• Journal of the Korean Society for Precision Engineering
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• v.19 no.7
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• pp.171-179
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• 2002

STEP-NC is the next generation CNC controller taking STEP-based data model as the interface scheme (or language) between CAM and CNC, and carrying out various intelligent functions. At the moment, efforts are being made worldwide to establish international standard for the new interface scheme formalized as ISO14649. As the new language is being established, increasing attention is being paid to the development of the new CNC. Korea STEP-NC is an integrated STEP-NC system taking ISO 14649 as an input, and carrying out various intelligent functions. It is composed of 5 modules: 1) Shop Floor Programming System (PosSFP), 2) Tool Path Generator (PosTPG), 3) Tool Path Viewer (PosTPV), 4) Man Machine Interface (PosMMI), and 5) CNC Kernel (PosCNC). Distinguished from other prototypes (of Europe and USA), the Korea STEP-NC is top-down designed, and bottom-up implemented comprehensively incorporating all the crucial components for realizing the full benefit of STEP-NC paradigm, without using any existing commercial CAD/CAM systems and CNC kernels. The Korea STEP-NC prototype was successfully demonstrated and evaluated in the ISO conventions Together with prototypes of Europe and USA, Korea STEP-NC will be used as a reference system fur the Triangular Conformance Test to be jointly carried out by ISO TC184 SC1, SC4, and IMS Project.

• Journal of Computational Design and Engineering
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• v.1 no.1
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• pp.27-36
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• 2014

Similar to the essential components of many mechanical systems, the geometrical properties of the teeth of spiral bevel gears greatly influence the kinematic and dynamic behaviors of mechanical systems. Logarithmic spiral bevel gears show a unique advantage in transmission due to their constant spiral angle property. However, a mathematical model suitable for accurate digital modeling, differential geometrical characteristics, and related contact analysis methods for tooth surfaces have not been deeply investigated, since such gears are not convenient in traditional cutting manufacturing in the gear industry. Accurate mathematical modeling of the tooth surface geometry for logarithmic spiral bevel gears is developed in this study, based on the basic gearing kinematics and spherical involute geometry along with the tangent planes geometry; actually, the tooth surface is a parametric surface defined on a parallelogrammic domain. Equivalence proof of the tooth surface geometry is then given in order to greatly simplify the mathematical model. As major factors affecting the lubrication, surface fatigue, contact stress, wear, and manufacturability of gear teeth, the differential geometrical characteristics of the tooth surface are summarized using classical fundamental forms. By using the geometrical properties mentioned, manufacturability (and its limitation in logarithmic spiral bevel gears) is analyzed using precision forging and multiaxis freeform milling, rather than classical cradle-type machine tool based milling or hobbing. Geometry and manufacturability analysis results show that logarithmic spiral gears have many application advantages, but many urgent issues such as contact tooth analysis for precision plastic forming and multiaxis freeform milling also need to be solved in a further study.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.97-105
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• 2004

The reason that an intelligent agent cannot understand the representation of its own perception or activity is caused by the traditional syntactic approach that translates a semantic feature into a simulated string, To implement an autonomously learning intelligent agent, Cohen introduces a experimentally semantic approach that the system learns a contentful representation of physical schema from physically interacting with environment using its own sensors and effectors. We propose that negation is a meta-level schema that enables an agent to recognize its own physical schema, To improve the planner's efficiency, Graphplan introduces the control rule that manipulates the inconsistency between planning operators, but it cannot cognitively understand negation and suffers from redundancy problem. By introducing a negative function not, IPP solves the problem, but its approach is still syntactic and is inefficient in terms of time and space. In this paper, we propose that, to represent a negative fact, a positive atom, which is called opposite concept, is a very efficient technique for implementing an cognitive agent, and demonstrate some empirical results supporting the hypothesis.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.101-115
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• 2021

Access re-review is a valuable and important task, but it is burdensome for archivists. Thus, an access re-review automation was proposed to address this. In this situation, the National Archives of Korea actually utilized the access re-review solution in the performance of the "2020 Access Re-Review Project" and compared and analyzed it with human work. The project was, however, not a research project centered on analysis on access re-review solutions, and it has a limited result in terms of experimental use of commercial programs. Nevertheless, in the current situation where there are only macro and superficial discussions on access re-review of intelligent archives, it would be meaningful to apply the access re-review solution to archivists in real businesses and examine the results. This paper seeks to discuss the practicality that can mitigate the task of access re-review through an analysis of use cases of access re-review solutions.

• 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 Korea Entertainment Industry Association
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• v.15 no.4
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• pp.349-357
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• 2021

Recently, drones are rapidly becoming common and expanding. There is a great need for diversity in whether drone flight data can be used as entertainment technology analysis data. In particular, it is necessary to check whether it is possible to analyze and utilize the flight and operation process of entertainment drones, which are developing through autonomous and intelligent methods, through data analysis and machine learning. In this paper, it was confirmed whether it can be used as a machine learning technology by using FC data in the evaluation of drones for entertainment. As a result, FC data from DJI and Parrot such as Mavic2 and Anafi were unable to analyze machine learning for entertainment. It is because data is collected at intervals of 0.1 second or more, so that it is impossible to find correlation with other data with GCS. On the other hand, it was found that machine learning technologies can be applied in the case of Fixhawk, which used an ARM processor and operates with the Nuttx OS. In the future, it is necessary to develop technologies capable of analyzing the characteristics of entertainment by dividing fixed-wing and rotary-wing flight information. For this, a model shoud be developed, and systematic big data collection and research should be conducted.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.71-92
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• 2010

As the use of trading systems increases recently, many researchers are interested in developing intelligent trading systems using artificial intelligence techniques. However, most prior studies on trading systems have common limitations. First, they just adopted several technical indicators based on stock indices as independent variables although there are a variety of variables that can be used as independent variables for predicting the market. In addition, most of them focus on developing a model that predicts the direction of the stock market indices rather than one that can generate trading signals for maximizing returns. Thus, in this study, we propose a novel intelligent trading system that mitigates these limitations. It is designed to use both the technical indicators and the other non-price variables on the market. Also, it adopts 'two-threshold mechanism' so that it can transform the outcome of the stock market prediction model based on support vector machines to the trading decision signals like buy, sell or hold. To validate the usefulness of the proposed system, we applied it to the real world data-the KOSPI200 index from May 2004 to December 2009. As a result, we found that the proposed system outperformed other comparative models from the perspective of 'rate of return'.

• Journal of KIISE:Software and Applications
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• v.27 no.6
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• pp.642-650
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• 2000

The pilot (human actor) involved in the control loop of the highly automated aircraft systems (machine actor) must be able to monitor these systems just as the machine actor must also be able to monitor the human actor. For its safety and better performance of the human machine system, each of the two elements must be knowledgeable about the other's intentions or goals. In fact, several recent accidents occurred due to goal conflicts between human and machines in a modern avionic system. To facilitate the coordination of these actors, a computational aid was developed. The aid, which operates in a part-task simulator environment, attempts to facilitate the management of the goals and functions being performed to accomplish them. To provide an accurate knowledge of both actors' goals and their function statuses, the aid uses agent-based objects representing the elements of the cockpit operations. This paper describes the development of the flightdeck goals and functions called Agenda Management.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.675-682
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• 2004

With the five-day workweek system in bank and the increased usage of ATM(Automatic Toller Machine), it is required that the financial crime using stolen credit card should be prevented. Though a CCTV camera is usually installed in near ATM, an intelligent criminal can cheat it disguising himself with sunglass or mask. In this paper, we propose facial feature verification system which can detect whether the user's face can be Identified or not, using image processing algorithm and SVM(Support Vector Machine). Experimental results show that FAR(Error Rate for accepting a disguised man as a non-disguised one) is 1% and FRR(Error Rate for rejecting a normal/non-disguised man as a disguised one) is 2% for training data. In addition, it shows the FAR of 2.5% and the FRR of 1.43% for test data.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.873-877
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• 2010

CAPTCHA is a security tool that prevents the automatic sign-up by a spam or a robot. This CAPTCHA usually depends on the smart readability of humans. However, the common and plain CAPTCHA with text-based system is not difficult to be solved by intelligent web-bot and machine learning tools. In this paper, we propose a new sub-image based CAPTCHA system totally different from the text based system. Our system offers a set of cropped sub-image from a whole digital picture and asks user to identify the correct orientation. Though there are some nice machine learning tools for this job, but they are useless for a cropped sub-images, which was clearly revealed by our experiment. Experiment showed that our sub-image based CAPTCHA is easy to human solver, but very hard to all kinds of machine learning or AI tools. Also our CAPTCHA is easy to be generated automatical without any human intervention.