• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.348-353
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• 2013

Recently, control theory including stochastic optimal control and various machine-learning-based artificial intelligence methods have become major tools in the field of financial engineering. In this paper, we briefly review some recent papers utilizing stochastic optimal control theory in the fields of the pair trading for mean-reverting markets and the trend-following strategy, and consider a couple of strategies utilizing both stochastic optimal control theory and machine learning methods to acquire more flexible and accessible tools. Illustrative simulations show that the considered strategies can yield encouraging results when applied to a set of real financial market data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.62
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• pp.69-77
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• 2001

A current design trend of a vending machine is pursuing easy manufacturing and large capacity of holding cans. Therefore this study aims to ergonomically redesign vending machine and to analyze the motion of awkward posture to take out cans. Using Vision 3000 system, after investigating which motion can affect users, a new ergonomically designed vending machine, which was based on anthropometric data and guideline for control panel and exit of cans. New design shows a significantly improved usability and less stress. With new ergonomically designed vending machine at figure 3 can give a benefit for both users and manufactures.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.579-583
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• 1991

A fuzzy logic controller with rule modification capability is proposed to overcome the difficulty of obtaining control rules from the human operators. This new SOC algorithm modifies control rules by a fuzzy inference machine utilizing data base. Computer simulation results show good performances on both a linear system and a nonlinear system.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.1
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• pp.1-9
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• 2006

Three categories such as the design of a machine, control and software are necessary in the development of the photomask inspection machine with high resolution. Among them, the design of a software detects inferiority through the comparison of CAD data and real data read by camera from photomask. The block matching algorithm is used since the domain is large and the comparison of data by pixel is accomplished. To correct the error arising from the assembly of a machine, calibration algorithm is used and prefocusing algorithm is suggested to correct the surface of the photomask.

• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.354-362
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• 2010

This paper presents a non-linear control chart based on support vector machine regression (SVM-R) to improve the accuracy of fault detection of cyclic signals. The proposed algorithm consists of the following two steps. First, the center line of the control chart is constructed by using SVM-R. Second, we calculate control limits by variances that are estimated by perpendicular and normal line of the center line. For performance evaluation, we apply proposed algorithm to the industrial data of the chemical vapor deposition process which is one of the semiconductor processes. The proposed method has better fault detection performance than other existing method

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.1-7
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• 2012

This paper presents a system used to automatically recognize the road traffic control gestures of police officers. In this approach,the control gestures of traffic police officers are captured in the form of depth images.A human skeleton is then constructed using a kinematic model. The feature vector describing a traffic control gesture is built from the relative angles found amongst the joints of the constructed human skeleton. We utilize Support Vector Machines (SVMs) to perform the gesture recognition. Experiments show that our proposed method is robust and efficient and is suitable for real-time application. We also present a testbed system based on the SVMs trained data for real-time traffic gesture recognition.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.4
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• pp.24-31
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• 2011

Double-color Injection molding machine is the assembly of many kinds of mechanical, fluid power part and electric electronic control system. From in these, fluid power is a part where becomes the first core of this machine. Fluid power systems of double-color injection molding machine are modelled and analyzed using a commercial program AMESim. Partial system models which is divided according to functional operation are made and its analysis results shows how design parameters work on operational characteristics like pressure, flow rates, displacement at each node and so on. Analysis modeling and compared the data which gets from experiment and the analysis result which has a reliability got data. The results made by analysis will be used design of fluid power circuit for developing a double-color injection molding machine.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2246-2248
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• 1998

To control the remote system in the unstructured environment requires data under certain circumstances. When a machine is dealt with an unstructured environment, new environment structure is to be composed. The stereo vision system can get both the intensity data and the range data. So, in this paper, data architecture of a stereo image is proposed to set them.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.