• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2305-2314
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• 2023

The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an analytic solution. A Gaussian model is simple and enables rapid simulations, but it can be difficult to apply to situations with complex model parameters. Recently, a method of solving PDEs using artificial neural networks called physics-informed neural network (PINN) has been proposed. The PINN assumes the latent (hidden) solution of a PDE as an arbitrary neural network model and approximates the solution by optimizing the model. Unlike a Gaussian model, the PINN is intuitive in that it does not require special assumptions and uses the original equation without modifications. In this paper, we describe an approach to atmospheric dispersion modeling using the PINN and show its applicability through simple case studies. The results are compared with analytic and fundamental numerical methods to assess the accuracy and other features. The proposed PINN approximates the solution with reasonable accuracy. Considering that its procedure is divided into training and prediction steps, the PINN also offers the advantage of rapid simulations once the training is over.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2883-2902
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• 2023

Since the existing anti-jamming methods, including intelligent methods, have difficulty against high-speed reactive jamming, we studied a new methodology for jamming utilization instead of avoiding jamming. Different from the existing jamming utilization techniques that harvest energy from the jamming signal as a power supply, our proposed method can take the jamming signal as a favorable factor for frequency detection. Specifically, we design an intelligent differential frequency hopping communication framework (IDFH), which contains two stages of training and communication. We first adopt supervised learning to get the jamming rule during the training stage when the synchronizing sequence is sent. And then, we utilize the jamming rule to improve the frequency detection during the communication stage when the real payload is sent. Simulation results show that the proposed method successfully combated high-speed reactive jamming with different parameters. And the communication performance increases as the power of the jamming signal increase, hence the jamming signal can help users communicate in a low signal-to-noise ratio (SNR) environment.

• Physical Therapy Rehabilitation Science
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• v.2 no.2
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• pp.115-118
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• 2013

Objective: Whole body vibration training is a relatively new approach for enhancement of muscle strength, physical performance, and balance. The aim of this study was to assess the effect of unilateral whole body vibration training. Design: One group pretest-posttest design. Methods: Sixteen healthy, physically active volunteers participated in this study. Whole body vibration was applied with a frequency of 20 Hz and an amplitude of 3 mm for 3 minutes. Muscle performance and static balance were assessed before and after unilateral whole body vibration training. One leg standing broad jump test was measured to determine muscle performance which is closely linked to lower extremity muscle function. The good balance system was used in evaluation static balance. All test were measured 3 times and the average value was analyzed. Results: Jumping length was significantly improved by 0.11m in all participants after intervention (p<0.05). Among static parameters, significant results were observed where in the eyes opened condition, X-speed (medial-lateral sway) changed from 4.20 mm/s to 4.95 mm/s, Y-speed (anterior-posterior sway) changed from 5.77 mm/s to 6.54 mm/s and velocity moment changed from $12.77mm^2/s$ to $13.57mm^2/s$ (p<0.05). In the eyes closed condition, X-speed changed from 4.34 mm/s to 4.85 mm/s, Y-speed changed from 7.84 mm/s to 8.16 mm/s and velocity moment changed from $16.03mm^2/s$ to $16.11mm^2/s$ (p<0.05). Conclusions: Immediate unilateral whole body vibration improved muscle performance but impaired static balance in young adults.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.28-38
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• 2010

In order to remove the noise contained in recorded analog video, it is important to recognize the real characteristics and strength of the noise. This paper presents an efficient training-based noise reduction method for recorded analog video after analyzing the noise characteristics of analog video captured in a real broadcasting system. First we show that there is non-negligible noise correlation in recorded analog video and describe the limitations of the traditional noise estimation and reduction methods based on additive white Gaussian noise (AWGN) model. In addition, we show that auto-regressive (AR) model considering noise correlation can be successfully utilized to estimate and synthesize the noise contained in the recorded analog video, and the estimated AR parameters are utilized in the training-based noise reduction scheme to reduce the video noise. Experiment results show that the proposed method can be efficiently applied for noise reduction of recorded analog video with non-negligible noise correlation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.2878-2885
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• 2012

In this study, the training and sporting equipment for leg rehabilitation featuring the MR fluids is proposed. The compact MR fluid brake is designed and manufactured to apply to the rehabilitation training and sporting mechanism. The resistance characteristic of the MR fluid brake is controllable by varying the magnetic field around the fluid. Under consideration of spatial limitation, design parameters which are related with the magnetic strength are determined to maximize to a torque using finite element method. The FE analysis is performed using a commercial code, ANSYS Workbench. The proposed brake device is manufactured, and its field-dependant torque is experimentally evaluated. When the electric current is supplied, the torque of the MR fluid brake is increased and the response is very fast. Depending on the strength of the current supply, torques of the MR fluid brake also increase similar to Bingham property of MR fluid.

• Nutritional Sciences
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• v.8 no.3
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• pp.189-195
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• 2005

To assess the effect of pinitol supplementation and strength training for two weeks on the anaerobic capacity during and after exercise, and improvement of glucose metabolism during the recovery period of muscular fatigue with repeated acute bouts of cycling exercise, a total of 24 healthy young men were recruited and randomly and equally divided into three groups; pinitol supplementation group (PSG), placebo group (PLG), and control group (CON). Using a randomized double-blinded design, subjects in PSG were provided pinitol supplement, consumed orally 1.2 g/day, and participated in the resistance exercise program and cycling exercise for two weeks. Subjects in PLG underwent the same protocol as those in PSG but consumed the same amount of placebo. No supplementation and exercise program was given to CON. Before and after the intervention, all subjects were tested for their anaerobic capacities evaluated by Wingate test twice separated by 30 min. During the test, peak anaerobic power (PP), mean anaerobic power, total work, and fatigue index were evaluated During resting and recovery, blood samples were drawn and plasma pinitol, myo-inositol, chiro-inositol, insulin, free fatty acid, glucose, and lactate levels were analyzed After two weeks, PP and relative PP of the second biking were improved from the first biking in PSG only (p<0.05). No changes were found in all other variables of Wingate test in all groups. No statistical differences between groups and pre- and post-intervention were observed in concentrations of pinitol, myo-inositol, and chiro-inositol, but pinitol concentration was higher during recovery compared to the baseline in all groups and testings (p<0.05). Lactate level during recovery was higher than the resting level, but no other blood parameters were significantly changed. In conclusion, two weeks of pinitol supplementation in conjunction with short duration of anaerobic training in healthy young men did not induce any obvious benefits in terms of anaerobic capacity and energy metabolism Individual and/or population susceptibility may be one factor responsible for adopting pinitol supplementation.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5301-5310
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• 2013

The biological mechanisms of cancer and associations with behavior of tumours need to be studied to understand progression and determine appropriate treatments. Here we investigated expression of VEGF, MMP-9 and E-cadherin in laryngeal SCCs and their relations with clinical behavior. This prospective study was based on 38 surgical specimens from patients with primary laryngeal SCC and data recorded in their cards. Expression of the three factors in tumor tissue was examined using immunohistochemistry and correlations with clinical parameters of primary tumors, regional lymph node metastases, stage of disease, histopathologic differentiation, and vascular/cartilage invasion were investigated. Regarding the cases with positive MMP-9 expression, the difference between well and moderately/poorly differentiated tumors was statistically significant. However, differences between early stage (stage I and II) and late-stage (stage III and IV) tumours, and between positive and negative for pLN metastasis were not. No significant relationship between positive VEGF and tumor differentiation or stage was apparent, but E-cadherin levels significantly differed between well and moderately/poorly differentiated tumours and with the presence of pLN metastasis. E-cadherin staining did not vary between MMP-9 positive and negative cases. In conclusion, MMP-9 may be a negative predictor of differentiation in laryngeal SCC, while E-cadherin is a predictor of differentiation and nodal metastases. Even if the difference between VEGF expression and tumor stage was not statistically significant, it seems that there exists some relationship, which might be clarified with a greater number of cases.

• The Journal of Korean Physical Therapy
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• v.22 no.6
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• pp.65-70
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• 2010

Purpose: Exercise has been shown to be a simple and economical therapeutic modality that may be considered as an effective aid for diabetic mellitus. For example, exercise training increases insulin sensitivity in type 2 diabetes. But we found no reported of how exercise affect type 1 diabetes. This study investigated the impact of aerobic and graduated treadmill exercise regimens on body weight, glucose and insulin concentrations, lipid profiles, and oxidative stress indicators in rats with streptozotocin (STZ) induced diabetes. Glycosylated hemoglobin ($HbA_{1c}$) was determined as an indicator of glucose control during exercise. Methods: In our study, a total of 40 rats were used. Three groups of 10 rats each were given STZ to induce diabetes. The remaining 10 rats became the normal group. After 28 days we determined biochemical parameters such as glucose, glycosylated hemoglobin ($HbA_{1c}$), insulin concentration, serum total cholesterol (TC), triglycerides (TG), and high-density lipoprotein (HDL). Superoxide dismutase (SOD) and catalase activities were also measured. Results: Concentrations of blood glucose and $HbA_{1c}$ in the moderated exercise groups were significantly decreased after 28 days compared with the control group (p<0.05). There was a significant reduction in serum TC and TG in the experimental groups. The activity of SOD increased significantly by 17.70% and 48.25% respectively. Conclusion: These results indicate that physical training and exercise training affects body weight, fasting blood glucose, $HbA_{1c}$, insulin, lipid profiles, and antioxidant status in rats with streptozotocin-induced diabetes. We suggest that graduated treadmill exercise may have therapeutic, preventative, and protective effects against diabetes mellitusby improving glycemic control, oxidant defenses, and lipid metabolism.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.425-430
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• 2015

This paper is a study to improve the classification efficiency of rotating objects by using deep neural networks to which a deep learning algorithm was applied. For the classification experiment of rotating objects, COIL-20 is used as data and total 3 types of classifiers are compared and analyzed. 3 types of classifiers used in the study include PCA classifier to derive a feature value while reducing the dimension of data by using Principal Component Analysis and classify by using euclidean distance, MLP classifier of the way of reducing the error energy by using error back-propagation algorithm and finally, deep learning applied DBN classifier of the way of increasing the probability of observing learning data through pre-training and reducing the error energy through fine-tuning. In order to identify the structure-specific error rate of the deep neural networks, the experiment is carried out while changing the number of hidden layers and number of hidden neurons. The classifier using DBN showed the lowest error rate. Its structure of deep neural networks with 2 hidden layers showed a high recognition rate by moving parameters to a location helpful for recognition.