• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.272-278
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• 2011

Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2451-2458
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• 1997

Adaptive channel equalization complished without resorting to a training sequence is known as blind equalization. In this paper, in order to increase the speed of the convergence and to reduce the steady-state mean squared error simulatneously, we propose the multi-stage DD(decision-direct) algorithm derived from the combination of the Sato algorithm and the decision-directed algorithm. In the starting stage, the multi-stage DD algorithm is identical to the Sato algorithm which guarantees the convergence of the equalizer. As the blind equalizer converges, the number of the level of the quantizers is increased gradally, so that the proposed algorithm operates identical to the decision-directed algorithm which leads to the low error power after the convergence. Therefore, the multi-stage DD algorithm obtains fast convergence rate and low steady state mean squared error.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.397-401
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• 2006

In this paper, we proposed a high precision location estimation algorithm using an enhanced decision scheme for RTLS and analyzed its performance in point of an average estimation error distance at 2D coordinates searching area, $300m\times300m$ and LOS propagation environments. Also the performance was compared with that of conventional TDOA algorithm according to the number of available reader and received sub-blink. From the results, we confirmed that the proposed location estimation algorithm using an enhanced decision scheme was able to improve an estimation accuracy even in boundary region of searching area. Moreover, effectively reduced an error distance in entire searching area so that increased the stability of location estimation in RTLS. Therefore, we verified that the proposed algorithm provided a more higher estimation accuracy and stability than conventional TDOA.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2863-2874
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• 1999

Apartment auction is a system that is used for the citizens to get a house. This paper deals with the implementation of a web-based intelligent decision support system using OLAP technique and data mining technique for auction decision support. The implemented decision support system is working on a real auction database and is mainly composed of OLAP Knowledge Extractor based on data warehouse and Auction Data Miner based on data mining methodology. OLAP Knowledge Extractor extracts required knowledge and visualizes it from auction database. The OLAP technique uses fact, dimension, and hierarchies to provide the result of data analysis by menas of roll-up, drill-down, slicing, dicing, and pivoting. Auction Data Miner predicts a successful bid price by means of applying classification to auction database. The Miner is based on the lazy model-based classification algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm and applies the concepts such as decision fields, dynamic domain information, and field weighted function to this algorithm to reflect the characteristics of auction database.

• Journal of the Optical Society of Korea
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• v.14 no.1
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• pp.22-27
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• 2010

Bit error rate (BER) monitoring is the ultimate goal of performance monitoring in all digital transmission systems as well as optical fiber transmission systems. To achieve this goal, optimization of the decision threshold must also be considered because BER is dependent on the level of decision threshold. In this paper, we analyze a pseudo-error counting scheme and propose an algorithm to achieve both BER monitoring and adaptive decision threshold optimization in optical fiber transmission systems. To verify the effectiveness of the proposed algorithm, we conduct computer simulations in both Gaussian and non-Gaussian distribution cases. According to the simulation results, BER and the optimum decision threshold can be estimated with the errors of < 20% and < 10 mV, respectively, within 0.1-s processing time in > 40-Gb/s transmission systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.27-33
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• 2011

In digital communication system that the Modified Constant Modulus Algorithm (MCMA) reduced the use of the adaptive equalization algorithm to combat the Inter-symbol Interference (ISI). MCMA is relatively brief operation. The major point of MCMA that it only achieves moderate convergence rate and steady state mean square error (MSE). In this paper suggest, MCMA equalization improve the performance with parallel structure. It combines Modified Constant Modulus Algorithm(MCMA) and Modified Decision Directed(MDD) algorithm. By exploiting the inherent structural relationship between the 4-QAM signal's coordinates and 16-QAM signal's coordinates, another style of cost function for Modified Constant Modulus Algorithm(MCMA) is defined and If it happen to offset of received signals and MCMA is poor performance in order to overcome this because the paper combines apply for MCMA and MDD(Modified Decision Direct) algorithm. By computer simulation, we confirmed that the proposed PMCMA-MDD algorithm has the fater convergence rate and steady mean square error than the conventional MCMA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.235-238
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• 2000

This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database. On the basis of experiment using semiconductor etching machine, it has been verified that our model outperforms previously proposed decision tree models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1121-1125
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• 2004

This paper is studied the fast block mode decision algorithm for H.264/AVC. The fast block mode decision algorithm is consist of block range decision and merge algorithm. The block range decision algorithm classifies the block over 8$\times$8 size or below for 16$\times$16 macroblock to decide the size and type of sub blocks. The block over 8$\times$8 size is divided into the blocks of 16$\times$8, 8$\times$16 and 16$\times$16 size using merging algorithm which is considered MVD(motion vector difference) of 8$\times$8 block. The sub block range decision reduces encoding arithmetic amount by 48.25% on the average more than the case not using block range decision.

• ETRI Journal
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• v.32 no.4
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• pp.577-587
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• 2010

The layered coding structure of scalable video coding (SVC) with adaptive inter-layer prediction causes noticeable computational complexity increments when compared to existing video coding standards. To lighten the computational complexity of SVC, we present a fast algorithm to speed up the inter-mode decision process. The proposed algorithm terminates inter-mode decision early in the enhancement layers by estimating the rate-distortion (RD) cost from the macroblocks of the base layer and the enhancement layer in temporal, spatial, and inter-layer directions. Moreover, a search range decision algorithm is also proposed in this paper to further increase the motion estimation speed by using the motion vector information from temporal, spatial, or inter-layer domains. Simulation results show that the proposed algorithm can determine the best mode and provide more efficient total coding time saving with very slight RD performance degradation for spatial and quality scalabilities.