• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.131-136
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• 2003

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the states of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, DNA coding-based intelligent Kalman filter (DNA coding-based IKF) is proposed. The proposed method can overcome the mathematical limits of conventional methods and can effectively track a maneuvering target with only one filter by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and the GA-based IKF in computer simulations.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.2
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• pp.16-34
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• 1991

It has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential - uncertain, imprecise, and occasionally inaccurate - called evidential information. Evidence theory (Dempster/Shafet theory) provides one of the most general framework for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain - such as time, distance, or sensor measurements. However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat's pioneeiring work, this paper provides a new combination rule, a new method for mass function transffrmation, and a new method for rendering joint mass fuctions which are of great utility in evidence theory in the continuous domain.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.46-51
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• 2005

Intelligent Space(ISpace) is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.124-128
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• 2011

Music emotion recognition is currently one of the most attractive research areas in music information retrieval. In order to use emotion as clues when searching for a particular music, several music based emotion recognizing systems are fundamentally utilized. In order to maximize user satisfaction, the recognition accuracy is very important. In this paper, we develop a new music emotion recognition system, which employs a multilabel feature selector and multilabel classifier. The performance of the proposed system is demonstrated using novel musical emotion data.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.22.1-22
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• 2001

In this paper we present Multiple-Channel Active Noise Control[ANC] system by employing Independent Component Analysis[ICA] and Adaptive Network Fuzzy Inference System[ANFIS]. ICA is widely used in signal processing and communication and it use prewhiting and appropriate choice of non-linearities, ICA can separate mixed signal. ANFIS controller is trained with the hybrid learning algorithm to optimize its parameters for adaptively canceling noise. This new method which minimizes a statistical dependency of mutual information(MI) in mixed low frequency noise signal and there is no need to secondary path modeling. The proposed implementations achieve more powerful and stable noise reduction than Filtered-X LMS algorithms which is needed for LTI assumption and precise secondary error

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.6-41
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• 2001

As autonomous mobile robot become more widely used in industry, the importance of navigation system is rising, But eh primary method of locomotion is with wheels, which cause man problems in controlling tracked mobile robots. In this paper, we discuss the used navigation control of tracked mobile robots with multiple sensors. The multiple sensors are composed of ultrasonic wave sensors and vision sensors. Vision sensors gauge distance using a laser and create visual images, to estimate robot position. The 80196 is used at close range and the vision board is used at long range. Data is managed in the main PC and management is distributed to ever sensor. The controller employs fuzzy logic.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.563-572
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• 2007

Transportation infrastructure is critical to economic growth of a country such as China. Careful evaluation of investments in traffic infrastructure projects is therefore pertinent. As traditional evaluation methods do not consider the uncertainty of future cash flows and mobility during project execution, the real option approach is gradually gaining recognition in the context of valuing construction and infrastructure projects. However, many of the cases only evaluate individual options separately although multiple options often exist in a typical large infrastructure project. Using a highway project in China as a case study, this paper first evaluates a deferment option and a growth option embedded in the project. Subsequently, the values are combined using the fuzzy analytical hierarchy process. It is found that the combined value is less than the sum of the two option values. This finding is consistent with the theoretical observations given in past real option literature despite the use of a different approach.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.834-844
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• 2012

Nonlinear electronic loads draw harmonic currents from the power grids that can cause energy loss, miss-operation of power equipment, and other serious problems in the power grids. This paper proposes a harmonic compensation method using multiple distributed resources (DRs) such as small distributed generators (DGs) and battery energy storage systems (BESSs) that are integrated to the power grids through power inverters. For harmonic compensation, DRs should inject additional apparent power to the grids so that certain DRs, especially operated in proximity to their rated power, may possibly reach their maximum current limits. Therefore, intelligent coordination methods of multiple DRs are required for efficient harmonic current compensation considering the power margins of DRs, energy cost, and the battery state-of-charge. The proposed method is based on fuzzy multi-objective optimization so that DRs can cooperate with other DRs to eliminate harmonic currents with optimizing mutually conflicting multi-objectives.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.