• Journal of Advanced Marine Engineering and Technology
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• v.28 no.1
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• pp.90-97
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• 2004

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as speed detectors. but they increase cost and size of the motor and restrict the industrial drive applications. So in these days. many Papers have reported on the sensorless operation or DC motor(3)-(5). This paper Presents a new sensorless strategy using neural networks(6)-(8). Neural network structure has three layers which are input layer. hidden layer and output layer. The optimal neural network structure was tracked down by trial and error and it was found that 4-16-1 neural network has given suitable results for the instantaneous rotor speed. Also. learning method is very important in neural network. Supervised learning methods(8) are typically used to train the neural network for learning the input/output pattern presented. The back-propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• Structural Engineering and Mechanics
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• v.71 no.6
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• pp.739-749
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• 2019

This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function (${\delta}$) in which E and w are inputs and ${\delta}$ is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination ($R^2$) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• Korean Journal of Applied Biomechanics
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• v.29 no.2
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• pp.71-77
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• 2019

Objective: The aim of this study was to investigate postural control strategies on smart phone use during gait in over 50-year-old adults. Method: 8 elderly subjects (age: $55.5{\pm}3.29yrs$, height: $159.75{\pm}4.20cm$, weight: $62.87{\pm}8.44kg$) and 10 young subjects (age: $23.8{\pm}3.19yrs$, height: $158.8{\pm}5.97cm$, weight: $53.6{\pm}5.6kg$) participated in the study. They walked at a comfortable pace in a gaitway of ~8 m while: 1) reading text on a smart phone, 2) typing text on a smart phone, or 3) walking without the use of a phone. Gait parameters and kinematic data were evaluated using a three-dimensional movement analysis system. Results: The participants read or wrote text messages they walked with: slower speed; lesser stride length and step width; greater flexion range of motion of the head; more flexion of the thorax in comparison with normal walking. Conclusion: Texting or reading message on a smart phone while walking may pose an additional risk to pedestrians' safety.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1729-1737
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• 2019

This paper presents neutronics design of VVER-1000 fuel assembly using burnable poison particles (BPPs) for controlling excess reactivity and pin-wise power distribution. The advantage of using BPPs is that the thermal conductivity of BPP-dispersed fuel pin could be improved. Numerical calculations have been conducted for optimizing the BPP parameters using the MVP code and the JENDL-3.3 data library. The results show that by using $Gd_2O_3$ particles with the diameter of $60{\mu}m$ and the packing fraction of 5%, the burnup reactivity curve and pin-wise power distribution are obtained approximately that of the reference design. To minimize power peaking factor (PPF), total BP amount has been distributed in a larger number of fuel rods. Optimization has been conducted for the number of BPP-dispersed rods, their distribution, BPP diameter and packing fraction. Two models of assembly consisting of 18 BPP-dispersed rods have been selected. The diameter of $300{\mu}m$ and the packing fraction of 3.33% were determined so that the burnup reactivity curve is approximate that of the reference one, while the PPF can be decreased from 1.167 to 1.105 and 1.113, respectively. Application of BPPs for compensating the reduction of soluble boron content to 50% and 0% is also investigated.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.424-431
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• 2019

The aim of the present study was to develop model for detonation cell sizes prediction based on a deep artificial neural network of hydrogen, methane and propane mixtures with air and oxygen. The discussion about the currently available algorithms compared existing solutions and resulted in a conclusion that there is a need for a new model, free from uncertainty of the effective activation energy and the reaction length definitions. The model offers a better and more feasible alternative to the existing ones. Resulting predictions were validated against experimental data obtained during the investigation of detonation parameters, as well as with data collected from the literature. Additionally, separate models for individual mixtures were created and compared with the main model. The comparison showed no drawbacks caused by fitting one model to many mixtures. Moreover, it was demonstrated that the model may be easily extended by including more independent variables. As an example, dependency on pressure was examined. The preparation of experimental data for deep neural network training was described in detail to allow reproducing the results obtained and extending the model to different mixtures and initial conditions. The source code of ready to use models is also provided.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.46.2-46.2
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• 2019

We propose a deep learning model that can generate a waveform of coalescing binary black holes in merging and ring-down phases in less than one second with a graphics processing unit (GPU) as an approximant of gravitational waveforms. Up to date, numerical relativity has been accepted as the most adequate tool for the accurate prediction of merger phase of waveform, but it is known that it typically requires huge amount of computational costs. We present our method can generate the waveform with ~98% matching to that of the status-of-the-art waveform approximant, effective-one-body model calibrated to numerical relativity simulation and the time for the generation of ~1500 waveforms takes O(1) seconds. The validity of our model is also tested through the recovery of signal-to-noise ratio and the recovery of waveform parameters by injecting the generated waveforms into a public open noise data produced by LIGO. Our model is readily extendable to incorporate additional physics such as higher harmonics modes of the ring-down phase and eccentric encounters, since it only requires sufficient number of training data from numerical relativity simulations.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.337-344
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• 2019

The TRIGA fuel of the Philippine Research Reactor-1 (PRR-1) will be used in a subcritical reactor assembly (SRA) to strengthen and advance nuclear science and engineering expertise in the Philippines. SRA offers a versatile and safe training and research facility since it can produce neutrons through nuclear fission reaction without achieving criticality. In this work, we used a geometrically detailed model of the PRR-1 TRIGA fuel to design a subcritical reactor assembly and calculate physical parameters of different fuel configurations. Based on extensive neutron transport simulations an SRA configuration is proposed, comprising 44 TRIGA fuel rods arranged in a $7{\times}7$ square lattice. This configuration is found to have a maximum $k_{eff}$ value of $0.95001{\pm}0.00009$ at 4 cm pitch. The SRA is characterized by calculating the 3-dimensional neutron flux distribution and neutron spectrum. The effective delayed neutron fraction and mean neutron generation time of the system are calculated to be $748pcm{\pm}7pcm$ and $41{\mu}s$, respectively. Results obtained from this work will be the basis of the core design for the subcritical reactor facility that will be established in the Philippines.