• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.439-446
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• 2015

Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.

• Transactions on Electrical and Electronic Materials
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• v.15 no.3
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• pp.125-129
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• 2014

A dataset can be clustered by merging the bucket indices that come from the random projection of locality sensitive hashing functions. It should be noted that for this to work the merging interval must be calculated first. To improve the feasibility of large scale data clustering in high dimensional space we propose an enhanced Locality Sensitive Hashing Clustering Method. Firstly, multiple hashing functions are generated. Secondly, data points are projected to bucket indices. Thirdly, bucket indices are clustered to get class labels. Experimental results showed that on synthetic datasets this method achieves high accuracy at much improved cluster speeds. These attributes make it well suited to clustering data in high dimensional space.

• Journal of the Korea Computer Industry Society
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• v.3 no.5
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• pp.529-546
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• 2002

A VOD server, which is the central component for constructing VOD systems, requires to provide high bandwidth and continuous real-time delivery. It is also necessary to the sophisticated disk scheduling and data placement schemes in VOD sewers. One of the most common problem facing in such a system is the high initial latency time to service multiple users concurrently. In this paper, we propose a dynamic stream merging scheme for reducing the initial latency time in VOD servers. The proposed scheme allows clients to merge streams on a request as long as their requests fall within the reasonable time interval. The basic idea behind the dynamic stream merging is to merge multiple streams into one by increasing the frame rate of each stream. In the performance study, the proposed scheme can reduce the initial latency time under the minimum buffer use and also can enhance the performance of the VOD server with respect to the capacity of user admission.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.27-34
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• 2013

Background subtraction is one of the key techniques for automatic video content analysis, especially in the tasks of visual detection and tracking of moving object. In this paper, we present a new sample-based technique for background extraction that provides background image as well as background model. To handle both high-frequency and low-frequency events at the same time, multiple interval background models are adopted. The main innovation concerns the use of a confidence factor to select the best model from the multiple interval background models. To our knowledge, it is the first time that a confidence factor is used for merging several background models in the field of background extraction. Experimental results revealed that our approach based on multiple interval sampling works well in complicated situations containing various speed moving objects with environmental changes.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.115-126
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• 2009

The lane-change behavior usually consists of discretionary lane-change and mandatory lane-change types. For the first type, drivers change lanes selectively to maintain their own driving condition and the second type is the case that the drivers must change the current lane, which can occur in recurrent congestion sections like merging and weaving sections. The mandatory lane-change behavior have a great effect on the operation condition of freeway. In this paper, we first generate data such as traffic volumes, speeds, densities, and the number of lane-change within the merging and weaving sections using the data of individual vehicle collected from time-lapse aerial photography. And then, the data is divided into the stable and congested flow by analyzing the speed variation pattern of individual vehicles. In addition, the number of lane-changing from ramp to mainline within every 30-meter interval is investigated before and after traffic congestion at study sites and the distribution of lane-changing at each 30-meter point is analyzed to identify the variation of lane-changing ratio depending on the stable and congested flows. To recognize the effect of mainline flow influenced by ramp flow, this study also analyzes the characteristics of the lane-changing distributions within the lanes of mainline. The purpose of this paper is to present the basic theory to be used in developing a lane-changing model at the merging and weaving sections on freeways.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.188-200
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• 2006

Once a continuous query, which is commonly used in sensor networks, is issued, the query is executed many times with a certain interval and the results of those query executions are collected to the base station. Since this comes many communication messages continuously, it is important to reduce communication cost for collecting data to the base station. In sensor networks, in-network processing reduces the number of message transmissions by partially aggregating results of an aggregate query in intermediate nodes, or merging the results in one message, resulting in reduction of communication cost. In this paper, we propose a routing tree for sensor nodes that qualify the given query predicate, called the query specific routing tree(QSRT). The idea of the QSRT is to maximize in-network processing opportunity. A QSRT is created seperately for each query during dissemination of the query. It is constructed in such a way that during the collection of query results partial aggregation and packet merging of intermediate results can be fully utilized. Our experimental results show that our proposed method can reduce message transmissions more than 18% compared to the existing one.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.803-810
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• 2005

Information retrieval is one of the most challenging areas in which the ontology technology is effectively used. Among them image retrieval using the image meta data and ontology is the one that can substitute the keyword-based image retrieval. In the paper, the retrieval of visual media such as the art image and photo picture is handled. It is assumed that there are more than one service providers of the visual media and also there is one central service broker that mediates the user's query. Given the user's query the first step that must be done in the service broker is to get the list of candidate service providers that fit the query. This is done by defining various ontologies such as the service ontology and matching the query against the ontology and providers. A novel matching method based on the ASN.1. The experiment shows that the method is more effective than existing tree-based and interval-based methods. Ontology merging issue is also handled that can happen when the service providers register their service into the service broker. An effective method is also proposed.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.105-111
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• 2009

Widely used color information recognition methods based on the RGB color model with static fuzzy inference rules have limitations due to the model itself - the detachment of human vision and applicability of limited environment. In this paper, we propose a method that is based on HSI model with new inference process that resembles human vision recognition process. Also, a user can add, delete, update the inference rules in this system. In our method, we design membership intervals with sine, cosine function in H channel and with functions in trigonometric style in S and I channel. The membership degree is computed via interval merging process. Then, the inference rules are applied to the result in order to infer the color information. Our method is proven to be more intuitive and efficient compared with RGB model in experiment.

• Economic and Environmental Geology
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• v.39 no.4 s.179
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• pp.417-426
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• 2006

Data acquisition is as important as data processing and interpretation in the field of marine geophysical exploration. Marine geophysicist, however, may not have enough information in this field because data acquisition method has been mainly developed by the commercial companies manufacturing the equipment. Therefore, the purpose of this paper is to introduce the general data acquisition method and information to help to construct the systematic and effective survey plan. When a survey plan is set up, the most important thing is to select the seismic equipment based on required penetration depth and resolution, and then construct the survey line intervals. Although a line interval varies from the research purposes, it should be narrower than the expected subsurface structures. Also, if 100% coverage of multibeam data is required, line intervals need to be adjusted based on the equipment characteristics. In case of merging with the preexisting dataset like bathymetry, gravity and magnetic, cross-over errors occurred at the each cross point should be removed to avoid any kinds of misinterpretation.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.775-782
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• 2005

In this paper, we propose multicast technique in order to reduce the required network bandwidth by n times, by merging the adjacent multicasts depending on the number of HENs (Head-End-Nodes) n that request the same video. Allowing new clients to immediately join an existing multicast through patching improves the efficiency of the multicast and offers services without any initial latency. A client might have to download data through two channels simultaneously, one for multicast and the other for patching. The more the frequency of requesting the video is, the higher the probability of caching it among HENs increases. Therefore, the requests for the cached video data can be served by HENs. Multicast from server is generated when the playback time exceeds the amount of cached video data. Since the interval of multicast can be dynamically expanded according to the popularity of videos, it can be reduced the server's workload and the network bandwidth. We perform simulations to compare its performance with that of conventional multicast. From simulation results, we confirm that the Proposed multicast technique offers substantially better performance.