was significantly higher in rural areas than urban areas CHCs in post-partum maternal & neonate care giver service (in maternal child health), management of health educational programs and management of physical exercise (in implementing healthy life style) and networking resources in & outside of CHCs (in management of chronic disease). Conclusion: CHPs deliver health-care services to multi-cultural clients, but have not received sufficient training or education to serve these clients effectively. CHPs who received multi-cultural and foreign language training had more positive experiences with multi-cultural clients. This supports the needs for developing educational programs to enhance multi-cultural understanding amongst CHPs.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.117-126
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• 2007

In this paper, we propose an early rehabilitation training system for the improvement of postural balance with multi-modality on a tilting bed. The integration of the visual, somatosensory and vestibular functions is significant to for maintaining the postural control of the human body. However, conventional rehabilitation systems do not provide multi-modality to trainees. We analyzed the characterization of postural control at different tilt angles of an early rehabilitation training system, which consists of a tilting bed, a visual feedback, a computer interface, a computer, and a force plate. The software that we developed for the system consists of the training programs and the analysis programs. To evaluate the characterization of postural control, we conducted the first evaluation before the beginning of the training. In the following four weeks, 12 healthy young and 5 healthy elderly subjects were trained to improve postural control using the training programs with the tilting bed. After four weeks of training, we conducted the second evaluation. The analysis programs assess (center of pressure) COP moving time, COP maintaining time, and mean absolute deviation of the trace before and after training at different tilt angles on the bed. After 4 weeks, the COP moving time was reduced, the COP maintaining time was lengthened, and the mean absolute deviation of the trace was lowered through the repeated use of vertical, horizontal, dynamic circle movement training programs. These results show that this system improves postural balance and could be applied to clinical use as an effective training system.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.934-940
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• 2014

Recently, the emergence of a variety of devices to be able to access web have brought many technological changes in the field of web development. However some web sites are still operating with a focus on computers, especially exclusive and slow developing job training related web sites which utilize a User Interface that targets only PCs. In this paper, we propose the job training management system which has a menu configuration and functions to be applied to a variety of devices, and also an applicable technologies for training students to access more easily. Finally, we evaluated its efficiency by applying the implemented system in the actual field.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.75-86
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• 2022

The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.