• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.3
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• pp.228-239
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• 1995

A model experiment on the pair midwater trawl net which is prevailing in Denmark is carried out to get the basic data available for Korean pair bottom trawlers. The model net was made in 1/30 scale considering the Tauti's Similarity law of fishing gear and the dimension of experimental tank. The vertical opening, horizontal opening, towing tension and net working depth of the model net were determined in the tank within the towing velocity 0.46~1.15m/sec, front weight 15.5~62.0g and distance between paired boats 5~8m(which correpond to 2~5k't in towing velocity, 70~280kg in weight and 150~240m in distance respectively in the prototype net). The results got from the model experiment can be converted into the full scale net as follows; 1. Vertical opening showed the largest value of 32m at the condition of 2k't in towing velocity, 280kg in front weight and 150m in the distance between paired boats, and the smallest value of 6m at the condition of 5k't in towing velocity, 70kg in front weight and 240m in the distance between paired boats. 2. Horizontal opening showed the largest value of 45m at the condition of 5k't in towing velocity, 70kg in front weight and 240m in the distance between paired boats, and the smallest value of 33m at the condition of 2k't in towing velocity, 280kg in front weight and 150m in the distance between paired boats. 3. Towing tension showed the largest value of 10, 000kg at the condition of 5k't in towing velocity, 280kg in front weight and 240m in the distance between paired boats, and the smallest value of 1, 600kg at the condition of 2k't in towing velocity, 70kg in front weight and 150m in the distance between paired boats. 4. Net working depth showed the largest value of 38m at the condition of 2k't in towing velocity, 280kg in front weight and 150m in the distance between paired boats, and the smallest value of 6m at the condition of 5k't in towing velocity, 70kg in front weight and 240m in the distance between paired boats. 5. Net opening area showed the largest value of 1, 100m super(2) at the condition of 2k't in towing velocity, 280kg in front weight and 180m in the distance between paired boats, and the smallest value of 250m super(2) at the condition of 5k't in towing velocity, 70kg in front weight and 240m in the distance between paired boats.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Korean Journal of Soil Science and Fertilizer
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• v.29 no.2
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• pp.165-172
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• 1996

Symbiotic effectiveness and significance of Bradyrhizobium japonicum strains in five serogroups which were consisted of three corresponding serotype strains, respectively, indigenous to Korean soils were evaluated in terms of utility of strain serogroup for symbiotic improvement on soybean plant. The nodulation by rhizobia of each serogroup on four soybean cultivars(Danweonkong, Kwangkyo, Pangsakong, and Eunhakong) was good in order of USDA 123 > YCK 150 > YCK 117 > YCK 141 > USDA 110 serogroup members. Shoot dry weight of soybean was relatively high with USDA 110 serogroup members as well as with YCK 141 serogroup members, whereas the effectiveness of USDA 123 serogroup members was the lowest among the serogroups examined. In particular, Pangsakong among soybeans inoculated with five-serogroup members was positively outstanding on nodulation and shoot dry weight of the plant. Overall, symbiotic parameters of serogroup members associated with soybean plant such as nodule number, nodule mass, $N_2$ase activity, and shoot dry weight showed significantly different responses at level of 1% probability among both rhizobial serogroups and soybean cultivars, respectively. The rate in symbiotic similarity of the members of each serogroup from F-test ($$P{\leq_-}0.05$$) was 100% for nodule No., 90% for $N_2$ase activity. and 80% for soybean shoot dry weight. Taken together, the results indicated that the serological grouping of B. japonicum could be strongly useful for improving the symbiotic effectiveness hetween soybean and Rhizobium.

• Korean Journal of Environment and Ecology
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• v.21 no.6
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• pp.536-543
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• 2007

This research was conducted every month from June 1994 until August 1996 with the aim to understand the ecosystem structure through the analysis of oribatid mite community structure in soil subsequent to environmental difference of its habitats located at northward & southward slopes adjacent to each other at an altitude of 1,000 meters of Mt.Jumbong, which is a natural reserved forest, remaining intact. There appeared a significant difference [t-test, p<0.06] in comparison of the number of the species and individuals of Oribatid mite species which were collected and identified at two survey areas. The mean density and the number of the species collected and identified at the northward slopes, and southward slopes were $99.2{\pm}17.6,\;234.2{\pm}62.6$ and $24.7{\pm}3.0,\;40.8{\pm}5.8$, respectively. Species diversity index(H') was higher at the southward slopes($3.09{\pm}0.11$) than at the northward slopes($2.71{\pm}0.13$). The population size of Oribatid mite species was found by the percentage of each species density as against the whole density and classified into dominant species, influent species, and recessive species according to the percentage; as a result, O. nova and Suctobelbella naginata was found to be a dominant species at both survey slopes while Trichogalumna nipponica was found to be a dominant species, at southward but it wasn't collected at the northward slopes at all. The feeding habit of the dominant species at two survey slopes was found to be microphytophagous- eating soil microbe. There appeared a conspicuous difference in compositions of the number of the species, individuals and dominant species at the southward/northward slopes adjoining each other at an attitude of 1,000 meters and less similarity between the two survey slopes. Conclusively, It was found that the heterogeneity of microhabitat has a great effect on Oribatid mite's community characteristics.

• Development and Reproduction
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• v.10 no.1
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• pp.19-32
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• 2006

Genomic DNA isolated from two species of Korean freshwater crab(Eriocheir sinensis) and swimming crab(Portunus trituberculatus) was amplified several times by PCR reactions. The seven arbitrarily selected primers OPA-05, OPA-13, OPA-16, OPB-06, OPB-15, OPB-17 and OPD-10 were used to generate the identical, polymorphic, and specific fragments. 505 fragments were identified in the freshwater crab species, and 513 in the swimming crab from Buan: 81 specific fragments(16.0%) in the freshwater crab species and 100(19.5%) in the swimming crab. 165 identical fragments, with an average of 23.6 per primer, were observed in the freshwater crab species. 66 fragments, with an average of 9.4 per primer, were identified in the swimming crab species. The numbers of polymorphic fragments in the freshwater crab and swimming crab were 50 and 14, respectively. The oligonucleotides decamer primer OPB-17 generated identical DNA fragments, approximately 300 bp, in both the freshwater crab and swimming crab species. Compared separately, the average genetic difference was higher in the swimming crab than in the freshwater crab species. The average genetic difference was $0.726{\pm}0.004$ between the freshwater crab and swimming crab species. The dendrogram obtained by the seven primers indicates four genetic clusters: cluster 1(FRESHWATER 01), cluster 2(FRESHWATER 02, 03, 04, 05 and 06), cluster 3(FRESHWATER 07, 08, 09, 10 and 11), and cluster 4(SWIMMING 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 and 22). The shortest genetic distance displaying significant molecular difference was between individuals SWIMMING no. 18 and SWIMMING no. 17 from swimming crab(0.096). Ultimately, individual no. 02 of the freshwater crab was most distantly related to freshwater crab no. 03(genetic distance = 0.770). As stated above, the potential of RAPD-PCR to identify diagnostic markers for the identification of two crab species has been demonstrated.

• The Korean Journal of Pesticide Science
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• v.14 no.4
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• pp.319-325
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• 2010

To understand the influences of the substituents ($R_1{\sim}R_4$) on insecticidal activity of N'-phenyl-N-methylformamidine analogues (1~22) against two spotted spider mite (Tetranychus urticae), comparative molecular field analysis (CoMFA) model and comparative molecular similarity indices analysis (CoMSIA) model as three dimensional quantitative structure-activity relationships (3D-QSARs) model were derived and discussed quantitatively. From the results, the correlativity and predictability ($r^2{_{cv.}}=0.575$ and $r^2{_{ncv.}}=0.945$) of the CoMFA 1 model were higher than those of the rest models. The the CoMFA 1 and CoMSIA 1 model with the sensitivity of the perturbation and the prediction produced ($d_q{^{2'}}/dr^2{_{yy}}=1.071{\sim}1.146$ & $q^2=0.545{\sim}0.626$) by a progressive scrambling analysis were not dependent on chance correlation. The insecticidal activities from the optimized CoMFA 1 model were depend upon the steric field (62.5%), electrostatic field (28.9%), and hydrophobic field (8.6%) of N'-phenyl-N-methylformamidine analogues. Therefore, the inhibitory activities with optimized CoMFA 1 model were dependent upon steric factor. From the contour maps of the optimized models, it is predicted that the structural distinctions that contribute to the insecticidal activity will be able to applied new potent insecticides design.

• Microbiology and Biotechnology Letters
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• v.39 no.4
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• pp.317-323
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• 2011

Strain A-3, an amylase-producing bacteria, was isolated from coastal seawater near Daecheon in the Republic of Korea. It was seen to possess a single polar flagella and grow well, on ASW-YP agar plates, at temperatures of between $20-37^{\circ}C$. However, it grew more slowly at the temperatures of $15^{\circ}C$ and $40^{\circ}C$. Similarly, it was observed to grow abundantly, in an Artificial Sea Water-Yeast extract-Peptone (ASW-YP) liquid medium, in a pH range of 6-9, but not grow at pHs of 4-5 and a pH of 10. Strain A-3 was noted as being close to Pseudoalteromonas phenolica O-$BC30^T$, Pseudoalteromonas luteoviolacea $NCIMB1893^T$, Pseudoalteromonas rubra $ATCC29570^T$, and Pseudoalteromonas byunsanensis $FR1199^T$, with 98.30%, 97.86%, 97.78%, and 97.25% similarities respectively, in its 16S rRNA sequence. A phylogenetic tree revealed that strain A-3 and P. phenolica O-$BC30^T$ belong to a clade. However, strain A-3 differed from P. phenolica O-$BC30^T$ in relation to a number of physiological characteristics. Strain A-3 exhibited no growth above 5% NaCl concentrations, no utilization of D-glucose, D-mannose, D-maltose, or D-melibose, and no lipase (C-14) activity. All of these properties strongly indicate that strain A-3 is distant from P. phenolica O-$BC30^T$ and thus led us to name it Pseudoalteromonas sp. A-3. Pseudoalteromonas sp. A-3 produces ${\alpha}$-amylase throughout growth. Maximal amylase activities of 144.48 U/mL and 149.20 U/mL were seen at pH 7.0 and $37^{\circ}C$, respectively. Pseudoalteromonas sp. A-3's high, stable production of ${\alpha}$-amylase in addition to its biochemical features, such as alkalitolerance, suggest that it is a good candidate for industrial applications.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.