• Physical Therapy Korea
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• v.26 no.3
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• pp.11-22
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• 2019

Background: Sling exercises are frequently used for the rehabilitation process of patients with shoulder joint injuries, but research on the significant frequency intensity and appropriate treatment duration for sling exercises with local vibration stimulation is lacking. Objects: The aim of this study was to investigate the effects of sling exercise with vibration on shoulder range of motion (ROM), muscle strength, pain, and dysfunction in patients with a medical diagnosis of shoulder joint injury. Methods: Twenty-two patients were randomly assigned to the experiment and control groups. Six sling exercises with and without 50 Hz vibrations were applied in the experiment and control groups, respectively. Each exercise consisted of 3 sets of 5 repetitions performed for 6 weeks. The assessment tools used included shoulder joint range of motion, muscle strength, pain level, and shoulder pain and disability index for functional disability. We conducted re-evaluations before and 3 and 6 weeks after intervention. The changes in the measurement variables were analyzed and compared between the two groups. Results: The ROM of the external rotation of the shoulder joint had a significant interaction between the group and the measurement point (F=3.652, p<.05). In both groups, we found a significant increase in external rotation angle between the measurement points (p<.05). The flexor strength of the shoulder joint significant interaction between the group and the measurement point (F=4.247, p<.05). Both the experiment (p<.01) and control groups (p<.05) showed a significant increase in shoulder flexor strength at the measurement points. After 6 weeks of the interventions, both the groups showed significantly improved VAS (p<.01), SPADI (p<.01), and orthopedic tests (p<.01). However, there was no significant difference between the group and the measurement point in terms of the clinical outcomes observed. Conclusion: The sling exercise with local vibration of 50 Hz affected the external rotation of the shoulder range of motion and improved shoulder flexor strength in the patients with shoulder injuries. Therefore, we propose the use of the sling exercise intervention with vibration in the exercise rehabilitation of patients with shoulder joint injuries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.733-735
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• 2014

Example based Super Resolution(SR) is using the correspondence between the low and high resolution image from a database. This method uses only one image to estimate a high resolution image and can get the larger image than 2 times. Example based SR is proposed to solve the problem of classical SR. Neighbor embedding(NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the poor generalization of NE decreases the performance of such algorithm. The sizes of local training sets are always too small to improve the performance of NE. We propose the advanced NE baesd on SVR having an excellent generalization ability to solve this problem. Given a low resolution image, we estimate a pixel in its high resolution version by using SVR based NE. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed algorithm when comparing with conventional interpolation methods and NE.

• The korean journal of orthodontics
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• v.52 no.4
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• pp.287-297
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• 2022

Objective: To investigate the pattern of accuracy change in artificial intelligence-assisted landmark identification (LI) using a convolutional neural network (CNN) algorithm in serial lateral cephalograms (Lat-cephs) of Class III (C-III) patients who underwent two-jaw orthognathic surgery. Methods: A total of 3,188 Lat-cephs of C-III patients were allocated into the training and validation sets (3,004 Lat-cephs of 751 patients) and test set (184 Lat-cephs of 46 patients; subdivided into the genioplasty and non-genioplasty groups, n = 23 per group) for LI. Each C-III patient in the test set had four Lat-cephs: initial (T0), pre-surgery (T1, presence of orthodontic brackets [OBs]), post-surgery (T2, presence of OBs and surgical plates and screws [S-PS]), and debonding (T3, presence of S-PS and fixed retainers [FR]). After mean errors of 20 landmarks between human gold standard and the CNN model were calculated, statistical analysis was performed. Results: The total mean error was 1.17 mm without significant difference among the four time-points (T0, 1.20 mm; T1, 1.14 mm; T2, 1.18 mm; T3, 1.15 mm). In comparison of two time-points ([T0, T1] vs. [T2, T3]), ANS, A point, and B point showed an increase in error (p < 0.01, 0.05, 0.01, respectively), while Mx6D and Md6D showeda decrease in error (all p < 0.01). No difference in errors existed at B point, Pogonion, Menton, Md1C, and Md1R between the genioplasty and non-genioplasty groups. Conclusions: The CNN model can be used for LI in serial Lat-cephs despite the presence of OB, S-PS, FR, genioplasty, and bone remodeling.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.397-405
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• 2020

The purpose of study was to compare respiratory function and quadriceps muscle activity in stroke patients by applying inspiratory muscle training combined with whole body vibration. In addition, the purpose of study is to present an exercise method for improving the respiratory function of stroke patients and the function of the lower limb muscles of stroke patients. Totally, 21 patients with Stroke patients were randomly assigned to two groups through clinical sampling. 11 patients who applied whole body vibration combined with respiratory exercise were randomly assigned to Experiment Group I, and 10 patients who applied placebo exercise combined with breathing exercise were randomly assigned to Experiment Group II. And for 5 weeks, 4 days/week, 1 time/day, 4 sets/1 time intervention program was implemented. Before intervention, the respiratory function was measured with a maximum inspiratory pressure meter, the lower extremity muscle activity was measured using the surface EMG, and the balance ability was measured using a bug balance test. And after 5 weeks, the post-test was re-measured and analyzed in the same way as the pre-test. In the comparison of changes within the group of experimental group I, there were significant differences in the activity and balance of the respiratory muscle strength, the biceps femoris, and the anterior tibialis muscle (p<.05). In the comparison of the changes in the experimental group I, there was a significant difference in respiratory strength and balance (p<.05). In the comparison of changes between groups, there was a significant difference in the activity of the biceps femoris and anterior tibialis (p<.01). In the future, research on protocols for respiratory exercise and whole body vibration to improve neuromuscular function is considered to be necessary.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.4
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• pp.175-185
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• 2022

Purpose : This study was conducted to determine the effect of Kegel exercise using a pressure biofeedback unit (PBU) for 2 weeks on maximum voluntary ventilation (MVV) and abdominal muscle thickness based on previous studies. Methods : The subjects of this study were 20 healthy female students in their 20s. Subjects were randomly assigned to two groups. Eleven subjects were assigned to the experimental group (EG) and 9 subjects were assigned to the control group (CG). Subjects measured MVV with a spirometer. In hooklying position, transverse abdominis (TrA), internal oblique (IO), and external oblique (EO) of the dominant side were measured using ultrasound. For the measurement value, the average value of three times was adopted. After 2 weeks of intervention, the measurements were measured in the same way. In the EG, pelvic setting training using PBU was performed before Kegel exercise. The PBU was first placed at the waist in the Kegel exercise position and the starting pressure was set at 40 mmHg and adjusted to 60 mmHg through pelvic floor muscle contraction. After performing pelvic control using PBU, Kegel exercise was performed with 8 seconds of contraction, 8 seconds of relaxation, and 3 sets of 10 reps per set. A significance level of 𝛼=.05 was used to verify statistical significance. Results : In the variable of MVV, a significant increase was confirmed in the EG (p<.05). In the abdominal muscle thickness variable, significant increases were confirmed in IO and TrA in the EG (p<.05). In addition, a significant increase in IO was confirmed in the CG (p<.05). Significant increases in IO and TrA were confirmed between groups (p<.05). Conclusion : Based on the previous study, this study confirmed that Kegel exercise using a PBU had a positive effect on MVV and abdominal muscle thickness based on a 2-week intervention.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.255-260
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• 2022

This study aimed to develop a service model that uses a deep learning algorithm for detecting diseases and pests in strawberries through image data. In addition, the pest detection performance of deep learning models was further improved by proposing segmented image data sets specialized in disease and pest symptoms. The CNN-based YOLO deep learning model was selected to enhance the existing R-CNN-based model's slow learning speed and inference speed. A general image data set and a proposed segmented image dataset was prepared to train the pest and disease detection model. When the deep learning model was trained with the general training data set, the pest detection rate was 81.35%, and the pest detection reliability was 73.35%. On the other hand, when the deep learning model was trained with the segmented image dataset, the pest detection rate increased to 91.93%, and detection reliability was increased to 83.41%. This study concludes with the possibility of improving the performance of the deep learning model by using a segmented image dataset instead of a general image dataset.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.293-301
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• 2023

Unstandardized medical data collection and management are still being conducted manually, and studies are being conducted to classify CT data using deep learning to solve this problem. However, most studies are developing models based only on the axial plane, which is a basic CT slice. Because CT images depict only human structures unlike general images, reconstructing CT scans alone can provide richer physical features. This study seeks to find ways to achieve higher performance through various methods of converting CT scan to 2D as well as axial planes. The training used 1042 CT scans from five body parts and collected 179 test sets and 448 with external datasets for model evaluation. To develop a deep learning model, we used InceptionResNetV2 pre-trained with ImageNet as a backbone and re-trained the entire layer of the model. As a result of the experiment, the reconstruction data model achieved 99.33% in body part classification, 1.12% higher than the axial model, and the axial model was higher only in brain and neck in contrast classification. In conclusion, it was possible to achieve more accurate performance when learning with data that shows better anatomical features than when trained with axial slice alone.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• 한국체육학회지인문사회과학편
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• v.54 no.5
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• pp.769-780
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• 2015

This study investigated the changes in inflammatory mediators, immunocompetent cells and bone merrow progenitor cells by the magnitude of muscle damage and type of the muscle contraction in the elderly. Twenty older adults who had not been involved in a resistance-training program at least 6 months prior to the present study were assigned to eccentric exercise group (ECC, n=10) and concentric exercise group (CON, n=10). All subjects performed 10 sets of 6 maximal isokinetic eccentric (ECC 1) or concentric (CON) contractions with the non-dominant arm in a randomized, with 4 wk between bouts (ECC 2). Skeletal muscle damage index (ROM, VAS, Plasma CK), inflammation mediators (TNF-α, IL-1, IL-6), immunocomperent cells (CD3+, CD4+, CD8+, CD19+), bone merrow progenitor cell (CD34+) and leukocytes were measured before, immediately after, 2, 24, 48, 72, and 96 h after exercise. Changes in ROM and VAS were greater (P<.05) after ECC 1 than CON and ECC 2. Increases in TNF-α and IL-6 were greater (P<.05) 24, 48 and 72 h after ECC 1 than CON and ECC 2. Increases in neutrophils were greater (P<.05) 2 h after ECC 1 than CON and ECC 2. It was confirmed that muscle damage was greater following eccentric than concentric contractions as well as first bout than second bout in the elderly, and suggested that TNF-α, IL-6 and neutrophils should closely correlate with magnitude of muscle damage.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.