• Journal of the Speleological Society of Korea
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• no.79
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• pp.77-81
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• 2007

In this paper deals with behavior of the magnetic abrasive using Sr-Ferrite on polishing charateristiccs in a internal finishing of staninless steel pipe a tying magnetic abrasive polishing. The magnetic polishing is the useful method to finish some machinery fabrications by using magnetic power. This method is one of the precision techniques and has in aim for clean technology in the transportation of the pure gas in the clean pipes. The magnetic abrasive polishing method is not so common in the field of machine that it is not known to widely. There are rarely researcher in this field because of non-effectiveness of magnetic abrasive. Therefore, in this paper we deals with the development of the magnetic abrasive with the use of Sr-Ferrite. In this development, abrasive grain SiC has been made by using the resin bond fabricated at low temperature. And magnetic abrasive powder was fabricated from the Sr-Ferrite which was crushed into 200 mesh. The XRD analysis result shows that only SiC abrasive and Sr-Ferrite crystal peaks were detected, explaining that resin bond was not any more to contribute chemical reaction. From MACRO analysis, we found that SiC abrasive and Sr-Ferrite were strongly bonding with each other.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.8
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• pp.1840-1846
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• 2005

In this paper, does by purpose DC servo motor speed controller design about PID coefficient tuning techniques that use genetic algerian. DC servo motor is used in application field of a peat many control machine or robot etc. and in this field, selection of controller parameters requires user's expert knowledge. Therefore, general amount of work engineers must continuously iteration tuning in controller parameters by trial and error. With this, when must tuning parameter coefficient about change of dynamic system or disturbance, can improve the efficiency according to following that is more precised and parameter coefficient value that is optimized by using genetic algorithm. In this paper, from dynamic character modeling get in analyze dynamic character of DC motor desist controller drive control possible that is fast response character md improved speed precision using a Genetic Algorithms.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.101-106
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• 2006

The geoidal undulations are needed for determining the orthometric heights from the Global Positioning System GPS-derived ellipsoidal heights. There are several methods for geoidal undulation determination. The paper presents a method employing a simple architecture Two Layer Multiquadric-Biharmonic Artificial Neural Network (TLMB-ANN) to approximate an area of 4200 square kilometres quasigeoid surface with GPS-levelling data. Hardy’s Multiquadric-Biharmonic functions is used as the hidden layer neurons’ activation function and Levenberg-Marquardt algorithm is used to train the artificial neural network. In numerical examples five surfaces were compared: the gravimetric geometry hybrid quasigeoid, Support Vector Machine (SVM) model, Hybrid Fuzzy Neural Network (HFNN) model, Traditional Three Layer Artificial Neural Network (ANN) with tanh activation function and TLMB-ANN surface approximation. The effectiveness of TLMB-ANN surface approximation depends on the number of control points. If the number of well-distributed control points is sufficiently large, the results are similar with those obtained by gravity and geometry hybrid method. Importantly, TLMB-ANN surface approximation model possesses good extrapolation performance with high precision.

• Design & Manufacturing
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• v.11 no.2
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• pp.20-24
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• 2017

Recent industrial developments are constantly advancing, and rapid technological development is demanding high technology level in related fields. The need for polishing is increasing even more to improve quality. In order to improve the surface quality, the final finishing process or polishing process is a very important part. Research on super precise polishing method using MR fluid is actively being carried out in domestic and foreign countries. Fine magnetic abrasive grains are aligned in the direction of a magnetic force line formed by a magnetic field and serve as a brush to polish a metal surface. This method has the advantage that the shape of the tool is not fixed and is not affected by the shape of the workpiece or the machining area. We will design the electromagnets for the MR polish polishing system and apply the magnetic field analysis using the magnetic field analysis program (ANSYS). The data obtained through this process suggests an efficient method to increase the magnetic flux density important for polishing. We will investigate the influence of the Al6061-T6 specimen on the surface of the MR polishing machine based on the optimized design.

• Journal of the Korean Society of Safety
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• v.23 no.5
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• pp.43-48
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• 2008

The magnetic polishing is the useful method to finish some machinery fabrications by using magnetic power. This method is one of the precision polishing techniques and has an aim for clean technology in the transportation of the pure gas in the clean pipes. The magnetic abrasive polishing method is not so common in the field of machine that it is not known to widely. There are only few researchers in this field because of non-effectiveness of magnetic abrasive. Therefore, in this paper deals with development of the magnetic abrasive using Ba-Ferrite. In this development, abrasive grain GC and CBN has been made by using the resin bond fabricated at low temperature. And magnetic abrasive powder was fabricated from the Ba-Ferrite which was crushed into 200 mesh. The XRD analysis result shows that only GC, CBN and Ba-Ferrite crystal peaks were detected, explaining that resin bond was not any more to contribute chemical reaction. From SEM analysis, we found that GC, CBN abrasive and Ba-Ferrite were strongly bonding with each other.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1103-1125
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• 2017

This paper aims to extract an ObjectProperty-UsageMethod relation, in particular the HerbalMedicinalProperty-UsageMethod relation of the herb-plant object, as a semantic relation between two related sets, a herbal-medicinal-property concept set and a usage-method concept set from several web documents. This HerbalMedicinalProperty-UsageMethod relation benefits people by providing an alternative treatment/solution knowledge to health problems. The research includes three main problems: how to determine EDU (where EDU is an elementary discourse unit or a simple sentence/clause) with a medicinal-property/usage-method concept; how to determine the usage-method boundary; and how to determine the HerbalMedicinalProperty-UsageMethod relation between the two related sets. We propose using N-Word-Co on the verb phrase with the medicinal-property/usage-method concept to solve the first and second problems where the N-Word-Co size is determined by the learning of maximum entropy, support vector machine, and naïve Bayes. We also apply naïve Bayes to solve the third problem of determining the HerbalMedicinalProperty-UsageMethod relation with N-Word-Co elements as features. The research results can provide high precision in the HerbalMedicinalProperty-UsageMethod relation extraction.

• Advances in robotics research
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• v.1 no.1
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• pp.81-99
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• 2014

This paper contributes towards the development of a computer vision system for telemonitoring of industrial articulated robotic arms. The system aims to provide precision real time measurements of the joint angles by employing low cost cameras and visual markers on the body of the robot. To achieve this, a mathematical model that connects image features and joint angles was developed covering rotation of a single joint whose axis is parallel to the visual projection plane. The feature that is examined during image processing is the varying area of given circular target placed on the body of the robot, as registered by the camera during rotation of the arm. In order to distinguish between rotation directions four targets were used placed every $90^{\circ}$ and observed by two cameras at suitable angular distances. The results were deemed acceptable considering camera cost and lighting conditions of the workspace. A computational error analysis explored how deviations from the ideal camera positions affect the measurements and led to appropriate correction. The method is deemed to be extensible to multiple joint motion of a known kinematic chain.

• Food Science and Industry
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• v.54 no.3
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• pp.145-159
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• 2021

Conventional nutrition services involve producer-oriented approaches without considering the differences in the characteristics and circumstances of each individual, whereas personalized nutrition services are consumer-oriented concepts that provide products and services for maintaining optimal health conditions based on the genetic, physiological, and metabolic characteristics of individuals, with these products based on balanced nutrition and healthy living. Currently, methods for evaluating dietary habits, monitoring dietary behaviors, deep phenotyping, and metabotyping via microbiota profiling, as well as methods for predicting big data by using machine learning, have been previously studied in Korea and abroad. With the development of medical technology and the improvement of hygiene, the demand for personalized nutrition and health services for healthier, happier, and more satisfying lives is rapidly increasing. Therefore, based on scientific technologies, attempts are needed to advance these services into global personalized markets and to boost the global competitiveness of countries and companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.