• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.757-763
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• 2018

It is very difficult to achieve sufficient data rate and required quality of service due to the time-varying nature of the radio channel and various jammers such as path loss, delay, Doppler, shadowing and interference. Especially, the propagation path between the transmitting antenna and the tracking antenna mounted on the fuselage during the test and evaluation of the projectile system considered in this paper is based on the rapid movement of the projectile, the interference due to multipath fading due to the terrain, The propagation path may be blocked. In order to effectively improve the multipath fading occurring in the wireless communication system, a diversity combiner technique is required. In this paper, to derive the design and improvement schemes for the space diversity combiner technique among the diversity combiner schemes, the BER performance of maximum ratio combining (MRC) and selection combining (SC) In an adaptive SC / MRC diversity combiner that operates with MRC when it is lower than the specified threshold criterion when comparing the SNR between two signals received from the channel and operates with SC at high and combines the two received signals The BER performance of the system was compared and analyzed.

• Nuclear Engineering and Technology
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• v.19 no.4
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• pp.233-241
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• 1987

Difficulties are encountered when the behavior of complex systems (i.e., fuel failure probability) that have unreliable deterministic models is predicted. For more realistic prediction of the behavior of complex systems with limited observational data, the present study was undertaken to devise an approach of combining predictions from the deterministic model and actual observational data. Predictions by this method of combining are inferred to be of higher reliability than separate predictions made by either model taken independently. A systematic method of hierarchical pattern discovery based on the method developed in the SPEAR was used for systematic search of weighting factors and pattern boundaries for the present method. A sample calculation was performed for prediction of CANDU fuel failures that had occurred due to power ramp during refuelling process. It was demonstrated by this sample calculation that there exists a region of feature space in which fuel failure probability from the PROFIT model nearly agree with that from observational data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.367-377
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• 2019

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1272-1277
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• 2003

This paper presents a method for combining classifier which is constructed by fuzzy and neural network classifiers and uses classifier fusion algorithms and selection algorithms. The input space of combing classifier is divided by the extended hyperbox region proposed in this paper to guarantee non-overlapped data property. To fuse the fuzzy classifier and the neural network classifier, we propose the fusion parameter for the overlapped data. In addition, the adaptive learning algorithm also proposed to maximize classifier performance. Finally, simulation examples are given to illustrate the effectiveness of the method.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.351-362
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• 1997

This study has been performed to develop a program which can help the management of the entrusted farming company(EFC). An algorithm for machinery cost estimation and computer programs were developed and those were tested with sample data of EFC for the fm size of 50 ha. The results of the test showed for the farm size of 50ha that tillage cost was 18, 785 thousand won ; 23, 441 thousand won for the transplanting, 24, 904 thousand won for the combining, and 4, 024 thousand won for drying. An algorithm for the critical entrusted In analysis and a computer program were developed and those were tested with data estimated. The results showed that tillage fee was 376 thousand won per ha, 496 thousand won for transplanting 495 thousand won for combining and, 32, 480 won per ton for drying. The algorithms and a computer program were developed for the analysis of the critical optimum working area when the entrusted working fee was provided.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.949-952
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• 2002

A regression model is used in predicting the response variable given predictor variables However, in case of large number of predictor variables, a regression model has some problems such as multicollinearity, interpretation of the functional relationship between the response and predictors and prediction accuracy. A clustering method and stepwise regression could be used to reduce the amount of data by grouping predictors having similar properties and by selecting the subset of predictors. respectively. This paper proposes a prediction method combining clustering method and stepwise regression. The proposed method fits a global model and local models and predicts responses given new observations by using both models. The paper also compares the performance of proposed method with stepwise regression via a real data of ample obtained in a steel process.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1145-1155
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• 2007

In this paper, we consider the permutation tests for the multivariate data under the two-sample problem setting. We review some testing procedures, which are parametric and nonparametric and compare them with the permutation ones. Then we consider to try to apply the permutation tests to the multivariate data having the continuous and discrete components together by choosing some suitable combining function through the partial testing. Finally we discuss more aspects for the permutation tests as concluding remarks.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.114-118
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• 1986

The study was conducted to find out the gene effects on litter size and birth weights in mice with 362 progenies from full-diallel crosses of four lines of BALB/c, CBA, C3H and C57BL. The progenies were farrowed at the Experimental Animal Farm, College of Agriculture, Kyungpook National University in November, 1984, and data were analyzed into general combining ability, specific combining ability and reciprocal effects with Griffing's model. General combining ability effects estimated in line-crosses were -0.4163~0.3337 for litter size and -0.0356~0.0894 for birth weights. However, no significant differences were observed in general combining ability effects on litter size and birth weights. Specific combining ability effects estimated in line-crosses were -1.0388~1.7913 for litter size and -0.1144~0.1343 for birth weights. However, the specific combining ability effects for litter size and birth weights appeared to be insignificant. The reciprocal effects, which appeared to be significant, were -2.26 from BALB/c ${\times}$ C3H, 1.84 from CBA ${\times}$ C57BL and -1.50 from BALB/c ${\times}$ CBA for litter size. For birth weights, the reciprocal effects were estimated -0.26 from CBA ${\times}$ C57BL, 0.15 from BALB/c ${\times}$ CBA and -0.15 from BALB/c ${\times}$ C57BL.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.5
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• pp.384-393
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• 2015

This paper proposes an optimized classifier based on feature elimination (OCFE) for gene selection with combining two feature elimination methods, ReliefF and SVM-RFE. ReliefF algorithm is filter feature selection which rank the data by the importance of the data. SVM-RFE algorithm is a wrapper feature selection which wrapped the data and rank the data based on the weight of feature. With combining these two methods we get less error rate average, 0.3016138 for OCFE and 0.3096779 for SVM-RFE. The proposed method also get better accuracy with 70% for OCFE and 69% for SVM-RFE.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.387-388
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• 2006

In this paper, we propose an efficient algorithm based on PCA and LDA combining K-means clustering method, which has better accuracy of face recognition than Eigenface and Fisherface. In this algorithm, PCA is firstly used to reduce the dimensionality of original face image. Secondly, a truncated face image data are sub-clustered by K-means clustering method based on Euclidean distances, and all small subclusters are labeled in sequence. Then LDA method project data into low dimension feature space and group data easier to classify. Finally we use nearest neighborhood method to determine the label of test data. To show the recognition accuracy of the proposed algorithm, we performed several simulations using the Yale and ORL (Olivetti Research Laboratory) database. Simulation results show that proposed method achieves better performance in recognition accuracy.