• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.1
• /
• pp.184-191
• /
• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.20 no.9
• /
• pp.90-96
• /
• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.6
• /
• pp.2465-2471
• /
• 2013

This paper develops a travel time prediction algorithm that can be used for real-time application. The algorithm searches for the most similar pattern in historical travel time database as soon as a series of real-time data become available. Artificial neural network approach is then taken to forecast travel time in the near future. To examine the performance of this algorithm, travel time data from Gyungbu Highway were obtained and the algorithm is applied. The evaluation shows that the algorithm could predict travel time within 4% error range if comparable patterns are available in the historical travel time database. This paper documents the detailed algorithm and validation procedure, thereby furnishing a key to generating future travel time information.

• Industrial Engineering and Management Systems
• /
• v.14 no.3
• /
• pp.312-317
• /
• 2015

The dynamic system approach in time series has been used in many real problems. Based on Taken's embedding theorem, we can build the predictive function where input is the time delay coordinates vector which consists of the lagged values of the observed series and output is the future values of the observed series. Although the time delay coordinates vector from multivariate time series brings more information than the one from univariate time series, it can exhibit statistical redundancy which disturbs the performance of the prediction function. We apply dimension reduction techniques to solve this problem and analyze the effect of this approach for prediction. Our experiment uses delayed Lorenz series; least squares support vector regression approximates the predictive function. The result shows that linearly preserving projection improves the prediction performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 1986.10a
• /
• pp.216-224
• /
• 1986

This paper treats the modelling and the control of the burnthrough point control system for an industrial sintering process. First, a state-space model is derived by defining new unconventional variables. A simple control law is proposed, which consists of the modified receding horizon control law and the least-squares prediction algorithm. The stability and the tracking properties of this control law are proved. The real-time experiments are carried out in a POSCO sintering plant and satisfactory results are presented in this paper. Before the real-time experiments, computer simulations are done and their results are also given for the comparison with the real-time experiments.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.25 no.2
• /
• pp.119-124
• /
• 1988

Real-time adaptive lattice predictor was implemented on the TMS32020 DSP chip for digital signal processing. The implemented system was composed of Input-Output units and centrla processing-control unit and its supporting assembly soft ware. The performance of hardware realization was verified by comparing input signal and one-step prediction signal which are calcualted by the real-time adaptive lattice predictor. As a result, for 4 stage lattice structure, the maximum running frequency was obtained as 6.41 KHz in this experiment.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.310-318
• /
• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.6
• /
• pp.766-773
• /
• 2021

This paper presents an implementation result and performance evaluation of pavilion management services that does not only provide real-time status of the pavilion in the forest but also prediction services through machine learning. The developed hardware prototype detects whether the pavilion is occupied using a motion detection sensor and then sends it to a cloud database along with location information, date and time, temperature, and humidity data. The real-time usage status of the collected data is provided to the user's mobile application. The performance evaluation confirms that the average response time from the hardware module to the applications was 1.9 seconds. The accuracy was 99%. In addition, we implemented a pavilion availability prediction service that applied a machine learning-based SVM (Support Vector Model) model to collected data and provided it through mobile and web applications.

• Journal of Power System Engineering
• /
• v.6 no.1
• /
• pp.55-60
• /
• 2002

There has been no report on the life prediction for gas turbine materials at high temperatures based on the creep properties and their relationship with the AE(acoustic emission) properties as a means of real-time non-destructive testing. One of the important issues is thus to develop a reliable method of evaluating creep properties by the AE technique. In this paper, the real-time evaluation of high temperature creep time and AE cumulative counts for nickel-based superalloy Udimet 720 was performed on round-bar type specimens under pure load at the temperatures of 811, 922 and 977K. The total AE cumulative counts until the starting point of secondary creep($N_1$) and that of tertiary creep($N_2$) have quantitative relationship with the tertiary creep time and the rupture time. It is thus possible to construct the life prediction system based on creep and the prevention system of tertiary creep by using AE technique.

• Journal of Environmental Impact Assessment
• /
• v.19 no.6
• /
• pp.617-623
• /
• 2010

It is the most important subject to figure out characteristics of the wastewater inflows of sewerage treatment plant(STP) when situation models are applied to operation of the biological processes and in the automatic control based on ICA(Instrument, Control and Automation). For the purposes, real-time influent monitoring method has been applied by using on-line monitoring equipments for the process optimization in conventional STP. Since, the influent of STP is consist of complex components such as, COD, BOD, TN, $NH_4$-N, $NO_3$-N, TP and $PO_4$-P. MRA2(Microbial Respiration Analyzer 2), which is capable of real-time analyzing of wastewater characteristics is used to overcome the limitations and defects of conventional online monitoring equipments in this study. Rapidity, accuracy and stability of developed MRA2 are evaluated and compared with the results from on-line monitoring equipments for seven months after installation in Full-scale STP.