• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.192-200
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• 2022

Nitrogen is the most essential macronutrient for the growth of fruit trees and is important factor determining the fruit yield. In order to produce high-quality fruits, it is necessary to supply the appropriate nitrogen fertilizer at the right time. For this, it is a prerequisite to accurately diagnose the nitrogen status of fruit trees. The fastest and most accurate way to determine the nitrogen deficiency of fruit trees is to measure the nitrogen concentration in leaves. However, it is not easy for citrus growers to measure nitrogen concentration through leaf analysis. In this study, several machine learning models were developed to classify the nitrogen deficiency based on the concentration measurement of mineral nutrients in the leaves of tangor Shiranuhi (Citrus unshiu × C. sinensis). The data analyzed from the leaves were increased to about 1,000 training dataset through the bootstrapping method and used to train the models. As a result of testing each model, gradient boosting model showed the best classification performance with an accuracy of 0.971.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1267-1267
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• 2001

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.226-230
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• 2009

Intelligent Tutoring System (ITS) is a procedure of analyzing collected data for teaming, making a strategy and performing adequate service for learners. To perform suitable service for learners, modeling is the first step to collect data from the process of their learning. The model, however, cannot be authentic if collected data can contain learners' inconsistent behaviors or unpredictable learning inclination. This study focused on how to sort normal and abnormal data by analyzing collected data from learners through visualization. A model has been set up to assort unusual data from collected learner's data by using DOLLS-HI which makes possible to diagnose learner's learning propensity based on housing interior learning contents in the experiment. The created model has been confirmed its improved reliability comparing to previous one.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.48-55
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• 2020

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.277-287
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• 2021

The purpose of this study is to establish a model that can quantitatively diagnose personal color. Representative color systems for personal colors have limitations in that it oversimplify personal color diagnosis types or it is difficult to distinguish objective differences between diagnosis types. To develop a brand new color system that enhances this, a PCCS color system capable of logical color was introduced and reclassified based on the four main properties of color. Twenty diagnostic types, which are more diverse than the existing color system were proposed and a quantitative method was used to evaluate the degree of harmony with a subject to find an optimized type of subject. The experimenter's individual competency and subjective intervention were minimized by devising a matrix in which a type suitable for the subject is derived when the coded evaluation result is substituted. Finally a quantitative diagnosis model of personal color consisting of three stages: property diagnosis, coding, and seasonal diagnosis was constructed. It can be seen that this will give diversity, reliability, and accuracy to the existing diagnostic methods.

• Journal of Venture Innovation
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• v.7 no.2
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• pp.143-152
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• 2024

Recently, as a lot of software with AI functions has been developed, the number of software products with various prediction functions is increasing, and as a result, the importance of software quality has increased. In particular, as consideration for functional safety of products with AI functions increases, software quality management is being conducted at a national level. In particular, the GS Quality Certification System is a quality certification system for software products that is being implemented at the national level, and the GS Certification System is also researching quality evaluation methods for AI products. In this study, we attempt to present an evaluation model that satisfies the basic conditions of software quality based on international standards among the various quality evaluation models presented to verify software reliability. Considering the software quality characteristics of the artificial intelligence sector, we study quality evaluation models, diagnose quality, and predict failures. .In this study, we propose an international standard model for artificial intelligence based on the software reliability growth model, present an evaluation model, and present a method for quality diagnosis through the model. In this respect, this study is considered to be important in that it can predict failures in advance and find failures in advance to prevent risks by predicting the failure time that will occur in software in the future. In particular, it is believed that predicting failures will be important in various safety-related software.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.3
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• pp.74-83
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• 2020

Securing excellent pilots is not only directly linked to the military's improved combat capabilities, but also a way to minimize human and property losses from aircraft accidents. Therefore, a scientific method is needed to diagnose pilot aptitude from the pilot selection process and select those with high accident potential, those who are dropped out of the flight training process, and those who are not suitable for pilot life in advance. Developed countries have implemented pilot aptitude tests to solve these problems early on, but so far, the Korean Army has not introduced a pilot aptitude test system that uses diagnostic tools in the helicopter pilot selection process. Therefore, in this study, scientific diagnostic tools are developed for selecting helicopter pilots, and through this, it is predicted that the number of people who are likely to be dropped out of the training course and who have the potential for accidents will be selected in advance and eliminated in the selection process. In this context, prior research examined the key factors involved in the pilot aptitude test. Through this, the aptitude test items were developed and aptitude tests were conducted on student pilots currently in flight training, and the results of flight training were analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6502-6508
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• 2014

The concept of Communities of Practice (CoPs) has been highlighted as an effective method for knowledge sharing in Knowledge Management (KM) and has been utilized strategically by many organizations. Therefore, the need to diagnose knowledge sharing activities in CoPs has increased. Previous studies of CoP strategies has generally suggested broad guidelines without diagnosing the current knowledge sharing status of individual CoPs. Furthermore, diagnosis methodologies are not connected to the strategic direction and require considerable time and effort to conduct regularly. The purpose of this paper was to develop a sustainable diagnosis framework for identifying knowledge sharing activities in virtual CoPs and to suggest strategies for CoPs-based on the proposed diagnosis framework. Finally, the proposed diagnosis framework was applied to an educational service case.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.99-104
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• 2009

The turn-to-turn short is one major fault of the motor faults of BLDC motors and can appear frequently. When the fault happens, the motor can be operated without breakdown, but it is necessary to maintain the motor for continuous working. In past research, several methods have been applied to detect winding faults. The representative approaches have been focusing on current signals, which can give important information to extract features and to detect faults. In this study, current sensors were installed to measure signals for fault detection of BLDC motors. In this study, the Park's vector method was used to extract the features and to isolate the faults from the current measured by sensors. Because this method can consider the three-phase current values, it is useful to detect features from one-phase and three-phase faults. After extracting two-dimensional features, the final feature was generated by using the two-dimensional values using the distance equation. The values were used in fuzzy similarity to isolate the faults. Fuzzy similarity is an available tool to diagnose the fault without model generation and the fault was converted to the percentage value that can be considered as possibility of the fault.