• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.3
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• pp.279-287
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• 2010

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• The Journal of Korean Association of Computer Education
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• v.23 no.1
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• pp.37-44
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• 2020

As the importance of artificial intelligence(AI) education is emphasized recently, policies and researches are being promoted to develop the AI curriculum or courses for K-12 students in worldwide. In this study, researcher analysed a synthesis of contents and standards on AI education curriculum to present implications for AI education in the elementary and secondary schools. As a result, Korea and the United States are proposing national curriculum standards to provide the basis for AI curriculum establishment in school sites and to provide guidelines for various related policies such as teacher training programs. The EU's AI education is characterized by its curriculum and online courses to ensure that all citizens of the EU have AI literacy, rather than designating students or subjects at specific school levels. In terms of educational contents and levels, Korea, United States, and EU's curriculum or standards includes basics and applications related to machine learning and neural network based on the fundamental concepts and principles of artificial intelligence.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.327-332
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• 2009

In this paper, we propose the diagnosis system that can predict pet's state of health for pet lovers lacking a technical knowledge of dog-diseases. The proposed system deduces diseases of dogs from input symptoms by our database constructed with 105 kinds of diseases and symptoms. First, a disease is clustered by ART2, the self-learning method in neural network and secondly, the result values, outputs and the weight values clustered by the algorithm are stored to database. Finally, our system diagnoses the state of health by means of comparing the learned information of diseases with the input vectors of each symptom and the related results of questions on diseases. The correct information of diseases and symptom diagnosing is important to predict the state of health of dogs. Therefore, in this paper, the proposed system can manage symptoms and diseases efficiently by database and ART2. We ask veterinary specialist with the efficiency of our system. As a result, we could confirm the possibility as the auxiliary diagnosis system for dog diseases.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Journal of the Korean housing association
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• v.23 no.4
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• pp.59-67
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• 2012

Developing the modern design of Hanok and providing support for the commercialization model development in recent years propelled by the New Hanok Support Strategies of the central government in conjunction with the New Hanok revitalization related projects reflecting local goverments. New Hanok revitalization, the rekindling and revaluing of human behaviors and interests in local goverments following the social and cultural changes of the past decades, has emeraged as an increasingly traditional area of concerning in New Hanok planning. In this paper we attempt to this discussion by describing recent projects in New Hanok revitalization in Jeollanam-do Province. Therefore, this study aims to examine the classification of compound knowledges based multidimensional relationship by using Self-Organizing Maps (SOM). SOM is an unsupervised learning neural network model for the analysis of high-dimensional input data. By using SOM, we were able to create a cluster map reflecting the characteristics of the New Hanok. In this case the pattern of the preference data was easily understood by visual analysis. Liking for compound knowledge deduced from this data was classified into 8 categories according to the compound knowledge properties of New Hanok. As a result, a systematic approach for analysis the characteristics of individual family and living environment of New Hanoks and 10 space usage patterns the changes in some aspects of New Hanok.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.304-308
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• 2010

In this paper, we propose a new method of recognizing the vehicle license plate using color space, morphological characteristics and ART2 algorithm. Morphological characteristics of old and/or new style vehicle license plate among the candidate regions are applied to remove noise areas using 8-directional contour tracking algorithm, then follow by the extraction of vehicle plate. From the extracted license plate area, plate morphological characteristics of each region are removed. After that, labeling algorithm to extract the individual characters are then combined. The classified individual character and numeric codes are applied to the ART2 algorithm for the learning and recognition. In order to evaluate the performance of our proposed extraction and recognition of vehicle license method, we have run experiments on 100 green plates and white plates. Experimental results shown that the proposed license plate extraction and recognition method was effective.

• Journal of Ocean Engineering and Technology
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• v.18 no.6 s.61
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• pp.79-83
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• 2004

The curved blocks which compose the bow and stem of a ship contain many skewed joints that are inclined horizontally and vertically. Most of these joints have a large fitness error and are continuously changing their form and are not easily accessible. The welding position and parameter values should be appropriately set in correspondence to the shape and the inclination of the joints. The welding parameters such as current, voltage, travel speed, and melting rate, are related to each other and their values must be in a specific limited range for the sound welding. These correlations and the ranges are dependent up on the kind and size of wire, shielding gas, joint shape and fitness. To determine these relationships, extensive welding experiments were performed. The experimental data were processed using several information processing technologies. The regression method was used to determine the relationship between current voltage, and deposition rate. When a joint is inclined, the weld bead should be confined to a the limited size, inorder to avoid undercut as well as overlap due to flowing down of molten metal by gravity. The dependency of the limited weld size which is defined as the critical deposited area on various factors such as the horizontally and vertically inclined angle of the joint, skewed angle of the joint, up or down welding direction and weaving was investigated through a number of welding experiments. On the basis of this result, an ANN system was developed to estimate the critical deposited area. The ANN system consists of a 4 layer structure and uses an error back propagation learning algorithm. The estimated values of the ANN were validated using experimental values.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.758-766
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• 2017

In this paper, we propose a robust duality of CNN(Du-CNN) method which can classify the target and clutter in coastal environment for IR Imaging Sensor. In coastal environment, there are various clutter that have many similarities with real target due to diverse change of air temperature, water temperature, weather and season. Also, real target have various feature due to the same reason. Thus, the proposed Du-CNN method adopts human's multiple personality utilization and CNN technique to learn and classify target and clutter. This method has an advantage of the real time operation. Experimental results on sampled dataset of real infrared target and clutter demonstrate that the proposed method have better success rate to classify the target and clutter than general CNN method.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.408-411
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• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2180-2188
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• 2009

This paper is study on real-time hand gesture recognition system based on vision for intelligent robot control. We are proposed a recognition system using PCA and BP algorithm. Recognition of hand gestures consists of two steps which are preprocessing step using PCA algorithm and classification step using BP algorithm. The PCA algorithm is a technique used to reduce multidimensional data sets to lower dimensions for effective analysis. In our simulation, the PCA is applied to calculate feature projection vectors for the image of a given hand. The BP algorithm is capable of doing parallel distributed processing and expedite processing since it take parallel structure. The BP algorithm recognized in real time hand gestures by self learning of trained eigen hand gesture. The proposed PCA and BP algorithm show improvement on the recognition compared to PCA algorithm.