• Journal of Biosystems Engineering
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• v.10 no.2
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• pp.19-26
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• 1985

This study was carried out to develop a versatile data acquisition/processing system for overall tractor performance utilizing a NEC PC-8001 microcomputer. The data acquisition system measures drawbar pull and power, wheel torque and axle power, ground speed, wheel slip, fuel flow, and engine speed. The system stores hexadecimal data for these variables in memory. Upon completion of each test run, all hexadecimal data stored in memory are recorded on floppy disc. The data processing system reads in the data collected on floppy disc and interprete them using several graphical and statistical techniques. The system uses the same microcomputer and a dot-matrix printer. The data acquisition system has been installed on a GOLDSTAR 500 tractor (2WD, 50 ps). A field study has shown that tractor performance data can be quickly and easily collected. It also appeared that the data processing system can be used to efficiently analyze the collected data. The data acquisition system has some troublesome in mounting and handling on tractor since it uses a general-purpose computer consisting of several components.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.67-75
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• 2021

As pass the three revised bills, the Personal Information Protection Act was revised to have a larger application for personal information. For an industrial development through an efficient and secure usage of personal information, there is a need to revise the existing anonymity processing method. This paper modifies the Zero Knowledge Proofs algorithm among the anonymity processing methods to modify the anonymity process calculations by taking into account the reliability of the used service company. More detail, the formula of ZKP (Zero Knowledge Proof) used by ZK-SNAKE is used to modify the personal information for pseudonymization processing. The core function of the proposed algorithm is the addition of user variables and adjustment of the difficulty level according to the reliability of the data user organization and the scope of use. Through Setup_p, the additional variable γ can be selectively applied according to the reliability of the user institution, and the degree of agreement of Witness is adjusted according to the reliability of the institution entered through Prove_p. The difficulty of the verification process is adjusted by considering the reliability of the institution entered through Verify_p. SimProve, a simulator, also refers to the scope of use and the reliability of the input authority. With this suggestion, it is possible to increase reliability and security of anonymity processing and distribution of personal information.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.123-129
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• 2022

Quartz (SiO2) has high abrasion and heat resistances and excellent chemical and mechanical properties; therefore, it is used in various industries, such as machinery, chemistry, optics, and medicine. Quartz is a high-hardness and brittle material and is classified as the topmost difficult-to-cut material, which is because of the cracking or chipping at the edge during processing. Corner wear, such as cracks and chippings that occur during cutting, is a major cause for the deterioration in the machining quality. Therefore, many researchers are investigating various techniques to process quartz effectively. However, owing to the mechanical properties of quartz, most studies have been conducted on grinding, micromachining, and microdrilling. Few studies have been conducted on quartz processing. The purpose of this study was to analyze the machining characteristics according to the machining factors during the slot machining of quartz using a cubic boron nitride (CBN) tool and to select the optimal machining conditions using the Taguchi method. The machining experiment was performed considering three process variables: the spindle speed, feed rate, and depth of cut. The cutting force and surface roughness were analyzed according to the processing conditions.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.239-251
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• 2024

Coronavirus disease 2019 (COVID-19) has severely curtailed travelers' willingness to carpool and complicated the psychological processing system of travelers' carpooling decisions. In the post-COVID-19 era, a two-stage decision model under dynamic decision scenarios is constructed by tracking the psychological states of subjects in the face of multi-scenario carpooling decisions. Through a scenario experiment method, this paper investigates how three psychological variables, travelers' psychological distance to COVID-19, anticipated regret, and experienced regret about carpooling decisions, affect their willingness to carpool and re-carpool. The results show that in the initial carpooling decision, travelers' perception gap of anticipated regret positively predicts carpooling willingness and partially mediates between psychological distance to COVID-19 and carpooling willingness; in the re-carpooling decision, travelers' perception gap of anticipated regret mediates in the process of experienced regret influencing re-carpooling willingness; the inhibitory effect of experienced regret on carpooling in the context of COVID-19 is stronger than its facilitative effect on carpooling willingness. This paper tries to offer a fact-based decision-processing system for travelers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.569-577
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• 2018

In this paper, with the aid of information which is included within data, preprocessing algorithm-based black plastic classifier is designed. The slope and area of spectrum obtained by using laser induced breakdown spectroscopy(LIBS) are analyzed for each material and its ensuing information is applied as the input data of the proposed classifier. The slope is represented by the rate of change of wavelength and intensity. Also, the area is calculated by the wavelength of the spectrum peak where the material property of chemical elements such as carbon and hydrogen appears. Using informations such as slope and area, input data of the proposed classifier is constructed. In the preprocessing part of the classifier, Principal Component Analysis(PCA) and fuzzy transform are used for dimensional reduction from high dimensional input variables to low dimensional input variables. Characteristic analysis of the materials as well as the processing speed of the classifier is improved. In the condition part, FCM clustering is applied and linear function is used as connection weight in the conclusion part. By means of Particle Swarm Optimization(PSO), parameters such as the number of clusters, fuzzification coefficient and the number of input variables are optimized. To demonstrate the superiority of classification performance, classification rate is compared by using WEKA 3.8 data mining software which contains various classifiers such as Naivebayes, SVM and Multilayer perceptron.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2888-2890
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• 2005

In this paper, we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C- Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Journal of the Korean Ceramic Society
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• v.44 no.10
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• pp.568-573
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• 2007

For the manufacturing lightweight fine aggregate, clay and waste material was formed by pelletizer. The fine aggregate of 1-5 mm diameter was formed by diameter 76 cm pelletizer disc. Pelletization variables were : (1) pelletizer disc angle, (2) speed of revolution of pelletizer, (3) added pelletization time. Green and sintered aggregate were measured specific gravity, absorption rate and average size. The optimum condition were found that the pelletization variables were angle at $70^{\circ}$, speed of revolution of pelletizer at 23.2 rpm, and water/solid ratio at 1/5. At these conditions, it was formed that fine aggregate green whose average size was $2.0{\sim}3.35mm$. Specific gravity and average size are increased with low angle of disc and fast revolution speed of disc. Specific gravity and average size were not distinctly influenced by added pelletization time. Sintered aggregate was distinctly influenced by properties of green.

• Transactions of Materials Processing
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• v.12 no.8
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• pp.724-729
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• 2003

In order to study the shearing characteristic of anti-vibration sheet metal which is used to reduce vibration noise, a blanking die was manufactured to blank a workpiece. The variables employed in this study were clearance, type of stripper plate, position of the rubber layer and type of the die design. These variables were used to study the effects on burr height, blank diameter and camber height. In the case of burr height from experimental investigation, the push-back die, combined with a movable stripper plate, showed greater burr height. The rubber-top position of a workpiece resulted in better qualities regardless of working variables. In the comparison of diameter measurement, the use of the push-back die with a fixed stripper plate, with a 4.5% clearance, showed better accuracy. For comparing camber height, the push-back die resulted in less cambering than the drop-through die. Also, the larger the clearance, the greater was the camber height. Considering experimental results, the shearing of anti-vibrational sheet metal is best achieved when the rubber layer is laying on the top, blanked with a fixed stripper plate in a push-back die, with a 4.5％ clearance.