• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.26-31
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• 2015

Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.21 no.6
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• pp.333-339
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• 2009

The objective of this study is to predict optimal intermediate pressure of a 2-stage compression heat pump system using river water. To determine the maximum performance of the 2-stage compression heat pump system, the experimental evaluations on the 2-stage compression cycle were carried out under various operating conditions. Electronic expansion valves were applied to control intermediate pressure and superheat. Based on the experimental data, an empirical correlation for predicting optimal intermediate pressure which considering cycle operating parameters was developed. The present correlation was verified by comparing the predicted data with the measured data. The predictions showed a good agreement with the measured data within a relative deviation of ${\pm}4%$ at various operating conditions.

• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.

• Proceedings of the Korean Biophysical Society Conference
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• 2003.06a
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• pp.65-65
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• 2003

After many genome projects, algorithms and software to process explosively growing biological information have been developed. To process huge amount of biological information, high performance computing equipments are essential. If we use the remote resources such as computing power, storages etc., through a Grid to share the resources in the Internet environment, we will be able to obtain great efficiency to process data at a low cost. Here we present the performance improvement of the protein secondary structure prediction (PSIPred) by using the Grid platform, distributing protein sequence data on the Grid where each computer node analyzes its own part of protein sequence data to speed up the structure prediction. On the Grid, genome scale secondary structure prediction for Mycoplasma genitalium, Escherichia coli, Helicobacter pylori, Saccharomyces cerevisiae and Caenorhabditis slogans were performed and analyzed by a statistical way to show the protein structural deviation and comparison between the genomes. Experimental results show that the Grid is a viable platform to speed up the protein structure prediction and from the predicted structures.

• Journal of the Korean Home Economics Association
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• v.30 no.1
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• pp.267-282
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• 1992

This study investigated (1) present modes of parent education in the kindergartens, (2) mother's level of and need for knowledge about child development, child rearing and related areas, and (3) the variables that related to mothers' needs in these areas. The subjects of this study were 80 teachers and 674 mothers of 21 kindergartens in Seoul. The instruments were two questionnaires on a 4-point scale. The questionnair for mothers was composed of 86 items, and that for teachers was of 14 items. The data were analyzed with frequency, percentage, mean, standard deviation and multiple regression. The results showed that (1) The most common type of parent education was techer-parent conference and newsletter. The contents and methods of parent education was mainly decided by the directors in consulation with teachers. (2) Among the six general areas, mothers had most knowledge abut modification of child behavior, but their most felt needs was knowledge about child development (3) Need assessment based on item analysis revealed, in descending order, felt needs for knowledge about creative development, observation techniques, social developement, saftey and first-aid, and ways to stimulate educational motivation. (4) The variables that predicted mothers' felt needs were the birth order of the child, mother's experience in parent education, family cohesion, adaptability and communication.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.132-141
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• 2011

IEEE 802.15.4 networks are a feasible platform candidate for connecting all health-care-related equipment dispersed across a hospital room to collect critical time-sensitive data about patient health state, such as the heart rate and blood pressure. To meet the quality of service requirements of health-care systems, this paper proposes a multi-priority queue system that differentiates between various types of frames. The effect of the proposed system on the average delay and throughput is explored herein. By employing different contention window parameters, as in IEEE 802.11e, this multi-queue system prioritizes frames on the basis of priority classes. Performance under both saturated and unsaturated traffic conditions was evaluated using a novel analytical model that comprehensively integrates two legacy models for 802.15.4 and 802.11e. To improve the accuracy, our model also accommodates the transmission retries and deferment algorithms that significantly affect the performance of IEEE 802.15.4. The multi-queue scheme is predicted to separate the average delay and throughput of two different classes by up to 48.4% and 46%, respectively, without wasting bandwidth. These outcomes imply that the multi-queue system should be employed in health-care systems for prompt allocation of synchronous channels and faster delivery of urgent information. The simulation results validate these model's predictions with a maximum deviation of 7.6%.

• Journal of the Korean Chemical Society
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• v.62 no.3
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• pp.191-202
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• 2018

The aim of this work is to study Monte Carlo simulation of ethylene (co)polymerization over Ziegler-Natta catalyst as investigated by Chen et al. The results revealed that the Monte Carlo simulation was similar to sum square error (SSE) model to prediction of stage II and III of polymerization. In the case of activation stage (stage I) both model had slightly deviation from experimental results. The modeling results demonstrated that in homopolymerization, SSE was superior to predict polymerization rate in current stage while for copolymerization, Monte Carlo had preferable prediction. The Monte Carlo simulation approved the SSE results to determine role of each site in total polymerization rate and revealed that homopolymerization rate changed from site to site and order of center was different compared to copolymerization. The polymer yield was reduced by addition of hydrogen amount however there was no specific effect on uptake curve which was predicted by Monte Carlo simulation with good accuracy. In the case of copolymerization it was evolved that monomer chain length and monomer concentration influenced the rate of polymerization as rate of polymerization reduced from 1-hexene to 1-octene and increased when monomer concentration proliferate.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.5
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• pp.213-219
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• 2017

This study investigated the thermal conductivity of $CO_2$ gas mixtures in order to ascertain the effects of particular impurities in $CO_2$ in pipeline transportation. We predicted the thermal conductivity of three $CO_2$ gas mixtures ($CO_2+N_2$, $CO_2+H_2S$, and $CO_2+CH_4$) by utilizing three different methods : Chung et al., TRAPP, and the REFPROP model. We validated predictions by comparing the estimated results with 216 experimental data for $CO_2+CH_4$, $CO_2+N_2$, and $CO_2+C_2H_6$. Following $CO_2$ transportation conditions, we observed that the model developed by Chung et al. showed the lowest mean deviation of 3.07%. Further investigations were carried out on the thermal conductivity of $CO_2$ gas mixtures based on the Chung et al. model including the effects of the operation parameters of pressure, temperature, and mole fraction of impurities.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.203-206
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• 1996

During the electromyographic evaluation, the presence and site of lession may be predicted by a detection of denervation potentials such as fibrllation potentials or positive sharp waves in the group of muscles. Currently, clinicians diagnose the neuropathy by detecting fibrillation potentials during EMG tests, and sometimes it is not easy to distinguish between denervation potentials and endplate spikes. The purpose of this study was to find statistically significant parameters for the quantitative distinction between denervation potentials and endplate spikes. Endplate spikes and denervation potentials from the EDB muscle of 10 patients were extracted. Also, EMG signals were classified by experienced clinicians, and were collected using a 12 bit ADC with a sampling rate of 20KHz for the duration of 400msec. In order to find statistically significant parameters, positive and negative peaks were used for analysis. As a results, standard deviation of the endplate spikes and denervation potentials showed more significant difference than others specially for the positive sharp waves. It was concluded that the results of this study could be used to develope an automated system of a EMG analysis.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1301-1309
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• 2017

The extinction probability of a branching process (a neutron chain in a multiplying medium) is calculated for a system randomly varying in time. The evolution of the first two moments of such a process was calculated previously by the authors in a system randomly shifting between two states of different multiplication properties. The same model is used here for the investigation of the extinction probability. It is seen that the determination of the extinction probability is significantly more complicated than that of the moments, and it can only be achieved by pure numerical methods. The numerical results indicate that for systems fluctuating between two subcritical or two supercritical states, the extinction probability behaves as expected, but for systems fluctuating between a supercritical and a subcritical state, there is a crucial and unexpected deviation from the predicted behaviour. The results bear some significance not only for neutron chains in a multiplying medium, but also for the evolution of biological populations in a time-varying environment.