• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.219-228
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• 2013

The surveillance imaging equipments are functioning to observe the shape of the target in real time or to measure its location precisely. The roles of such equipments are becoming more important in today's weapon systems.The aforementioned imaging equipments can be classified based on the modes of operations such as fixed, installed on cars, or composite of those. Also, according to different concepts of sensor operation, a separate type uses independent housing for each sensor whereas in a composite type a set of multiple sensors are housed into a unit altogether. The sensors in general have magnetism, thereby introducing the possible negative effects, particularly in the composite types, in locating the reference position, which is carried out by the digital compass. The use of shielding material/housing could be an option but results in increased weight and reduced portability, restricting its use in composite type equipments. As such, the objective of this paper is to study on how to reduce such magnetic effects on the position location. To do so, in the absence of magnetic shielding, a variety of sensor positions were first modeled. By combing the result with the fact that the functions of PAN & Tilt are used in the equipments, a new position location algorithm is proposed. The use of the new algorithm can automate the position location process as compared to the manual process of the existing approach. In the algorithm developed, twelve locations are measured in connection with both the azimuth and elevation angles in comparison to the six locations alone around the azimuth angle. As a result, it turns out that the measurement range has been widened but the measurement time reduced. Also, note that the effect of errors the operators may make during measurement could be reduced.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.95-101
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• 2008

Modeling of stock prices forecast has been considered as one of the most difficult problem to develop accurately since stock prices are highly correlated with various environmental conditions including economics and political situation. In this paper, we propose a agent system approach to predict Korea Composite Stock Price Index (KOSPI) using neural network and statistical methods. To minimize mean of prediction error and variation of prediction error, agent system includes sub-agent modules for feature extraction, variables selection, forecast engine selection, and forecasting results analysis. As a first step to develop agent system for KOSPI forecasting, twelve economic indices are selected from twenty two basic standard economic indices using principal component analysis. From selected twelve economic indices, prediction model input variables are chosen again using best-subsets regression method. Two different types data are tested for KOSPI forecasting and the Prediction results showed 11.92 points of root mean squared error for consecutive thirty days of prediction. Also, it is shown that proposed agent system approach for KOSPI forecast is effective since required types and numbers of prediction variables are time-varying, so adaptable selection of modeling inputs and prediction engine are essential for reliable and accurate forecast model.