• Journal of the Ergonomics Society of Korea
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• v.30 no.5
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• pp.645-650
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• 2011

Objective: The aim of this study is to design a sampling inspection plan with human error which is changing according to inspection time. Background: Typical sampling inspection plans have been established typically based on an assumption of the perfect inspection without human error. However, most of all inspection tasks include human errors in the process of inspection. Therefore, a sampling inspection plan should be designed with consideration of imperfect inspection. Method: A model for single sampling inspection plans were proposed for the cases that visual inspection error rate is changing according to inspection time. Additionally, a sampling inspection plan for an optimal inspection time was proposed. In order to show an applied example of the proposed model, an experiment for visual inspection task was performed and the inspection error rates were measured according to the inspection time. Results: Inspection error rates changed according to inspection time. The inspection error rate could be reflected on the single sampling inspection plans for attribute. In particular, inspection error rate in an optimal inspection time may be used for a reasonable single sampling plan in a practical view. Conclusion: Human error rate in inspection tasks should be reflected on typical single sampling inspection plans. A sampling inspection plan with consideration of human error requires more sampling number than a typical sampling plan with perfect inspection. Application: The result of this research may help to determine more practical sampling inspection plan rather than typical one.

• Journal of the Korean Solar Energy Society
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• v.38 no.3
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• pp.1-20
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• 2018

It is a very important and fundamental process to know accurately the intensity of the solar energy coming into the installed module considering the tilted angle. Europe and the US commonly use a program called PVsyst to convert the global horizontal irradiation to global irradiation on tilted plane. There are two types of models that PVsyst uses to convert to irradiation on tilted plane. In this paper, Perez model, which is a decomposition model and Perez model, which is a transposition model used in PVsyst, are applied based on global horizontal irradiation and global irradiation on tilted plane measured in a specific area. The comparison of the decomposition model shows the effect of the transpostion model on global irradiation on tilted plane conversion by comparing the ratio of the horizontal diffuse irradiation amount of the Watanabe model which are highly trusted in Asia and the Perez model. The comparison of transposition model confirm the error between the measured data and the calculated value which is applied Perez model to global horizontal irradiation decomposed by Perez model and Watanabe model. Based on the two comparisons, This paper propose a method to confirm the reliability of transposition model and reduce the error when PVsyst is used in Korea.

• Journal of the Korean Applied Science and Technology
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• v.22 no.1
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• pp.62-70
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• 2005

Step-feed process for biological nitrogen removal were analyzed numerically for the each unit and final total nitrogen(TN) effluent by water quality management(WQM) model and the results were compared data from these wastewater treatment plants. No bugs and logic error were occurred during simulation work. All of the simulation results tried to two times were obtained and both results were almost same as this model has become good reappearance. It was concluded that most of nitrogen removal occurred in the first oxic tank. Thus the controlling of the first anoxic tank may be more important in term of nitrogen removal. Also each unit of simulation result was kept good relationship with that of measured data. Accordingly this WQM model has good reliance. Finally, WQM model can predict final TN effluent within ${\pm}6.0mg/{\ell}$.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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• pp.957-964
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• 2006

As the requirement of LCD products which are large screen and have high brightness increases, the role of light guide panel (LGP) of which micro-features diffuse the light uniformly on surface is getting important. In general, there are many errors in machining like machine tool errors process error, setup error and etc. The amount of setup error in general machining is not so big in comparison with the others, so it is mostly neglected. But, especially in v-groove micromachining, setup error has a significant effect on micro-features. Low quality product and high cost are resulted from setup error. In v-groove micromachining, to confirm the effect of setup error, it is identified and then setup error synthesis model is derived from analysis of tool and workpiece setup. In addition, to predict the micro-features affected by setup error and enhance the production efficiency, the setup condition satisfying the tolerance of micro-features is geometrically analyzed and presented.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.2
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• pp.127-133
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• 2015

The error motion of a machine tool spindle directly affects the surface errors of machined parts. Those are usually due to the imperfectness of bearings, stiffness of spindle, assembly errors, external force or unbalance of rotors. The error motions of the spindle have been needed to be decreased to desired goal of spindle's performance. The level of error motion is needed to be estimated during the design and assembly process of the spindle. In this paper, the estimation method for the five degree of freedom (5 D.O.F) error motions for rotary units such as a spindle and rotary table are suggested. To estimate the error motions of the rotary unit, waviness of bearings and external force model were used as input data. The estimation model considers geometric relationship and force equilibrium of the five degree of the freedom motions.

• Journal of the Korea Furniture Society
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• v.19 no.5
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• pp.319-327
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• 2008

As art of lamination by plywood got to be generally used, it became a suitable material for expressing live curves that were not able to be expressed on wood furniture made of plank and timber, as well as, openwork deep in curved space, heavy quality of material, and changing contour line-looking wave lines with different process angles. As an alternative, it would be good to build a full scale model, since it would provide practice in form-building and it would also provide a chance to correct the form. Less material can be used and reduce the cutting process by Properly trimming models made of soft formal structure such as Styrofoam Iso-pink and adhesive Styrofoam, and separating the layers and using them on shape cutting of plywood with the same thickness. And by attaching the model veneer that was used in shape cutting of the model and using it as a cutting guide, we can reduce the error of work and successively build the planned form. Since this study is about the need of a full scale model for a laminated wood model and an efficient process, this study concentrates more on process.

• Journal of Biosystems Engineering
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• v.43 no.3
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• pp.229-236
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• 2018

Purpose: Makgeolli is a traditional alcoholic beverage made from rice with a fermentation starter called "nuruk." The concentration of alcohol in makgeolli depends on the temperature of the fermentation tank. It is important to monitor the alcohol concentration to manage the makgeolli production process. Methods: Data were collected from 84 makgeolli fermentation tanks over a year period. Independent variables included the temperatures of the tanks and the room where the tanks were located, as well as the quantity, acidity, and water concentration of the source. Software for the multilayer perceptron model (MLP) was written in Python using the Scikit-learn library. Results: Many models were created for which the optimization converged within 100 iterations, and their coefficients of determination $R^2$ were considerably high. The coefficient of determination $R^2$ of the best model with the training set and the test set were 0.94 and 0.93, respectively. The fact that the difference between them was very small indicated that the model was not overfitted. The maximum and minimum error was approximately 2% and the total MSE was 0.078%. Conclusions: The MLP model could help predict the alcohol concentration and to control the production process of makgeolli. In future research, the optimization of the production process will be studied based on the model.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.93-101
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• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.892-898
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• 2012

Rough classification methods are used to improving the recognition speed in many character recognition problems. In this case, some irreversible result can occur by an error in rough classification. Methods for duplicating each model in several classes are used in order to reduce this risk. But the errors by rough classfication can not be completely ruled out by these methods. In this paper, an recognition method is proposed to increase speed that matches models selectively without any increase in error. This method constructs a model graph using similarity between models. Then a search process begins from a particular point in the model graph. In this process, matching of unnecessary models are reduced that are not similar to the input pattern. In this paper, the proposed method is applied to the recognition problem of handwriting numbers and upper/lower cases of English alphabets. In the experiments, the proposed method was compared with the basic method that matches all models with input pattern. As a result, the same recognition rate, which has shown as the basic method, was obtained by controlling the out-degree of the model graph and the number of maintaining candidates during the search process thereby being increased the recognition speed to 2.45 times.