• Journal of Korean Society for Atmospheric Environment
• /
• v.9 no.3
• /
• pp.242-246
• /
• 1993

Theoretically, spectral method has the highest accuracy among present numerical methods, but it is generally difficult to apply to complex terrains because of complex boundary conditions. Recently, spectral-element method, basically divide the domain into a set of rectangular subdomain and solve the equation at each subdomain, has been introduced. However, boundary conditions become more complex and requires more computing time, thus spectral-element method is not powerful for all complex terrain problems. In this paper, potential flow theory was intorduced to solve the air flows and diffusion phenomenon in the presence of terrain obstacles. Using the velocity potential-stream line orthogonal coordinate space, the diffusion problems of hilly terrain by pseudospectral method were solved and compared those with no terrain real scale solutions.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.5 no.2
• /
• pp.51-57
• /
• 1993

It is often assumed that the wave velocity at the bottom given by potential wave theory il the same as the wave velocity at the top of the turbulent boundary layer. This assumption is found to be the major cause of the error detected by recent elaborate theories and numerical models for the description of velocity profile near the sea bottom. A relationship is suggested between the potential velocity and the real boundary velocity. Based on this relation, the existing theories of Jonsson (1967) and Fredsoe (1984) are refined for the estimation of wave friction factor, and the computation results of the modified theories are favourably compared with the published laboratory results.

• Journal of Korean Society on Water Environment
• /
• v.27 no.3
• /
• pp.273-285
• /
• 2011

This study describes the modeling of climate change impact on runoff across southeast Korea using a conceptual rainfall-runoff model TANK and assesses the results using the concept of environmental flows developed by International Water Management Institute. The future climate time series is obtained by scaling the historical series, informed by 4 global climate models and 3 greenhouse gas emission scenarios, to reflect a $4.0^{\circ}C$ increase at most in average surface air temperature and 31.7% increase at most in annual precipitation, using the spatio-temporal changing factor method that considers changes in the future mean seasonal rainfall and potential evapotranspiration as well as in the daily rainfall distribution. Although the simulation results from different global circulation models and greenhouse emission scenarios indicate different responses in flows to the climate change, the majority of the modeling results show that there will be more runoff in southeast Korea in the future. However, there is substantial uncertainty, with the results ranging from a 5.82% decrease to a 48.15% increase in the mean annual runoff averaged across the study area according to the corresponding climate change scenarios. We then assess the hydrologic perturbations based on the comparison between present and future flow duration curves suggested by IMWI. As a result, the effect of hydrologic perturbation on aquatic ecosystems may be significant at several locations of the Nakdong river main stream in dry season.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.45 no.5
• /
• pp.110-116
• /
• 2003

A catchment modelling system is the summation of the numerous hydrologic, hydraulic and other process models necessary to simulate the response of a catchment to a storm event. Differences between the recorded catchment response and that predicted by a catchment modelling system can arise from structural errors within the catchment modelling system, evaluation errors in the control parameters, or measurement errors in the recorded data being used to assess the reliability of the evaluation of the control parameters. Presented herein is an investigation of the potential measurement errors within the recorded information, which was considered to occur from instrument error in the ultra sonic flow monitor. This investigation was undertaken using three available rating curves at the Musgrave Avenue Stormwater System in Centennial Park, Sydney, developed by Abustan (1997), Water Board (1994), and using Manning's equation.

• Land and Housing Review
• /
• v.10 no.3
• /
• pp.9-16
• /
• 2019

This study aimed at developing control algorithms for operating a variable refrigerant flow (VRF) heating and cooling system with optimal system parameter set-points. Two artificial neural network (ANN) models, which were respectively designed to predict the heating energy cost and cooling energy amount for upcoming next control cycle, was developed and embedded into the control algorithms. Performance of the algorithms were tested using the computer simulation programs - EnergyPlus, BCVTB, MATLAB in an incorporative manner. The results revealed that the proposed control algorithms remarkably saved the heating energy cost by as much as 7.93% and cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings support that the ANN-based predictive control algorithms showed potential for cost- and energy-effectiveness of VRF heating and cooling systems.

• Journal of Advanced Navigation Technology
• /
• v.14 no.6
• /
• pp.984-989
• /
• 2010

In this paper, parallel programming technique by using Java Thread is introduced so as to develop parallel design tool to analyze the small micro flow sensor. To estimate computing time for Thread-level parallelism, the performances of two experimental models for potential problem subject to Thermal transfer equation are examined. As a result, if the number of network PC is increase, computing time for parallelism on network environment is enhanced to be almost n times. The micro sensor design tool based on distributed computing can be utilized to analyze a large scale problem.

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

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.20-20
• /
• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Asian Spine Journal
• /
• v.12 no.6
• /
• pp.998-1009
• /
• 2018

Study Design: Olfactory ensheathing cells (OECs) from rat olfactory mucosa were cultured, characterized, and transplanted into a rat model of spinal cord injury (SCI). Purpose: To evaluate different doses of OECs in a rat model of SCI. Overview of Literature: SCI causes permanent functional deficit because the central nervous system lacks the ability to perform spontaneous repair. Cell therapy strategies are being explored globally. The clinical use of human embryonic stem cell is hampered by ethical controversies. Alternatively, OECs are a promising cell source for neurotransplantation. This study aimed to evaluate the efficacy of different doses of allogenic OEC transplantation in a rat model of SCI. Methods: OECs were cultured from the olfactory mucosa of Albino Wistar rats; these cells were characterized using immunohistochemistry and flow cytometry. Rats were divided into five groups (n=6 rats each). In each group, different dosage ($2{\times}10^5$, $5{\times}10^5$, $10{\times}10^5$, and >$10{\times}10^5$) of cultured cells were transplanted into experimentally injured spinal cords of rat models. However, in the SCI group, only DMEM (Dulbecco's modified Eagle's medium) was injected. Rats were followed up upto 8 weeks post-transplantation. The outcome of transplantation was assessed using the Basso, Beattie, Bresnahan (BBB) scale; motor-evoked potential studies; and histological examination. Results: Cultured cells expressed 41% of p75NTR, a marker for OEC, and 35% of anti-fibronectin, a marker for olfactory nerve fibroblast. These cells also expressed $S100{\beta}$ and glial fibrillary acid protein of approximately 75% and 83%, respectively. All the transplanted groups showed promising BBB scores for hind-limb motor recovery compared with the SCI group (p<0.05). A motor-evoked potential study showed increased amplitude in all the treated groups compared with the SCI. Green fluorescent protein-labeled cells survived in the injured cord, suggesting their role in the transplantation-mediated repair. Transplantation of $5{\times}10^5$ cells showed the best motor outcomes among all the doses. Conclusions: OECs demonstrated a therapeutic effect in rat models with the potential for future clinical applications.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.4
• /
• pp.268-273
• /
• 2000

Plasma models are crucial to equipment design and process optimization. A radial basis function network(RBFN) in con-junction with statistical experimental design has been used to model a process plasma. A 2$^4$ full factorial experiment was employed to characterized a hemispherical inductively coupled plasma(HICP) in characterizing HICP, the factors that were varied in the design include source power, pressure, position of shuck holder, and Cl$_2$ flow rate. Using a Langmuir probe, plasma attributes were collected, which include typical electron density, electron temperature. and plasma potential as well as their spatial uniformity. Root mean-squared prediction errors of RBEN are 0.409(10(sup)12/㎤), 0.277(eV), and 0.699(V), for electron density, electron temperature, and Plasma potential, respectively. For spatial uniformity data, they are 2.623(10(sup)12/㎤), 5.704(eV) and 3.481(V), for electron density, electron temperature, and plasma potential, respectively. Comparisons with generalized regression neural network(GRNN) revealed an improved prediction accuracy of RBFN as well as a comparable performance between GRNN and statistical response surface model. Both RBEN and GRNN, however, experienced difficulties in generalizing training data with smaller standard deviation.