• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.170-178
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• 2012

Dipterocarps or Dipterocarpaceae is a commercially important timber producing and dominant keystone tree family in the rain forests of Borneo. Borneo's landscape is changing at an unprecedented rate in recent years which affects this important biodiversity. This paper attempts to model the natural occurrence (distribution including those areas with natural forests before being converted to other land uses as opposed to current distribution) of dipterocarp species in Sarawak which is important for forest biodiversity conservation and management. Local modeling method of Inverse Distance Weighting was compared with commonly used statistical method (Binary Logistic Regression) to build the best natural distribution models for three genera (12 species) of dipterocarps. Database of species occurrence data and pseudoabsence data were constructed and divided into two halves for model building and validation. For logistic regression modeling, climatic, topographical and edaphic parameters were used. Proxy variables were used to represent the parameters which were highly (p>0.75) correlated to avoid over-fitting. The results show that Inverse Distance Weighting produced the best and consistent prediction with an average accuracy of over 80%. This study demonstrates that local interpolation method can be used for the modeling of natural distribution of dipterocarp species. The Inverse Distance Weighted was proven a better method and the possible reasons are discussed.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.427-432
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• 2005

I studied about existent load distribution algorithms and the WLC(Weighted Least Connection) algerian that is using much at present to distribute the connection request of users to real servers efficiently in web cluster system. The efficiency of web cluster system is fallen by load imbalance between servers, because there is problem In inaccurate load status measuring of servers and measuring timing at these load distribution algorithms. In this paper, I suggest an algorithm that distributes load base on various load state of servers by real time using broadcasting RPC(Remote Procedure Call) when a user requests connection, and implement a prototype and experiment its performance. The experiment result shows that load imbalance phenomenon between reai sowers was improved greatly than existing method, and performance of web cluster system was improved by efficiency that response time is shortened.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.537-537
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• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.2B
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• pp.137-144
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• 2002

In this paper, we optimize the base station placement and transmission power using genetic approach. A new representation describing base station placement and transmit power with real number is proposed, and new genetic operators are introduced. This new representation can describe the locations, powers, and number of base stations, Considering coverage, power and economy efficiency, we also suggest a weighted objective function. Our algorithm is applied to an obvious optimization problem, and then it is verified. Moreover, our approach is tried in inhomogeneous traffic distribution. Simulation result proves that the algorithm enables to fad near optimal solution according to the weighted objective function.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.1-9
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• 2003

In this article, we consider a Bayesian estimation method for the geometric mean of $textsc{k}$ exponential parameters, Using the Tibshirani's orthogonal parameterization, we suggest an invariant prior distribution of the $textsc{k}$ parameters. It is seen that the prior, probability matching prior, is better than the uniform prior in the sense of correct frequentist coverage probability of the posterior quantile. Then a weighted Monte Carlo method is developed to approximate the posterior distribution of the mean. The method is easily implemented and provides posterior mean and HPD(Highest Posterior Density) interval for the geometric mean. A simulation study is given to illustrates the efficiency of the method.

• Proceedings of the IEEK Conference
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• summer
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• pp.97-102
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• 2004

The very complicated proplem in spatial processing is effects of phading (Multipath and Delay Spread) and co-channel interference (CCI). The phading is one of principal causes, that form inter-symbol interference (ISI). Spatial-Temproral Adaptive Filter (STAF) has been taken as a solution of this problem, because it can suppress both these types of interference. But the performance of STAF exposes some elemental limitations, in which are the slow convergence of adaptive process and computational complexity. The cause of this is that, STAF must treat a large quantity of information in both space and time. The way that master these limitation is a use of Subband Spatial-Temproral Adaptive Filter (SSTAF). SSTAF reduce computational complexity by pruning off samples of signal and thus it lost some information in time. This draw on attennation of output SINR of SSTAF. The article analyse a optimal solution of this problem by introducing SSTAF with nonuniform weighted distribution.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.258-267
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• 2013

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.802-804
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• 2014

Edge detection of image for performing the road lane recognition is an essential preprocessing. Various studies are being performed in order to detect such edge and there are conventional edge detection methods such as Sobel, Prewitt and Roberts. Such methods regardless of pixel distribution are processed by applying the same weighted value to the entire pixels and show a somewhat insufficient edge detection results. Therefore, this paper has proposed an algorithm that detects the edge using the suitable weighted value for the road lane recognition considering the pixel distribution shape of the image.

• Proceedings of the ESK Conference
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• 2004.10a
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• pp.7-7
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• 2004

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.129-142
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• 2024

In various industries, especially manufacturing and chemical industries, it is often observed that the distribution of a specific process, initially having followed a normal distribution, becomes skewed as a result of unexpected causes. That is, a process deviates from a normal distribution and becomes a skewed distribution. The skew-normal (SN) distribution is one of the most employed models to characterize such processes. The shape of this distribution is determined by the asymmetry parameter. When this parameter is set to zero, the distribution is equal to the normal distribution. Moreover, when there is a shift in the asymmetry parameter, the mean and variance of a SN distribution shift accordingly. In this paper, we propose procedures for monitoring the asymmetry parameter, based on the statistic derived from the noncentral t-distribution. After applying the statistic to Shewhart and the exponentially weighted moving average (EWMA) charts, we evaluate the performance of the proposed procedures and compare it with previously studied procedures based on other skewness statistics.