• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.8 s.113
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• pp.806-813
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• 2006

An important factor of vibration test using MAST(multi axial simulation table) system is the reliance of input excitation source. Generally the generation of input excitation source is obtained by the measured data on special road in proving ground. The measured data on special road have more exciting energy than the data of real fields, therefore the time and expense for test can be reduced. But the magnitude of input excitation source must be defined by comparison with the excited energy on real field. The object of this paper makes the data base of domestic roads for the definition of input excitation source which is obtained by the measured data on special road in proving ground. These real field data on domestic roads are analyzed by the power spectral density and interrelationship index.

• The Plant Pathology Journal
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• v.38 no.4
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.523-536
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• 2011

The objective of this study is to develop the data driven model for the flood forecasting that are improved the problems of the existing hydrological model for flood forecasting in medium and small streams. Neuro-Fuzzy flood forecasting model which linked the Takagi-Sugeno fuzzy inference theory with neural network, that can forecast flood only by using the rainfall and flood level and discharge data without using lots of physical data that are necessary in existing hydrological rainfall-runoff model is established. The accuracy of flood forecasting using this model is determined by temporal distribution and number of used rainfall and water level as input data. So first of all, the various combinations of input data were constructed by using rainfall and water level to select optimal input data combination for applying Neuro-Fuzzy flood forecasting model. The forecasting results of each combination are compared and optimal input data combination for real-time flood forecasting is determined.

• Journal of Environmental Science International
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• v.24 no.7
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• pp.865-874
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• 2015

This study was conducted to investigate the correlation between the distribution chart and input data of the predicted 3-second gust and damage cost, by using the forecast field and analysis field of Regional Data Assimilation Prediction System (RDAPS) as initial input data of Korea risk assessment model (RAM) developed in the preceding study. In this study the cases of typhoon Rusa which caused occurred great damage to the Korean peninsula was analyzed to assess the suitability of initial input data. As a result, this study has found out that the distribution chart from the forecast field and analysis field predicted from the point where the effect due to the typhoon began had similarity in both 3-second gust and damage cost with the course of time. As a result of examining the correlation, the 3-second gust had over 0.8, and it means that the forecast field and analysis field show similar results. This study has shown that utilizing the forecast field as initial input data of Korea RAM could suit the purpose of pre-disaster prevention.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.3
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• pp.220-227
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• 2002

An one-dimensional VLSI array for high speed processing of Fractal image compression was designed. Using again the overlapped input data of adjacent domain blocks in the existing one-dimensional VLSI array, we can save the number of total input for the operations, and so we can save the total computation time. In the design procedure, we considered the data dependences between the input data, reordered the input data to the array, and designed the processing elements. Registers and multiplexors are added for the storing and routing of the input data in some processing elements. Consequently as adding a little hardware, this design shows (N-4B)/4(N-B) times of speed-up compared with the existing array, where N is image size and B is block size.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.182-187
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• 2007

The accuracy of the results of the rail vehicle dynamic model is dependent on the realism of the track input to the model. An important part of the track input is the irregularities that exist on actual track. This study presents a modeling method for creating track data with the irregularities for use as the input to VAMPIRE, a rail vehicle dynamic analysis program. The characteristics of the measured track data using the mid chord system has been studied and examined the method to create track data with the measured data to apply in the vehicle dynamic analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.63-80
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• 1992

The concept of rapid prototyping intended for a significant reduction in cost and lead time becomes even more practical with the recent development of various equipments to make the concept concrete. For the purpose of real application of commercially available SLA(stereolithography apparatus), this paper is intended to develop the standard conversion procedure from CAD data to the input data for SLA. While the procedure presented in this paper is based on CAD system "CATIA" and SLA of 3D systems, Inc., which are being used in authors' company DAEWOO Motor Co., Ltd., the basic concept of this paper can be applied to any other CAD systems and machines of using stereolithography process. The algorithm presented in this paper is classified into two stages-node sampling and triangulation. First of all, point data are sampled through the node sampling procedure, and then these are triangulated so that the input data for SLA operation is finally generated. The suggested method is devised in a way to meet the input requirements of SLA and more importantly consume less computation time and generate less number of input data for SLA.

• Journal of Technology Innovation
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• v.7 no.1
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• pp.176-186
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• 1999

This paper starts out by reviewing the literature that in different ways utilizes patent data as an output of Research ＆ Development (R&D) investment. The main focus, however, is an analysis of time-lag between industrial R&D input and its output. To achieve this research's purpose, the basic data associated with the industrial R&D input (expenditure, researchers) and output (applied patent and utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw input data were altered into real flow data (but stock data) using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag; between industrial R&D input and its output is within 1 to 3 years. b) The time-lag: of patents was longer than that of utility models. c) The time-lag: in electrical-electronic, chemical industry was longer than that of the mechanical industry.

• The Journal of Bigdata
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• v.7 no.1
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• pp.99-111
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• 2022

A simulation technique can be used to analyze performance measures and support decision makings in manufacturing systems considering operational uncertainty and complexity. The simulation requires an input modeling procedure to reflect the target system's characteristics. However, data collection to build a simulation is quite limited when a target system includes manual productions with a lot of operational time such as construction equipment assembly lines. This study proposes a procedure for simulation input modeling using video data when it is difficult to collect enough input data to fit a probability distribution. We conducted a video-data analysis and specify input distributions for the simulation. Based on the proposed procedure, simulation experiments were conducted to evaluate key performance measures of the target system. We also expect that the proposed procedure may help simulation-based decision makings when obtaining input data for a simulation modeling is quite challenging.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.47-53
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• 2001

In this paper, we compare the classification performances of both ensemble and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. In view of the unknown relationship between input and output function, we use a Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: When the level of the variance is medium, Bagging & Parameter Combining performs worse than Logistic Regression, Variable Selection Bagging and Clustering. However, classification performances of Logistic Regression, Variable Selection Bagging, Bagging and Clustering are not significantly different when the variance of input data is either small or large. When there is strong correlation in input variables, Variable Selection Bagging outperforms both Logistic Regression and Parameter combining. In general, Parameter Combining algorithm appears to be the worst at our disappointment.