• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.408-416
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• 2017

This paper proposes a real-time side-rear vehicle detection algorithm that detects vehicles quickly and accurately in blind spot areas when driving. The proposed algorithm uses a cascade classifier created by AdaBoost Learning using the MCT (modified census transformation) feature vector. Using this classifier, the smaller the detection window, the faster the processing speed of the MCT classifier, and the larger the detection window, the greater the accuracy of the MCT classifier. By considering these characteristics, the proposed algorithm uses two classifiers with different detection window sizes. The first classifier quickly generates candidates with a small detection window. The second classifier accurately verifies the generated candidates with a large detection window. Furthermore, the vehicle classifier and the wheel classifier are simultaneously used to effectively detect a vehicle entering the blind spot area, along with an adjacent vehicle in the blind spot area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1327-1334
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• 2017

The Bubble sort graph is node symmetric, and can be used in the data sorting algorithm. In this research we propose and analyze that Half Bubble sort graph that improved the network cost of Bubble sort graph. The Half Bubble sort graph's number of node is n!, and its degree is ${\lfloor}n/2{\rfloor}+1$. The Half Bubble sort graph's degree is $${\sim_=}0.5$$ times of the Bubble sort, and diameter is $${\sim_=}0.9$$ times of the Bubble sort. The network cost of the Bubble sort graph is $${\sim_=}0.5n^3$$, and the network cost of the half Bubble sort graph is $${\sim_=}0.2n^3$$. We have proved that half bubble sort graph is a sub graph of the bubble sort graph. In addition, we proposed a routing algorithm and analyzed the diameter. Finally, network cost is compared with the bubble sort graph.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.1
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• pp.63-71
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• 2013

In order to support mobility for sink groups, it is important to get the current location of a mobile sink group and then to offer the location to a source. Typically, previous works calculate a region including all member sinks by flooding; then, it notifies this region information to a source. However, flooding and location updates are periodically performed regardless of the group movement so that it causes considerable control overhead. In this paper, we propose an energy-efficient scheme supporting mobile sink groups. The proposed scheme obtains a location of a group without flooding. It exploits the inherent property of mobile sink groups which could approximate entire group movement by only partial member sinks movement. Also, the scheme learns group location by back-propagation learning method through exploiting overhearing feature in wireless communication environment. Our simulation studies show that the proposed scheme significantly improves in terms of energy consumption compared to the previous work.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.107-112
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• 2018

In this paper, optical flow based keypoint detection and tracking technique is proposed for the collaboration between flying drone with vision system and ground robots. There are many challenging problems in target detection research using moving vision system, so we combined the improved FAST algorithm and Lucas-Kanade method for adopting the better techniques in each feature detection and optical flow motion tracking, which results in 40% higher in processing speed than previous works. Also, proposed image binarization method which is appropriate for the given marker helped to improve the marker detection accuracy. We also studied how to optimize the embedded system which is operating complex computations for intelligent functions in a very limited resources while maintaining the drone's present weight and moving speed. In a future works, we are aiming to develop collaborating smarter robots by using the techniques of learning and recognizing targets even in a complex background.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.139-148
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• 2010

This paper shows a reinforcement post-processing method and feedback algorithm for improvement of assigning method in classification. Especially, we focused on complex documents that are generally considered to be hard to classify. A basis factors in traditional classification system are training methodology, classification models and features of documents. The classification problem of the documents containing shared features and multiple meanings, should be deeply mined or analyzed than general formatted data. To address the problems of these document, we proposed a method to expand classification scheme using decision boundary detected automatically in our previous studies. The assigning method that a document simply decides to the top ranked category, is a main factor that we focus on. In this paper, we propose a post-processing method and feedback algorithm to analyze the relevance of ranked list. In experiments, we applied our post-processing method and one time feedback algorithm to complex documents. The experimental results show that our system does not need to change the classification algorithm itself to improve the accuracy and flexibility.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1204-1217
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• 2004

Numerous statistical and machine learning techniques have been studied for automatic text classification. However, because they train the classifiers using only feature vectors of documents, ambiguity between two possible categories significantly degrades precision of classification. To remedy the drawback, we propose a new method which incorporates relationship information of categories into extant classifiers. In this paper, we first perform the document classification using the k-NN classifier which is generally known for relatively good performance in spite of its simplicity. We employ the relationship information from an object-based thesaurus to reduce the ambiguity. By referencing various relationships in the thesaurus corresponding to the structured categories, the precision of k-NN classification is drastically improved, removing the ambiguity. Experiment result shows that this method achieves the precision up to 13.86% over the k-NN classification, preserving its recall.

• Archives of design research
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• v.14 no.4
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• pp.25-33
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• 2001

With the increasing access to the internet, the number of designers who rely on internet to use information on design is on the rise. Therefore common dictionary of design terminology need to be formed and shared among designers. To do so, internet is very useful medium. However as relating terminology increases rapidly through interactivity among designers, it will be far from taking full advantage of features of internet to set up and provide such information unilaterally on internet. This indicates that providing data on the internet, not via traditional books, requires in-depth study on process of establishment of database structure and appropriate interface design. Thus this study will show design terms database model that harnesses internet feature that enables establishment of information spontaneously through user's interactivity, departing from a model that conveys information unilaterally. This report summarized and analyzed various models and suggested classification system in accordance with user's learning cognition. Problems on existing dictionary of design terminology were identified and new methods addressing such problems were exploited. In a word, this report is intended to propose user oriented inter-constructive database model that highlights high level of openness and interactivity by enabling changes of text in the cyber space and encouraging user to participate in making design dictionary.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.438-449
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• 2004

More and more users are using the query e-mail according to the increment of use of internet. The operator of internet site desires the users to check the FAQ and Q＆A contents first before sending the query e-mail to the operator However the users try to get the solution for a problem easily by simply sending a query e-mail. Therefore the increment of query e-mail is inevitable, and the site operator is suffering from too heavy loads and spending too much time and cost to reply the query e-mail. In this paper, we are proposing an efficient method of classifying the query e-mail of users automatically by using a neural network. To verify the reasonability of our work, the query e-mails of KORNET are used as the test data, which is actually gathered in KT. A total of 210 learning data and 280 test data were used to test the performance of the proposed approach. From the experiments we got the encouraging result from the view point of application in real life. The proposed approach satisfied the request of users who wanted rapid response for their query e-mail.

• Journal of the Korean association of regional geographers
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• v.15 no.4
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• pp.449-463
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• 2009

One feature of the multicultural policy practiced in Japan is that the central government stresses the importance of local administrations in executing the policy, providing a systematic framework through which local administrations can actively promote and execute multicultural policy intended to foster social tolerance. In other words, the multicultural policy practiced in Japan seeks to overcome some of the limits and issues inherent to such policy by encouraging the delivery of opinions from below that reflect differences among different localities, while the central government proposes the policy aims and recommendations from above down to local administrations. The multicultural policy of Japan, which allows local administrations to administer such networks by actively carrying out the roles of arbitration and integration within communities where multiculturalism is found, presents meaningful points of comparison and learning to the multicultural policy in Korea that has only recently begun to seek for the aims and ways of multicultural policy from the perspective of social integration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3925-3932
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• 2013

Emotion is closely related to the life of human, so has effect on many parts such as concentration, learning ability, etc. and makes to have different behavior patterns. The purpose of this paper is to extract important features based on physiological signals to recognize negative emotion. In this paper, after acquisition of electrocardiography(ECG), electroencephalography(EEG), skin temperature(SKT) and galvanic skin response(GSR) measurements based on physiological signals, we designed an accurate and fast algorithm using combination of linear discriminant analysis(LDA) and genetic algorithm(GA), then we selected important features. As a result, the accuracy of the algorithm is up to 96.4% and selected features are Mean, root mean square successive difference(RMSSD), NN intervals differing more than 50ms(NN50) of heart rate variability(HRV), ${\sigma}$ and ${\alpha}$ frequency power of EEG from frontal region, ${\alpha}$, ${\beta}$, and ${\gamma}$ frequency power of EEG from central region, and mean and standard deviation of SKT. Therefore, the features play an important role to recognize negative emotion.