• Journal of Energy Engineering
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• v.24 no.4
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• pp.183-191
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• 2015

Studies on the environment-friendly and permanent low energy saving measure are and will continue to be an eternal challenge. However, the demand is high for the technologies that can save energy significantly in everyday life that produce tangible benefits for the users by applying saving factors and that anyone can access easily when it comes the related procedure. Government policies related to the improvement of energy effect in the existing building structure are characterized by complex procedure. Moreover, cost required and reliability issue emerge when request is made to an expert. Accordingly, this study seeks to present energy improvement plan that can be utilized simply and conveniently at any place, any time by enabling customized design according to individual taste by enabling energy change amount and grade prediction when the users select only the part that they want to replace by using a simple program.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.1-7
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• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• Journal of Embryo Transfer
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• v.25 no.4
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• pp.263-266
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• 2010

Prediction of semen's fertilizing ability used in artificial insemination (AI) is one of very important factors on pig reproductive performance. In vitro fertilization (IVF) has been used for indirect evaluation of sperm's fertilizing ability and it has been showed as highly correlated index. In swine industry, increasing interest in preservation of boar semen raises questions on the sperm motility from semen used in commercial AI centers. Mitochondria in sperm mid-piece generate the energy to support motility and could be an explanation of impaired fertility. Objective of this study was to suggest usable sperm motility to farms in measuring the effect of sperm motility and sperm abnormality on in vitro production of embryo in which sperm's fertilizing ability can be determined indirectly. Semen samples were provided from local AI center and used within 3 days after collection. Semen samples were divided by 4 different motile groups (>70%; 61~70%; 51~60%; <50%) using CASA (computer-assisted sperm analysis) on the days of IVF. Developmental rate to the blastocyst stage from over 61% motile sperm group showed significantly higher rate than below 60% motile sperm group ($16.5{\pm}0.7{\sim}18.4{\pm}0.8%$ vs $6.3{\pm}0.8{\sim}11.5{\pm}0.7%$, p<0.05). In experiment to determine the relationship between sperm motility and viability and abnormality, over 61% motile sperm groups showed significantly higher viability rate compared to below 60% motile sperm groups ($84.8{\pm}4.0{\sim}88.1{\pm}4.0%$ vs $69.1{\pm}4.0{\sim}74.2{\pm}4.0%$, p<0.05). On the other hand, morphological sperm abnormality showed significantly higher in over 70% motile sperm group ($10.2{\pm}2.2$ vs $16.0{\pm}2.2{\sim}21.0{\pm}2.2%$, p<0.05). In experiment to find the correlation between sperm motility of 4 different motile groups and amount of mitochondria, lower motility group also showed lower level of mitochondria (p<0.05). The mitochondria parameter used in this study showed another possibility to differentiate the sperm motility. Taken together, because below 60% motile semen used in AI reduce the fertility, AI centers should provide the over 60% motile sperm to the farms at the time of AI.

• Clinical and Experimental Reproductive Medicine
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• v.43 no.2
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• pp.112-118
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• 2016

Objective: Ovarian reserve tests are commonly used to predict ovarian response in infertile patients undergoing ovarian stimulation. Although serum markers such as basal follicle-stimulating hormone (FSH) or random $anti-M{\ddot{u}}llerian$ hormone (AMH) level and ultrasonographic markers (antral follicle count, AFC) are good predictors, no single test has proven to be the best predictor. In this study, we developed appropriate equations and novel nomograms to predict the number of oocytes that will be retrieved using patients' age, serum levels of basal FSH and AMH, and AFC. Methods: We analyzed a database containing clinical and laboratory information of 141 stimulated in vitro fertilization (IVF) cycles performed at a university-based hospital between September 2009 and December 2013. We used generalized linear models for prediction of the number of oocytes. Results: Age, basal serum FSH level, serum AMH level, and AFC were significantly related to the number of oocytes retrieved according to the univariate and multivariate analyses. The equations that predicted the number of oocytes retrieved (log scale) were as follows: model (1) $3.21-0.036{\times}(age)+0.089{\times}(AMH)$, model (2) $3.422-0.03{\times}(age)-0.049{\times}(FSH)+0.08{\times}(AMH)$, model (3) $2.32-0.017{\times}(age)+0.039{\times}(AMH)+0.03{\times}(AFC)$, model (4) $2.584-0.015{\times}(age)-0.035{\times}(FSH)+0.038{\times}(AMH)+0.026{\times}(AFC)$. model 4 showed the best performance. On the basis of these variables, we developed nomograms to predict the number of oocytes that can be retrieved. Conclusion: Our nomograms helped predict the number of oocytes retrieved in stimulated IVF cycles.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.7
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• pp.782-790
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• 2016

When a fire occurs on a ship that has mechanical ventilation facilities, the air supply and exhaust systems directly effect smoke diffusion. And there is a high possibility that occupant's visibility will be harmed because of smoke. In this study, the effects and risks of air supply and exhaust systems with regard to smoke diffusion given a shipboard fire analyzed with a Fire Dynamic Simulator(FDS). Suggested measures are also provided for using air supply and exhaust systems more efficiently. The results showed that, when air supply and exhaust systems were both working at the time of a fire, rather than stopping these systems as previously encouraged, continuing to operate both was an effective measure to gain evacuation time. When a fire occurred and the exhaust system was operating, also starting the air supply system near the origin of the fire was another effective approach to gain evacuation time. However, when only the air supply system was operating and a fire occurred, the air supply system accelerated smoke diffusion, so it was necessary to stop the air supply system to detect smoke diffusion as much as possible.

• Current Optics and Photonics
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• v.1 no.2
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• pp.107-112
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• 2017

An adaptive optics system can be simulated or analyzed to predict its closed-loop performance. However, this type of prediction based on various assumptions can occasionally produce outcomes which are far from actual experience. Thus, every adaptive optics system is desired to be tested in a closed loop on an optical test bench before its application to a telescope. In the close-loop test bench, we need an atmospheric simulator that simulates atmospheric disturbances, mostly in phase, in terms of spatial and temporal behavior. We report the development of an atmospheric turbulence simulator consisting of two point sources, a commercially available deformable mirror with a $12{\times}12$ actuator array, and two random phase plates. The simulator generates an atmospherically distorted single or binary star with varying stellar magnitudes and angular separations. We conduct a simulation of a binary star by optically combining two point sources mounted on independent precision stages. The light intensity of each source (an LED with a pin hole) is adjustable to the corresponding stellar magnitude, while its angular separation is precisely adjusted by moving the corresponding stage. First, the atmospheric phase disturbance at a single instance, i.e., a phase screen, is generated via a computer simulation based on the thin-layer Kolmogorov atmospheric model and its temporal evolution is predicted based on the frozen flow hypothesis. The deformable mirror is then continuously best-fitted to the time-sequenced phase screens based on the least square method. Similarly, we also implement another simulation by rotating two random phase plates which were manufactured to have atmospheric-disturbance-like residual aberrations. This later method is limited in its ability to simulate atmospheric disturbances, but it is easy and inexpensive to implement. With these two methods, individually or in unison, we can simulate typical atmospheric disturbances observed at the Bohyun Observatory in South Korea, which corresponds to an area from 7 to 15 cm with regard to the Fried parameter at a telescope pupil plane of 500 nm.

• Radiation Oncology Journal
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• v.36 no.2
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• pp.139-146
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• 2018

Purpose: IBTR! 2.0 nomogram is web-based nomogram that predicts ipsilateral breast tumor recurrence (IBTR). We aimed to validate the IBTR! 2.0 using an external data set. Materials and Methods: The cohort consisted of 2,206 patients, who received breast conserving surgery and radiation therapy from 1992 to 2012 at our institution, where wide surgical excision is been routinely performed. Discrimination and calibration were used for assessing model performance. Patients with predicted 10-year IBTR risk based on an IBTR! 2.0 nomogram score of <3%, 3%-5%, 5%-10%, and >10% were assigned to groups 1, 2, 3, and 4, respectively. We also plotted calibration values to observe the actual IBTR rate against the nomogram-derived 10-year IBTR probabilities. Results: The median follow-up period was 73 months (range, 6 to 277 months). The area under the receiver operating characteristic curve was 0.607, showing poor accordance between the estimated and observed recurrence rate. Calibration plot confirmed that the IBTR! 2.0 nomogram predicted the 10-year IBTR risk higher than the observed IBTR rates in all groups. High discrepancies between nomogram IBTR predictions and observed IBTR rates were observed in overall risk groups. Compared with the original development dataset, our patients had fewer high grade tumors, less margin positivity, and less lymphovascular invasion, and more use of modern systemic therapies. Conclusions: IBTR! 2.0 nomogram seems to have the moderate discriminative ability with a tendency to over-estimating risk rate. Continued efforts are needed to ensure external applicability of published nomograms by validating the program using an external patient population.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.143-154
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• 2012

As demand of water resources and attentions to changes in climate (e.g., due to ENSO) increase, long/short term prediction of precipitation is getting necessary in water planning. This research evaluated the ability of MM5 to predict precipitation in the Tampa Bay region over 23 year period from 1986 to 2008. Additionally MM5 results were statistically bias-corrected using observation data at 33 stations over the study area using CDF-mapping approach and evaluated comparing to raw results for each ENSO phase (i.e., El Ni$\tilde{n}$o and La Ni$\tilde{n}$a). The bias-corrected model results accurately reproduced the monthly mean point precipitation values. Areal average daily/monthly precipitation predictions estimated using block-kriging algorithm showed fairly high accuracy with mean error of daily precipitation, 0.8 mm and mean error of monthly precipitation, 7.1 mm. The results evaluated according to ENSO phase showed that the accuracy in model output varies with the seasons and ENSO phases. Reasons for low predictions skills and alternatives for simulation improvement are discussed. A comprehensive evaluation including sensitivity to physics schemes, boundary conditions reanalysis products and updating land use maps is suggested to enhance model performance. We believe that the outcome of this research guides to a better implementation of regional climate modeling tools in water management at regional/seasonal scale.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.12
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• pp.17-23
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• 2010

In the mobile cloud computing, the mobile node should request and receive the services while being connected. In PMIPv6, all packets sent by mobile nodes or correspondent nodes are transferred through the local mobility anchor. This unnecessary detour still results in high delivery latency and significant processing cost. Several PMIPv6 route optimization schemes have been proposed to solve this issue. However, they also suffer from the high signaling costs and handover latency when determining the optimized path. We propose the route optimization handover scheme which adopts the prediction algorithm in PFMIPv6. In the proposed scheme, the new mobile access gateway establishes the bi-directional tunnel with the correspondent node's MAG using the context message when the mobile node's handover is imminent. This tunnel may eliminate the need of separate route optimization procedure. Hence, the proposed scheme can reduce the signaling cost than other conventional schemes do. Analytical performance evaluation is preformed to show the effectiveness of the proposed scheme. The result shows that our scheme is more effective than other schemes.

• Journal of the Korea Concrete Institute
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• v.22 no.1
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• pp.19-27
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• 2010

Aging and severe environments are major causes of damage in reinforced concrete (RC) structures such as buildings and bridges. Deterioration such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for strengthening and rehabilitation of RC structures have been developed in the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to the conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of adaptive neuro-fuzzy inference system (ANFIS) in predicting behavior of circular type concrete column retrofitted with FRP. To construct training and testing dataset, experiment results for the specimens which have different retrofit profile are used. Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber and size of specimens are selected as input parameters to predict strength, strain, and stiffness of post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers.