• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.531-539
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• 2009

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2E
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• pp.5-11
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• 1995

In this paper, a method using a neural network was applied for the purpose of urilizing spatial features. The adopted model of neural network the three-layered architecture, and the training algorithm is the back-propagation algorithm. Co-occurrence matrix which is generated from original imge was used for imput pattern to the neural network in order to tolerate variations of patterns like rotation of displacement. Co-occurrence matrix is explained in appendix. To evaluate this method, classification was executed with this method and texture features method over the city area and sand area, which cannot be separated with the conventional method mentioned aboved. In the results of this method and texture features proposed by Haralick the method using texture features was separation rate of 67%~89%. On the contrary, the method using neural network proposed in this research was stable and high separation rate of 80%~98%.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.58-70
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• 2007

The purpose of this study is to predict future land use using Landsat images through assessing the effect of spatial scale and resolution in applying CA (Cellular Automata)-Markov technique. The scale for areas ranging from $31.26km^2$ to $84.48km^2$ showed about 11% difference of overall accuracies. Among the five spatial resolutions (10m, 30m, 50m, 100m, 150m), 30m resolution showed the best result in the prediction of area and spatial distribution of urban areas. Based on the results, the 2004 land use by CA-Markov was predicted using 1996 and 2001 land use data and compared with the 2004 land use by maximum likelihood classification. After that, future land uses of 2030, 2060 and 2090 were predicted and the results showed a tendency of gradual increase in urban area and high decrease in forest area.

• Parasites, Hosts and Diseases
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• v.59 no.2
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• pp.139-148
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• 2021

This study was carried out to provide information on the taxonomic classification and analysis of mitochondrial genomes of Spirometra theileri. One strobila of S. theileri was collected from the intestine of an African leopard (Panthera pardus) in the Maswa Game Reserve, Tanzania. The complete mtDNA sequence of S. theileri was 13,685 bp encoding 36 genes including 12 protein genes, 22 tRNAs and 2 rRNAs with absence of atp8. Divergences of 12 protein-coding genes were as follow: 14.9% between S. theileri and S. erinaceieuropaei, 14.7% between S. theileri and S. decipiens, and 14.5% between S. theileri with S. ranarum. Divergences of 12 proteins of S. theileri and S. erinaceieuropaei ranged from 2.3% in cox1 to 15.7% in nad5, while S. theileri varied from S. decipiens and S. ranarum by 1.3% in cox1 to 15.7% in nad3. Phylogenetic relationship of S. theileri with eucestodes inferred using the maximum likelihood and Bayesian inferences exhibited identical tree topologies. A clade composed of S. decipiens and S. ranarum formed a sister species to S. erinaceieuropaei, and S. theileri formed a sister species to all species in this clade. Within the diphyllobothridean clade, Dibothriocephalus, Diphyllobothrium and Spirometra formed a monophyletic group, and sister genera were well supported.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.306-315
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• 2011

The study defines the job characteristics anxiety of the private security workers and the effect of the performance. While the study selected the private guards working at the private security companies registered at the National Police Agency in Seoul 2011 and sampled total 300 people by using the judgment sampling method, the final input case number is 266 people. The study used alpha value of the reliability analysis and the maximum-likelihood classification of the covariation structure analysis in order to verify the validity of the survey and the reliability. With the research method and the process the result of the study is as follows. First, the task importance of the private security workers affects the minus influence to the characteristic anxiety. Second, the feedback of the private security workers affects the minus influence to the characteristic anxiety. Third, the job autonomy of the private security workers affects the minus influence to the characteristic anxiety. Fourth, the feedback of the private security workers affects the plus influence to the task performance. Fifth, the job autonomy of the private security workers affects the plus influence to the task performance. Sixth, the skill variety of the private security workers affects the plus influence to the task performance. Seventh, the characteristic anxiety of the private security workers affects the plus influence to the task performance.

• Korean Journal of Construction Engineering and Management
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• v.17 no.4
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• pp.57-65
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• 2016

This study found a proper parametric life distribution based on maintenance history data of each bridge member under the jurisdiction of the Korea Expressway Corporation for the past 10 years by introducing the concept of reliability and suggested a measure to calculate the mean life and reliability of each bridge member using the parameter obtained with the maximum-likelihood classification. As a result of analyzing the exponential distribution, weibull distribution and log normal distribution being utilized frequently in order to find the parametric life distribution type which well described the life data of each bridge member, it was found that the log normal distribution and weibull distribution described the characteristics of the relevant life data the best. As a result of calculating the mean life of each bridge member based on the estimated parameter, the average life of the steel bridge coating was 18.51 years which was the longest, followed by the bridge deck as 17.56 years. The mean life of the drainage facility and the bridge bearing were 12.27 years and 12.57 years respectively, showing the shortest life.

• The Korea Journal of Herbology
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• v.33 no.3
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• pp.25-35
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• 2018

Objectives : Lepidii seu Descurainiae Semen has been frequently adulterated with the seeds of several inauthentic plant species. However, the accurate identification of these plant seeds is very difficult. To develop a reliable genetic authentication tool for Lepidii seu Descurainiae Semen, we analyzed matK sequence. Methods : To obtain the matK sequences of plant materials, genomic DNA was extracted from 24 samples and PCR amplification was carried out using matK-AF/matK-8R universal primer set and matK-LDSF/matK-LDSR primer set. For identifying species-specific nucleotides and phylogenetic analysis, matK regions were sequenced and comparatively analyzed by the ClustalW and Maximum Likelihood method. Results : We developed a new primer set to amplify matK region in Lepidii seu Descurainiae Semen and closely related plant samples. From the comparative analysis of matK sequences, we identified species-specific marker nucleotides for D. sophia, L. apetalum, L. latifolium, E. cheiranthoides, E. macilentum, and D. nemorosa, respectively. Furthermore, phylogenetic analysis revealed clear classification depending on the species. These results indicated that the matK sequence obtained a new primer set in this study was useful to identify Lepidii seu Descurainiae Semen in species level. Conclusions : We developed a primer set and identified species-specific marker nucleotides enough to distinguish authentic Lepidii seu Descurainiae Semen and adulterants at the species level based on the matK sequences. These genetic tool will be useful to prevent adulteration and to standardize the quality of Lepidii seu Descurainiae Semen.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.