• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1272-1272
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• 2001

Eutrophication processes of aquatic environment are strictly correlated with the concentration levels of nitrogen, phosphorous, organic matter and biological parameters such as phytoplankton and chlorophylla (Tremel, 1996; Burns et al., 1997; Young et al. 1999; Wei et al.,2000). Accordingly, the monitoring and evaluation of these factors will provide useful information about the health of aquatic ecosystem. However, the traditional types of auqatic chemistry analysis and ecological monitoring of phytoplankton are time-consuming, costly, and further resulting in secondary pollution due to the use of reagents. NIR (near-infrared) spectroscopy, as a rapid, non-destructive, little sample preparation and reagents-free technology (Hildrum et al., 1992), has been extensively applied to the characterization of food (Osborne and Fearn, 1988), pharmaceutical (Morisseau and Rhodes, 1995) and textile materials (Clove et al.,2000). Currently, NIR technology has been used indirectly in inferring lake water chemistry by two approaches, suspended (Malley et al., 1996) or seston (Dabakk et al., 1999), and sediments (Korsman et al., 1992; Malley et al., 1999). In addition, the evaluation of trophic state and the identification of the key factors contributed to the trophication are the key step to restore the damaged aquatic environment. Moreover, an understanding of the factors, which regulate the algal proliferation, is crucial to the successful management of aquatic ecosystem. In the paper, NIR technology will be used to study the environmental factors affecting the algal proliferation in combination with the trophic state index and diversity index. This novel developed system can be applied in monitoring and evaluating allopathic water environment and provide real time information services for the aquatic environment management.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.211-229
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• 2004

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

• Journal of Astronomy and Space Sciences
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• v.14 no.1
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• pp.126-135
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• 1997

We installed a pair of geomagnetic ground station in Ichon branch of Radio Research Laboratory(Ichon station, N37.1447, E127.5509) and Kyunghee University(Yongin station, N37.1419, E127.0454). We have successfully finished test operation, and we are now setting up a data base for the real time monitoring of the geomagnetic field. We are also going to have another geomagnetic station for the southward direction at Chejuisland(Cheju University) in summer of 1997. By that time, we will have a complete set of geomagnetic data base for the near earth solar-terrestrial environment in real time. In this paper, we compare and analyze the results of geomagnetic field observations from our stations, Kakioka observatory, Wind and Geotail satellites when the coronal mass ejections(CME) occurred on Dec. 2, 1996.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Proceedings of the KOR-KST Conference
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• 2000.02a
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• Journal of the Korea Safety Management & Science
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• v.4 no.3
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• pp.95-105
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• 2002

This paper considers the sequencing of products in car assembly lines under Just-In-Time systems. Under Just-In-Time systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. In this paper, tabu search technique for this problem is proposed. Tabu search is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality and computation time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.148-153
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• 2023

A digital controller uses a microprocessor and is a controller implemented as a program. This method has the advantage of being more maintenance-friendly than existing analog controllers. However, it inevitably requires computation time to execute the internal program. Therefore, the digital controller uses a method of controlling the system at a certain cycle by considering this time, and this cycle is very closely related to the performance of the microprocessor used. In other words, in the case of very high performance, this control cycle can be shortened to near real time, but this may result in a disadvantage in terms of cost. In this paper, we propose a method to solve this problem by implementing two processors with slightly lower performance in a control system in a series-parallel structure. For this purpose, we will use a digital distributed control system and implement an experimental system to examine its effectiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.765-773
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• 2009

Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2000.11a
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• pp.1-14
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• 2000

The Near Infrared region of the energy spectrum was first discovered by Hershel in the year 1800. The principles of NIR is based on light absorption of specific organic chemical bonds. The absorption at each wavelength is measured and a spectre is obtained. The spectre is then treated mathematically and with the absorption data is converted to absolute units via a calibration. In the last two decades it has developed dramatically. With the invention of computers and the ability to treat a large amount of data in a very short time the use of NIR for many different purposes has developed very fast. During the last decade with the aid of very powerful PC's the application of NIR technology has become even more widespread. Now or days development of very robust calibrations can be done in a relatively short time with a minimum of resources. The use of Near Infrared Spectroscopy (NIR) in the Meat industry is relatively new. The first installations were taken into operation in the 80ties. The Meat Industry in often referred to as rather conservative and slow to embrace new technologies, they stay with the old and proven methods. The first NIR instruments used by the Meat Industry, and most other industries, were multipurpose build, which means that the sample presentation was not well suited to this particular application, or many other applications for that sake. As the Meat Industry grows and develops to meet the demands of the modern markets, they realise the need for better control of processes and final products. From the early 90 ties and onward the demand for 'rear time' rapid results starts growing, and some suppliers of NIR instruments (and instruments based on other technologies, like X-ray) start to develop and manufacture instrumentation dedicated to the particular needs of the Meat Industry. Today it is estimated that there are approximately 2000 rapid instruments placed in the Meat industry world-wide. By far most of these are used as at-line or laboratory installations, but the trend and need is moving towards real on-line or in-line solutions. NIR is the most cost effective and reproducible analytical procedure available for the twenty first century.