• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.759-768
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• 2000

The ZG-machine is a space-efficient G-machine, which exploits a simple encoding method, called tag-forwarding, to compress the heap structure of graphs. Experiments on the ZG-machine without garbage collection shows that the ZG-machine saves 30% of heap space and the run-time overhead is no more than 6% than the G-machine. This paper presents the results of further experiments on the ZG-machine with the garbage collector. As a result, the heap-residency of the ZG-machine decreases by 34% on average although the run-time increases by 34% compared to the G-machine. The high rate of the run-time overhead of the ZG-machine is incurred by the garbage collector. However, when the heap size is 7 times the heap-residency, the run-time overhead of the ZG-machine is no more than 12% compared to the G-machine. With the aspect of reduced heap-residency, the ZG-machine may be useful in memory-restricted environments such as embedded systems. Also, with the development of a more efficient garbage collector, the run-time is expected to decrease significantly.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.167-173
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• 2002

The look up table method conventionally applied to control the inner temperature and humidity of a laver drying machine has repeatedly occurred not only laver's damage but also inferior goods since the reaching time at the optimum state takes a long time. In this paper, a fuzzy control theory instead of the look up table was proposed to reduce the reaching time at the optimum state. The proposed method used six input variables and four output variables for the fuzzy control, and a triangle rule for a fuzzifier, The Mandani's min-max method was applied to a fuzzy inference. Also, the mean method of maximum was applied to a defuzzifier. The method applied to the fuzzy controller contributed to reduce the reaching time at the optimum state, and to minimize not only laver's damage but also inferior goods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.446-449
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• 1995

In this paper, the accurate end position control method of ultraprecision machine tool post using piezoelectric material as an micro positonong devics is presented. This method employs the classical PID feedback and uses an additional notch filter which eliminates the resonance characteristics of controlled plant. And the simple predictor is added to make use of the future value of desired input for better tracking performance. To show the feasibilty of proposed method, the PC-based experimental apparacy can be obtained. Using method, Al specimen of diameter 100mm was cut under practical machining condition to test the practicability of proposed method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.3
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• pp.443-450
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• 1989

A digital testing method for measurement of radial error motions of a spindle is investigated with special emphasis on developing a computer-aided in-situ inspection for machine tool manufacturing. The method utilizes three non-contact type probes and an optical encoder, based on a special computational algorithm to eliminate undesirable offset and roundness errors of the master spindle. Details of the design of hardware and software required to realize the testing method are described. Finally, advantages and limitations of the method are discussed with several test results.

• Journal of information and communication convergence engineering
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• v.17 no.4
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.187-193
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• 2003

This paper presents an on-machine measurement method of flatness error fur surface machining processes. There are two kinds of on-machine measurement methods available to measure flatness errors in workpieces: i.e., surface scanning method and sensor scanning method. However, motion errors are often engaged in both methods. This paper proposes an idea to realize a measurement system of flatness errors and its rigorous application for estimation of motion errors of the positioning system. The measurement system is made by modifying the straightness measurement system, which consists of a laser, a CCD camera and processing system, a sensor head, and some optical units. The sensor head is composed of a retroreflector, a ball and ball socket, a linear motion guide unit and adjustable arms. The experimental .results show that the proposed method is useful to identify flatness errors of machined workpieces as well as motion errors of positioning systems.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.18-25
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• 2022

The cold staring issue of permanent magnet synchronous motors (PMSM) is a chronic problem in the field of PMSM sensorless drives. A traditional starting method, called the I-F method, is widely adopted because of its simple structure. However, when using this method, the pre-defined magnitude and frequency of the starting current should be changed according to the condition of the load and machine inertia. In this paper, a smart and simple algorithm for the cold starting of PMSM is proposed. In the proposed method, an integrated control angle from the estimated electrical rotor speed is used for vector control such as the indirect vector control of the induction machine. Thus, very stable cold starting is performed regardless of the machine load condition or inertia changing.

• IE interfaces
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• v.11 no.3
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• pp.23-40
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• 1998

In this paper, a method to determine the optimal configuration of operating policies in an integrated-automated manufacturing system using the Taguchi method and computer simulation experiments is presented. An integrated-automated manufacturing system called direct-input-output manufacturing system(DIOMS) is described. We only consider the operational aspect of the DIOMS. Four operating policies including input sequencing control, dispatching rule for the storage/retrieval(S/R) machine, machine center-based part type selection rule, and storage assignment policy are treated as design factors. The number of machine centers, the number of part types, demand rate, processing time and the rate of each part type, vertical and horizontal speed of the S/R machine, and the size of a local buffer in the machine centers are considered as noise factors in generating various manufacturing system environment. For the performance characteristics, mean flow time and throughput are adopted. A robust design experiment with inner and outer orthogonal arrays are conducted by computer simulation, and an optimal configuration of operating policies is presented which consists of a combination of the level of each design factor. The validity of the optimal configurations is investigated by comparing their signal-to-noise ratios with those obtained with full factorial designs.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.927-934
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• 2017

As the technology of drone develops, the use of drone is increasing, In addition, the types of sensors that are inside of smart phones are becoming various and the accuracy is enhancing day by day. Various of researches are being progressed. Therefore, we need to control drone by using smart phone's sensors. In this paper, we propose the most suitable machine learning model that matches the gyro sensor data with drone's moving. First, we classified drone by it's moving of the gyro sensor value of 4 and 8 degree of freedom. After that, we made it to study machine learning. For the method of machine learning, we applied the One-Rule, Neural Network, Decision Tree, and Navie Bayesian. According to the result of experiment that we designated the value from gyro sensor as the attribute, we had the 97.3 percent of highest accuracy that came out from Naive Bayesian method using 2 attributes in 4 degree of freedom. On and the same, in 8 degree of freedom, Naive Bayesian method using 2 attributes showed the highest accuracy of 93.1 percent.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.361-370
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• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.