• Journal of Bio-Environment Control
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• v.13 no.2
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• pp.90-95
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• 2004

This study was conducted to develop an algorithm for the embryo location in seed by using machine vision. The topic of this research is to detect the embryo location in seed regardless of seed supply direction. In order to detect the embryo location in Cham Bak, Tuktojwa and Hukjong, the effect of seed posture in the supply line was investigated. When the seed posture angle of Chambak from horizontal direction was $30^{\circ}$, the detection accuracy for embryo location was 77.8%, while detection accuracy was 100% for the $0^{\circ}$ or $15^{\circ}$. When seed posture angle of Tuktojwa was $30^{\circ}$ from the horizontal direction, the detection accuracy was 89.5% and it was 100% for the $0^{\circ}$ and $15^{\circ}$. Embryo location detection accuracy for the Hukjong was 94.4% when the seed posture angle from the horizontal direction is $30^{\circ}$, and it was 100% for the $0^{\circ}$ and $15^{\circ}$. When seeds are fed into the posturing and seeding line, the seed postures within $30^{\circ}$ with mechanical means, and at most $15^{\circ}$ seed stand posture. the developed algorithm can detect the embryo position in the seed. So, this embryo detection system is very useful tool in the posturing and seeding line.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.454-461
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• 1999

A concrete floor trowel machine, developed in the U.S in 1990's, consists of only two rotary trowels, and doesn't need any other mechanism for motion such as wheels. When the machine flattens a concrete floor with its rotary trowels, the machine can move in any direction by utilizing the unbalanced friction forces occurring between the rotary wheels and the floor when the trowels are tilted in appropriate directions. In order to automate the trowels machine, this paper proposed the self-propulsive concrete finishing trowel robot which has twin trowels. For the control of the robot, this paper discussed the following. Firstly, the dynamics model of the driving frictional force applied on each trowel from the floor is derived. Secondly, the relationship between the driving force for the robot and the control variable of the robot is derived. Finally, the basic motion of the robot are realized by using the obtained relationship. This paper figures out how the concrete floor finishing robot with tow trowels moves and will contribute to realizing it.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.2
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• pp.208-213
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• 2012

In this paper, the radial error of a rotary table at five-axis machine tool is evaluated by utilizing ISO 230-2 and estimation method using double ball-bar. The geometric error of a rotary table is defined as position dependent geometric errors or position independent geometric errors according to their physical character. Then estimation method of geometric errors using double ball-bar is simply summarized including measurement path, parametric modeling and least squares approach. To estimate representative radial error, offset error, set-up error which affect to the double ball-bar data, mean value of measured data including CCW/CW-direction are used at estimation process. Radial errors are separated from measured data and used for evaluation with ISO 230-2. Finally, suggested evaluation method is applied to a rotary table at five-axis machine tool and its result is analyzed to improve the accuracy of the rotary table.

• Advances in robotics research
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• v.1 no.2
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• pp.155-169
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• 2014

Study results in the last decades show that amount and quality of physical exercises, then the active participation, and now the cognitive involvement of patient in rehabilitation training are known of crux to enhance recovery outcome of motor dysfunction patients after stroke. Rehabilitation robots mainly have been developing along this direction to satisfy requirements of recovery therapy, or focusing on one or more of the above three points. Therefore, neuro-machine interaction based active rehabilitation robot has been proposed for assisting paralyzed limb performing designed tasks, which utilizes motor related EEG, UCSDI (Ultrasound Current Source Density Imaging), EMG for rehabilitation robot control and feeds back the multi-sensory interaction information such as visual, auditory, force, haptic sensation to the patient simultaneously. This neuro-controlled and perceptual rehabilitation robot will bring great benefits to post-stroke patients. In order to develop such kind of robot, some key technologies such as noninvasive precise detection of neural signal and realistic sensation feedback need to be solved. There are still some grand challenges in solving the fundamental questions to develop and optimize such kind of neuro-machine interaction based active rehabilitation robot.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.145-150
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• 2005

This paper presents an Electrotactile Display System (ETCS). One of the most important human sensory systems for human computer interaction is the sense of touch, which can be displayed to human through tactile output devices. To realize the sense of touch, electrotactile display produces controlled, localized touch sensation on the skin by passing small electric current. In electrotactile stimulation, the mechanoreceptors in the skin may be stimulated individually in order to display the sense of vibration, touch, itch, tingle, pressure etc. on the finger, palm, arm or any suitable location of the body by using appropriate electrodes and waveforms. We developed an ETCS and investigated effectiveness of the proposed system in terms of the perception of roughness of a surface by stimulating the palmar side of hand with different waveforms and the perception of direction and location information through forearm. Positive and negative pulse trains were tested with different current intensities and electrode switching times on the forearm or finger of the user with an electrode-embedded armband in order to investigate how subjects recognize displayed patterns and directions of stimulation.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.11-17
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• 2019

In recent years, damage and loss of life and property have been occurred frequently due to swelling waves in the East Sea. Swelling waves are not easy to predict because they are caused by various factors. In this research, we build a model for predicting the swelling waves occurrence in the East Coast of Korea using machine learning technique. We collect historical data of unloading interruption in the Pohang Port, and collect air pressure, wind speed, direction, water temperature data of the offshore Pohang Port. We select important variables for prediction, and test various machine learning prediction algorithms. As a result, tide level, water temperature, and air pressure were selected, and Random Forest model produced best performance. We confirm that Random Forest model shows best performance and it produces 88.86% of accuracy

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.137-143
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• 2022

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.

• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.1-7
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• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.467-468
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• 2021

In order to improve the performance of the indoor positioning system using BLE beacons, a receiver that measures the angle of arrival among the direction finding technologies supported by BLE5.1 was manufactured and analyzed by machine learning to measure the optimal position. For the creation and testing of machine learning models, k-nearest neighbor classification and regression, logistic regression, support vector machines, decision tree artificial neural networks, and deep neural networks were used to learn and test. As a result, when the test set 4 produced in the study was used, the accuracy was up to 99%.