• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1030-1035
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• 2012

In this research, we analysed design and CFD analysis of an inflow radial turbine for OTEC with an output power of 8kW using an working fluid of ammonia. The inflow radial turbine consists of scroll casing, vain nozzle with 18 blade numbers and rotor blade with 13 blade numbers. Mass flow rate, and inlet temperature are 0.5kg/s and $25^{\circ}C$ respectively, and variable rotational speeds were applied between 12,000 and 36,000 with 3,000 rpm intervals. As the results according to the rotational speeds, the designed speed is 24,000 rpm where maximum efficiency exists. The maximum efficiency and output power are 88.66% and 8.52kW, respectively. Through this study, we expect that the analysed results will be used as the design material for the composition of the turbine optimal design parameters corresponding to the target output power under various working material conditions.

• Journal of Oriental Neuropsychiatry
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• v.9 no.2
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• pp.19-35
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• 1998

Some oriental medicine turned out to have a significant clinical effect on the cure for dementia. Therefore, thorough scientific tests for physiological effect of oriental medicne are needed. This study is aimed at doing experimental studies on the effects of two medicines, Jowiseungcheongtang and hyungbangjihwangtang, on the cure for dementia.For the demonstration of the effect of the two medicines on aged rats, we perfomed a radial arm medicines on aged rats, water maze task, known for their proper learning paradigm for behavior.Previous studies on aging and dementia show that aged rats displayed significant impariments in the learning of the radial arm maze task compared with younger rats. As in experiment 1, we found that the learning of the radial arm maze task compared with younger rats. As in experiment 1, we found that the learning deficits aged rats exhibit in radial arm maze task were improved with the application of each medicine. The resutls suggest that these two medicine can be effective to patients whose working or shortterm memory is impaired. In experiment 2 we studied the effect of the two medicines on the deficit of the aged rats with the Morris water maze task known for measuring long-tern memory. We did not find significant results between the performance of the ages rats and the younger ones. Considered the different results previous studies have reported, more thorough studies are needed to investigate the effect of the medicines on long-term memory.In conclusion, the results we found in experiment 1 and 2 suggest that Jowisengcheongtang and hyungbangjihwangtang can have useful effects for the cure of age-related memory (especially for short-term memory)deficits. Recent interests in dementia urges researchers concerned to explore the effect of oriental medicine on the disease. As there have been relatively few behavioral or scientific studies on dementia using oriental medicine to date, further studies are expected are expected to continue to elucidate 'what the wisdom of the oriental medicine tells about dementia'.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1101-1105
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• 2014

Ocean Thermal Energy Conversion(OTEC) which uses the temperature difference between warm surface sea-water and cold deep sea-water to produce electric power is the promising technology. OTEC is able to be utilized as the $CO_2$ reducing technology by using the consistent temperature differential, while the system efficiency is very low. Thus, the design and development of a efficient turbine is essential to improve the system efficiency for OTEC. In this study, a 100kW-class radial inflow turbine using R32 was designed for OTEC and this turbine's performance was estimated by analysis of CFD. According as the simulation results, turbine's geometry was corrected. The radial inflow turbine satisfying the requirements is designed by the repeated attempts.

• Journal of Biomedical Engineering Research
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• v.41 no.5
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• pp.185-194
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• 2020

Arrhythmia is basically diagnosed with the electrocardiogram (ECG) signal, however, ECG is difficult to measure and it requires expert help in analyzing the signal. On the other hand, the radial pulse can be measured with easy and uncomplicated way in daily life, and could be suitable bio-signal for the recent untact paradigm and extensible signal for diagnosis of Korean medicine based on pulse pattern. In this study, we developed an irregular radial pulse detection algorithm based on a learning model and considered its applicability as arrhythmia screening. A total of 1432 pulse waves including irregular pulse data were used in the experiment. Three data sets were prepared with minimal preprocessing to avoid the heuristic feature extraction. As classification algorithms, elastic net logistic regression, random forest, and extreme gradient boosting were applied to each data set and the irregular pulse detection performances were estimated using area under the receiver operating characteristic curve based on a 10-fold cross-validation. The extreme gradient boosting method showed the superior performance than others and found that the classification accuracy reached 99.7%. The results confirmed that the proposed algorithm could be used for arrhythmia screening. To make a fusion technology integrating western and Korean medicine, arrhythmia subtype classification from the perspective of Korean medicine will be needed for future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2077-2082
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• 2008

Recent the advanced technologies in medical imaging such as magnetic resonance imaging (MRI) and computed tomography (CT) make doctors improve the diagnostic skill with detailed anatomical information. In general, it is necessary to get a number of MRI images in order to obtain more detail information. However, the performance of MRI machines of privately run hospitals is not good and thus we may obtain only a few of MRI images. If 3D surface reconstruction is accomplished with a few slices, then it generates 3D surface of poor qualify. This paper propose a way to Set a 3D surface of high quality from a few of number of slices. First of all, our algorithm detects the boundary of tissues which we want to reconstruct as a 3D object and find out the set of vortices on the boundary. And then we generate a 3D implicit surface to interpolate the boundary points by using radial basis function. Lastly, we render the 3D implicit surface by using Marching cube algorithms.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3150-3163
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• 2023

To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multiphysics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.

• Smart Media Journal
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• v.11 no.1
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• pp.46-57
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• 2022

In this paper, multi-region based Radial Graph Convolutional Network (MRGCN) algorithm which can perform end-to-end action recognition using the optical flow and gradient of input image is described. Because this method does not use information of skeleton that is difficult to acquire and complicated to estimate, it can be used in general CCTV environment in which only video camera is used. The novelty of MRGCN is that it expresses the optical flow and gradient of the input image as directional histograms and then converts it into six feature vectors to reduce the amount of computational load and uses a newly developed radial type network model to hierarchically propagate the deformation and shape change of the human body in spatio-temporal space. Another important feature is that the data input areas are arranged being overlapped each other, so that information is not spatially disconnected among input nodes. As a result of performing MRGCN's action recognition performance evaluation experiment for 30 actions, it was possible to obtain Top-1 accuracy of 84.78%, which is superior to the existing GCN-based action recognition method using skeleton data as an input.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.484-491
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• 2003

For most of linear time-varying (LTV) systems, it is difficult to design time-varying controllers in analytic way. Accordingly, by approximating LTV systems as uncertain linear time-invariant, control design approaches such as robust control have been applied to the resulting uncertain LTI systems. In particular, a robust control method such as quantitative feedback theory (QFT) has an advantage of guaranteeing the frozen-time stability and the performance specification against plant parameter uncertainties. However, if these methods are applied to the approximated linear. time-invariant (LTI) plants with large uncertainty, the resulting control law becomes complicated and also may not become ineffective with faster dynamic behavior. In this paper, as a method to enhance the fast dynamic performance of LTV systems with bounded time-varying parameters, the approximated uncertainty of time-varying parameters are reduced by the proposed QFT parameter-scheduling control design based on radial basis function (RBF) networks.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.182-187
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• 2003

In this thesis, we propose the algorithm to improve the performance of speaker verification that is mapping feature parameters by using RBF neural network. There is a big difference between wire vector region and wireless one which comes from the same speaker. For wire/wireless speakers model production, speaker verification system should distinguish the wire/wireless channel that based on speech recognition system. And the feature vector of untrained channel models is mapped to the feature vector(LPC Cepstrum) of trained channel model by using RBF neural network. As a simulation result, the proposed algorithm makes 0.6%∼10.5% performance improvement compared to conventional method such as cepstral mean subtraction.