• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.69-76
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• 2012

This paper proposes a novel way of personalized web search through re-ranking the search results with user profiles of concept-network structure. Basically, personalized search systems need to be based on user profiles that contain users' search patterns, and they actively use the user profiles in order to expand initial queries or to re-rank the search results. The proposed method is a sort of a re-ranking personalized search method integrated with query expansion facility. The method identifies some documents which occur commonly among a set of different search results from the expanded queries, and re-ranks the search results by the degree of co-occurring. We show that the proposed method outperforms the conventional ones by performing the empirical web search with a number of actual users who have diverse information needs and query intents.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.185-192
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• 2017

In various fields of science, 2-dimensional array data is being generated actively as a result of measurements and simulations. Although various query processing techniques for array data are being studied, the problem of finding similar regions, whose sizes are not known in advance, in 2-dimensional array has not been addressed yet. Therefore, in this paper, we propose an efficient method for finding regions with similar element values, whose size is larger than a user-specified value, for a given 2-dimensional array data. The proposed method, for each pair of elements in the array, expands the corresponding two regions, whose initial size is 1, along the right and down direction in stages, keeping the shape of the two regions the same. If the difference between the elements values in the two regions becomes larger than a user-specified value, the proposed method stops the expansion. Consequently, the proposed method can find similar regions efficiently by accessing only those parts that are likely to be similar regions. Through theoretical analysis and various experiments, we show that the proposed method can find similar regions very efficiently.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.367-371
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• 2015

In this paper, a novel localization method for a monocular camera is proposed by using a feature-based probabilistic map. The localization of a camera is generally estimated from 3D-to-2D correspondences between a 3D map and an image plane through the PnP algorithm. In the computer vision communities, an accurate 3D map is generated by optimization using a large number of image dataset for camera pose estimation. In robotics communities, a camera pose is estimated by probabilistic approaches with lack of feature. Thus, it needs an extra system because the camera system cannot estimate a full state of the robot pose. Therefore, we propose an accurate localization method for a monocular camera using a probabilistic approach in the case of an insufficient image dataset without any extra system. In our system, features from a probabilistic map are projected into an image plane using linear approximation. By minimizing Mahalanobis distance between the projected features from the probabilistic map and extracted features from a query image, the accurate pose of the monocular camera is estimated from an initial pose obtained by the PnP algorithm. The proposed algorithm is demonstrated through simulations in a 3D space.

• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.147-165
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• 2006

In this study, experiments for selection of association terms were conducted in order to discover the optimum method in selecting additional terms that are related to an initial query term. Association term sets were generated by using support, confidence, and lift measures of the Apriori algorithm, and also by using the similarity measures such as GSS, Jaccard coefficient, cosine coefficient, and Sokal & Sneath 5, and mutual information. In performance evaluation of term selection methods, precision of association terms as well as the overlap ratio of association terms and relevant documents' indexing terms were used. It was found that Apriori algorithm and GSS achieved the highest level of performances.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Progress in Medical Physics
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• v.14 no.1
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• pp.43-50
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• 2003

PACS mostly has been used in large scaled hospital due to expensive initial cost to set up the system. The network of PACS is independent of the others: network. The user's PC has to be connected physically to the network of PACS as well as the image viewer has to be installed. The web based mini-PACS can store, manage and search inexpensively a large quantity of radiologic image acquired in a hospital. The certificated user can search and diagnose the radiologic image using web browser anywhere Internet connected. The implemented Image viewer is a viewer to diagnose the radiologic image. Which support the DICOM standard and was implemented to use JAVA programming technology. The JAVA program language is cross-platform which makes easier upgrade the system than others. The image filter was added to the viewer so as to diagnose the radiologic image in detail. In order to access to the database, the user activates his web browser to specify the URL of the web based PACS. Thus, The invoked PERL script generates an HTML file, which displays a query form with two fields: Patient name and Patient ID. The user fills out the form and submits his request via the PERL script that enters the search into the relational database to determine the patient who is corresponding to the input criteria. The user selects a patient and obtains a display list of the patient's personal study and images.

• Journal of the Korean Society for information Management
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• v.20 no.3
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• pp.111-127
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• 2003

The purpose of this study is to ascertain whether event tracking is more effective in event retrieval than information filtering. This study examined the two techniques for event retrieval to suggest the more effective one. The event-retrieval performances of the event tracking technique based on a kNN classifier and the query-based information filtering technique were compared. Two event tracking experiments, one with the static training set and the other with the dynamic training set , were carried out. Two information filtering experiments, one with initial queries and the other with refined queries, were also carried out to evaluate the event-retrieval effectiveness. We found that the event tracking technique with the static training set performed better than on with the dynamic training set. It was also found that the information fitering technique using intial queries performed better than one using the refined queries. In conclusion, the comparison of the best cases of event tracking and information filtering revealed that the information filtering technique outperformed the event tracking technique in event retrieval.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.145-155
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• 2002

In content-based image retrieval (CBIR), most conventional approaches assume a linear relationship between different features and require users themselves to assign the appropriate weights to each feature. However, the linear relationship assumed between the features is too restricted to accurately represent high-level concepts and the intricacies of human perception. In this paper, a neural network-based image retrieval (NNIR) model is proposed. It has been developed based on a human-computer interaction approach to CBIR using a radial basis function network (RBFN). By using the RBFN, this approach determines the nonlinear relationship between features and it allows the user to select an initial query image and search incrementally the target images via relevance feedback so that more accurate similarity comparison between images can be supported. The experiment was performed to calculate the level of recall and precision based on a database that contains 1,015 images and consists of 145 classes. The experimental results showed that the recall and level of the proposed approach were 93.45% and 80.61% respectively, which is superior than precision the existing approaches such as the linearly combining approach, the rank-based method, and the backpropagation algorithm-based method.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.39-46
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• 2002

In distributed spatial database systems, users nay issue a query that joins two relations stored at different sites. The sheer volume and complexity of spatial data bring out expensive CPU and I/O costs during the spatial join processing. This paper shows a new spatial join method which joins two spatial relation in a parallel way. Firstly, the initial join operation is divided into two distinct ones by partitioning one of two participating relations based on the region. This two join operations are assigned to each sites and executed simultaneously. Finally, each intermediate result sets from the two join operations are merged to an ultimate result set. This method reduces the number of spatial objects participating in the spatial operations. It also reduces the scope and the number of scanning spatial indices. And it does not materialize the temporary results by implementing the join algebra operators using the iterator. The performance test shows that this join method can lead to efficient use in terms of buffer and disk by narrowing down the joining region and decreasing the number of spatial objects.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.63-73
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• 2011

In general, conventional search engines provide the same search results for the same queries of users, and however such techniques do not consider users' characteristics. To overcome this problem, we need a new way of personalized search which returns customized search results according to users' preference. In this paper, we propose a concept network profile-based personalized web search system in which the concept network is developed for accumulating users' characteristics. The concept network-based user profile is used to expand initial search queries to achieve personalized search. The concept network is a network structure of concepts where each concept is generated whenever each query is submitted, and it can be defined as a set of keywords extracted from the selected documents. Furthermore, we have improved the concept networks by augmenting intent keywords of each concept with a set of classification tags, called folksonomy, assigned to each document. For an additional personalized search technique, we propose a new re-ranking method that analayzes the degree of overlapped search results.