• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.2
• /
• pp.246-255
• /
• 1998

This paper presents a novel method for real-time malfunction detection of plasma etching process using EPD signal traces. First, many reference EPD signal traces are collected using monochromator and data acquisition system in normal etching processes. Critical points are defined by applying differentiation and zero-crossing method to the collected reference signal traces. Critical parameters such as intensity, slope, time, peak, overshoot, etc., determined by critical points, and frame attributes transformed signal-to symbol of reference signal traces are saved. Also, UCL(Upper Control Limit) and LCL(Lower Control Limit) are obtained by mean and standard deviation of critical parameters. Then, test EPD signal traces are collected in the actual processes, and frame attributes and critical parameters are obtained using the above mentioned method. Process malfunctions are detected in real-time by applying SPC(Statistical Process Control) method to critical parameters. the Real-time malfunction detection method presented in this paper was applied to actual processes and the results indicated that it was proved to be able to supplement disadvantages of existing quality control check inspecting or testing random-selected devices and detect process malfunctions correctly in real-time.

• ETRI Journal
• /
• v.43 no.4
• /
• pp.640-649
• /
• 2021

In this paper, an additional degree of freedom in phased multi-input multi-output (phased-MIMO) radar with any arbitrary desired covariance matrix is proposed using space-time codes. By using the proposed method, any desired transmit covariance matrix in MIMO radar (phased-MIMO radars) can be realized by employing fully correlated base waveforms such as phased-array radars and simply extending them to different time slots with predesigned phases and amplitudes. In the proposed method, the transmit covariance matrix depends on the base waveform and space-time codes. For simplicity, a base waveform can be selected arbitrarily (ie, all base waveforms can be fully correlated, similar to phased-array radars). Therefore, any desired covariance matrix can be achieved by using a very simple phased-array structure and space-time code in the transmitter. The main advantage of the proposed scheme is that it does not require diverse uncorrelated waveforms. This considerably reduces transmitter hardware and software complexity and cost. One the receiver side, multiple signals can be analyzed jointly in the time and space domains to improve the signal-to-interference-plus-noise ratio.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.12 no.1
• /
• pp.428-439
• /
• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.11
• /
• pp.3001-3010
• /
• 1999

The most of intrusion detection methods do not detect intrusion on real-time because it takes a long time to analyze an auditing data for intrusions. To solve the problem, we are studying a real-time intrusion detection. Therefore, this paper proposes an agent model using multi warning level for real-time intrusion detection. It applies to distributed environment using an extensibility and communication mechanism among agents, supports a portability, an extensibility and a confidentiality of IDS.

• Proceedings of the KIPE Conference
• /
• 2011.07a
• /
• pp.461-462
• /
• 2011

In this paper, a static overmodulation method using the effective time to control the overmodulation is proposed. The effective time is derived from actual switching time interval. The proposed method reduces the complex operations such as complicated gating time. The experimental results have been simulated in MATLAB/SIMULINK.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.994-998
• /
• 2005

A new control method, called a simple model matching, has been recently developed by the author. This is very simple and be applied for linear and nonlinear discrete time systems with/without time lag. Based on this formulation, identification is examined in this paper using an interconnected neural network with the EBP-EWLS learning algorithm. With this result, a control method is also presented for a nonlinear discrete time system.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.178-180
• /
• 2007

In this paper, a real-time transmission algorithm based on IEEE 802.15.4 is proposed. The superframe of IEEE 802.15.4 is applied to the transmission method of real-time mixed data (periodic data, sporadic data, and non real-time message). The simulation results show the real-time performance of sporadic data is improved by using the proposed transmission algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.294-297
• /
• 1996

This paper proposes the method to recognize of time series data based on the chaotic feature extraction. Features extract from time series data using the chaotic time series data analysis and the pattern recognition process is using a neural network classifier. In experiment, EEG(electroencephalograph) signals are extracted features by correlation dimension and Lyapunov experiments, and these features are classified by multilayer perceptron neural networks. Proposed chaotic feature extraction enhances recognition results from chaotic time series data.

• Journal of Aerospace System Engineering
• /
• v.3 no.2
• /
• pp.24-30
• /
• 2009

MI LV1B images, geometric corrected data of COMS MI, has no time information per each line, but field of weather prediction using the MI LV1B images needs time information on it. This paper explains two calculation methods for receiving time on lines of MI LV1B images and analyzes difference between two calculation methods using simulated data.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 1998.11a
• /
• pp.162-165
• /
• 1998

A certain degree of energy saving can be possible by controlling the delay time of occupancy sensor. In this paper a control circuit is designed for automatic control of delay time setting appropriate to different situations using a digital counter, two latches and an adder. The delay time is controlled by adjusting the time constant of RC circuit through on-off control of switching devices according to adder output, which determines the base current level of switching devices. And from PSpice simulation it is verified to function properly.