• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.418-422
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• 2013

This paper has considered the location estimation problem in two dimension space by using a non-linear filter under non-Gaussian noise in underwater acoustic sensor networks(UASNs). Recently, the extended Kalman filter (EKF) is widely used in location estimation. However, the EKF has a lot of problems in the non-linear system under the non-gaussian noise environment like underwater environment. In this paper, we propose the improved Two-Dimension Particle Filter (TDPF) using the re-interpretation distribution techniques based on the maximum likelihood (ML). Through the simulation, we compared and analyzed the proposed TDPF with the EKF under the non-Gaussian underwater sensor networks. Finally, we determined that the TDPF's result shows more accurate localization than EKF's result.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.335-342
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• 2016

When dynamics changes occurred in an existing Bayesian Network (BN), the related parameters embedding on the BN have to be updated to new parameters adapting to changed patterns. In this case, these parameters have to be updated with the consideration of the causalities in the BN. This research suggests a framework for updating parameters dynamically using Expectation Maximization (EM) algorithm and Harmony Search (HS) algorithm among several Meta-Heuristics techniques. While EM is an effective algorithm for estimating hidden parameters, it has a limitation that the generated solution converges a local optimum in usual. In order to overcome the limitation, this paper applies HS for tracking the global optimum values of Maximum Likelihood Estimators (MLE) of parameters. The proposed method suggests a learning and propagation framework of BN with dynamic changes for overcoming disadvantages of EM algorithm and converging a global optimum value of MLE of parameters.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1635-1645
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• 1999

In this paper, an intelligent resynthesis technique for timing optimization at the architecture-level has been studied. The proposed technique can remedy the problem which may occur in combinational timing optimization techniques applied to circuits which have the hierarchical subblock structure at the architectural-level. The approach first tries to maintain the original hierarchical subblock while minimizing the longest delay of whole circuit. This paper tries to find a new approach to timing optimization for circuits which have hierarchical structure at architectural-level, and has verified its effectiveness experimentally. We claim its usefulness from the fact that most designers design the circuits hierarchically due to the increase of design complexity.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.127-134
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• 2007

There are many researches about the intelligent stock trading systems with the help of the advance of the artificial intelligence such as machine learning techniques, Though the establishment of the reasonable trading policy plays an important role in the performance of the trading systems most researches focused on the improvement of the predictability. Also some previous works, which treated the trading policy, treated the simplified versions dependent on the predictors in less systematic ways. In this paper, we propose the integrated multiple simulation' as a method of optimizing trading performance of stock trading systems. The propose method is adopted in the NXShell a development environment for neural network based stock trading systems. Under the proposed integrated multiple simulation', we simulate the multiple tradings for all combinations of the neural network's outputs and the trading policy parameters, evaluate the learning performance according to the various metrics and establish the optimal policy for a given prediction module based on the resulting performance. In the experiment, we present the trading policy comparison results using the stock value data from the KOSPI and KOSDAQ.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1108-1120
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• 1990

The present study is to develop an intelligent control system for flexible manufacturing system, which is suitable for a variety of manufacturing types with smaller production rates. The controller is designed to integrate heuristic rules with optimization techniques for loading as well as flow rate of parts and ultimately meeting performance indices. The control function implemented by an optimization technique is to calculate short term production rates of parts. The heuristic control determined by production rules requires knowledge base to evaluate selected loading alternatives according to short term production rate and current process information, and also to determine final decision pertaining to loading. In this case, the knowledge base is constructed using the rules for evaluating alternatives, decision criteria, and flow control of parts in manufacturing system. The database is formulated by means of managing and updating current process information. A graphic system to monitor current status of the function and operation of manufacturing system is developed, and computer simulation is carried out to evaluate the performance of the proposed controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.171-177
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• 2011

Accuracy and computing time are considerable issues in machine learning. In general, the computing time for data analysis is increased in proportion to the size of given data. So, we need a sampling approach to reduce the size of training data. But, the accuracy of constructed model is decreased by going down the data size simultaneously. To solve this problem, we propose a new statistical sampling method having similar performance to the total data. We suggest a rule to select optimal sampling techniques according to given data structure. This paper shows a sampling method for reducing computing time with keeping the most of accuracy using cluster sampling, stratified sampling, and systematic sampling. We verify improved performance of proposed method by accuracy and computing time between sample data and total data using objective machine learning data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.297-303
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• 2009

In this paper, we propose the biomedical spectral pattern classification techniques by the fusion scheme based on the SpPCA and MLP in extended feature space. A conventional PCA technique for the dimension reduction has the problem that it can't find an optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback, we extract features by the SpPCA technique in extended space which use the local patterns rather than whole patterns. In the classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, biomedical spectral patterns is classified by the fusion scheme to effectively combine the individual information. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.63-70
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• 2003

Real-time information on vehicle queue at intersections is essential for optimal traffic signal control, which is substantial part of Intelligent Transport Systems (ITS). Computer vision is also potentially an important element in the foundation of integrated traffic surveillance and control systems. The objective of this research is to propose a method for detecting an exact queue lengths at signalized intersections using image processing techniques and a neural network model Fuzzy ARTMAP, which is a supervised and self-organizing system and claimed to be more powerful than many expert systems, genetic algorithms. and other neural network models like Backpropagation, is used for recognizing different patterns that come from complicated real scenes of a car park. The experiments have been done with the traffic scene images at intersections and the results show that the method proposed in the paper could be efficient for the noise, shadow, partial occlusion and perspective problems which are inevitable in the real world images.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.5
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• pp.211-218
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• 2017

Recent robot software platform research focuses on providing intelligent service via abstraction of robot devices. Context-aware techniques are necessary for intelligent robot services, which are based on the perception of environmental information obtained from heterogeneous sensors in IoT environment. Robot Operating System (ROS) provides protocols to operate robot devices. ROS includes functions for abstracting heterogeneous sensors themselves in order to control the robot, however, it lacks the ability to provide context information that the robot can perceive based on environmental information through consistent collection methods. In this paper, we propose a relay system for ROS to provide context-aware robot service. The proposed system makes it possible for ROS to control and provide context-aware robot services with relay of an external context-aware system and ROS. In experiments, we demonstrate procedures that robot services abstracted from ROS and an external context-aware system works together based on the proposed system.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.716-721
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• 2003

This paper deals with the iris recognition as one of biometric techniques which are applied to identify a person using his/her behavior or congenital characteristics. The iris of a human eye has a texture that is unique and time invariant for each individual. First, we obtain the feature vector from the 2D iris pattern having a property of size invariant and divide it into 24 sectors which are further through three types of 2D Gabor filters. At the recognition process, we compute the similarity measure based on the correlation values. Here, since we use three different matching values obtained from three different directional Gabor filters and select the maximum value among them, it is possible to minimize the recognition error rate. To show the usefulness of the proposed algorithm, we applied it to a biometric database consisting of 50 iris patterns extracted from 10 subjects and finally get more higher than 90% recognition rate.