• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.192-197
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• 2000

In CNC machining, a free curve is cut into small linear segments using the linear interpolation(G01) method. Therefore, the interpolation error along the curve is not constant due to the changing curvature. This paper presents a NURBS (Non-Uniform Rational B-Spline) interpolation algorithm for machining free curves with high precision and high speed. The proposed NURBS interpolation defines the tool path with NURBS parameters and limits the interpolation error to any desired level by adjusting the feed rate considering the curvature of the shape and sampling time. Both linear and NURBS interpolations are compared to show the validity of the proposed algorithm.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.199-208
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• 2008

Tagging represents one of the Web 2.0 technology, and has an appropriate mechanism for the classification of dynamically changing Web informations. This technique is capable of searching the Web informations using the user specified tags, but still it has a limitation of providing only the limited informations to the tags. Therefore, in order to search the related informations easily, we need to extend this technique further to search not only the desired informations through the designated tags and also the related informations. In this paper, we first have designed and developed an algorithm that can get a desired tag cluster, which is capable of collecting the searched tags along with the related tags. We first performed a test to compare the difference between the user collected tag data through RSS and the reduced data. The second test focused on the accuracy of extracted related tags that depends on the similarity functions, such as the Pearson Correlation and Euclidean. Finally, we showed the final results visually using the graph algorithm.

• Proceedings of the KIPE Conference
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• 2015.11a
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• pp.21-22
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• 2015

The maximum power point of a photovoltaic array alters with changing atmospheric conditions, temperature conditions, shadow conditions, so it is required to track maximum power point. As much as MPPT(Maximum Power Point Tracking) is important in photovoltaic systems, many MPPT techniques have been developed. In this paper, several major existing MPPT methods are comparatively analyzed and novel hybrid MPPT algorithm is proposed. The proposed hybrid MPPT algorithm is developed in combination with traditional MPPT methods to complement each other for improving performance and mitigating partial shading effects. The proposed algorithm is validated by using PISIM simulation tool and experiment in 3kW system.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.6
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• pp.381-388
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• 2018

A novel and simple algorithm for accurate calculation of RMS voltage is proposed in a digitally controlled grid-tie inverter system. Given that the actual frequency of grid voltage is continuously changing, the constant sampling frequency cannot be a multiple number of the fundamental frequency. Therefore, the RMS of grid voltage contains periodic oscillations due to the differences in the phase angle of sampled data during calculation. The proposed algorithm precisely calculates and updates the initial phase angle of the first sampled voltage in a half-cycle period using phase-locked loop, which is commonly utilized for phase angle detection in grid-tie inverter systems. The accuracy and dynamic performance of the proposed algorithm are compared with those of other algorithms through various simulations and experiments.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.3
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• pp.75-91
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• 2005

The popularization of the internet has produced an explosion in amount of the information. The importance of web personalization is being more and more increased. The personalization is realized by learning user's interest. User's interest is changing continuously and rapidly. We use user's profile to represent user's interest. User's profile is updated to reflect the change of user's interest. In this paper we present an adaptive learning algorithm that can be used to reflect user's interest that is changing with time. We propose the User's profile model. With this profile user's interest is learned based on user's feedback. This approach has applied to develop hybrid recommendation system.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.378-387
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• 2007

To enhancing the exercise effect, exercise management systems are introduced and generally used. They create the proper exercise program through exercise prescription after determining the personal body status. When the exercise programs are created, they will consider $2weeks{\sim}3months$ period. And, existing exercise programs cannot respect with personal exercise habits or exercise period which are changing variedly. If exercise period is long, it can be caused inappropriate exercise about user current status. To solve these problems in legacy systems, this paper proposes a Context Aware Exercise Model (CAEM) to provide the exercise program considering the user context. Also, we implemented that as Intelligent Fitness Guide (IFG) System. The IFG system is selectively received necessary measurement values as input values according to user's context. If exercise kinds, frequency and strength of user are changing, that system creates the exercise program through exercise optimization algorithm and exercise knowledge base. As IFG is providing the exercise program in a real time, it can be managed the effective exercise according to user context.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.6
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• pp.89-99
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• 2008

In this paper proposes a novel tacking algorithm regarding the power loss when operating a tracking system for a rapidly changing insolation to improve the power of PV hacking system. The tracking system of sensor method used in a conventional PV power station is unable to exactly track a sun position when lacking in the intensity of radiation and has the problem is malfunction of tracking system by a rapidly changing climatic. The tracking system of program method spends too much energy on the unnecessary operation of tracking system because that is unable to adapt itself to a outside factor of climatic environment. In case of tracking an azimuth and altitude of the sun in realtime, therefore, the actual PV power is less increasing than the power of tracking system fixed a specific position. To reduce the power loss, this pap proposes a novel control algorithm of the tracking system. Also, this paper is analyzed efficiency of traditional solar tracking method and proposed method, prove validity of proposed algorithm through demonstrable study.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.113-124
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• 2009

This paper suggests a new approach for providing information for driver safety at signalized intersections. Particularly dangerous situations at signalized intersections such as red-light violations, accelerating through yellow intervals, red-light running, and stopping abruptly due to the dilemma zone problem are considered in this study. This paper presents the development of a dangerous vehicle determination algorithm by collecting real-time vehicle speeds and times from multiple point detectors when the vehicles are traveling during phase-change. For an evaluation of this algorithm, VISSIM is used to perform a real-time multiple detection situation by changing the input data such as various inflow-volume, design speed change, driver perception, and response time. As a result the correct-classification rate is approximately 98.5% and the prediction rate of the algorithm is approximately 88.5%. This paper shows the sensitivity results by changing the input data. This result showed that the new approach can be used to improve safety for signalized intersections.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.