• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.22-24
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• 2005

RFID 시스템은 전자태그를 상품에 부착하여 리더를 통해 태그를 인식함으로써 상품의 정보 및 위치정보를 추적할 수 있다. 태그 객체의 위치정보는 시간에 따라 궤적 정보가 누적되는 이동체와 유사한 특성을 가지지만 이동체의 위치와 달리 태그의 위치는 리더의 위치로 인식되며 위치보고가 리더의 인식영역 안에서만 이루어지므로 시간축에 평행한 interval의 형태를 나타난다. 태그가 리더의 인식영역에 들어와 나가지 않으면 궤적이 완성되지 않아 리더에 머물고 있는 태그의 궤적을 표현할 수 없으므로 질의 시 이러한 태그를 검색할 수 없다. 시공간 이동체 색인에서는 이러한 태그의 특성을 표현하기 힘들기 때문에 태그의 특성을 고려한 색인이 필요하게 되었다. TPIR-Tree(Time Parameterized Interval R-tree)는 시간 매개변수 간격으로 태그의 interval을 정의하여 리더안에 머무는 태그의 interval을 표현할 수 있다. 그러나 각 interval이 시공간적으로 연결되어 있지 않아 색인 상에서 태그의 궤적을 검색하는 것은 매우 높은 검색 비용을 가지는 단점이 있다. 이 논문에서는 태그 궤적 검색 시 TPIR-Tree의 높은 검색 비용문제를 해결한 $TPIR^{*}$-Tree를 제안한다. 제안된 $TPIR^{*}$-Tree는 색인에서 태그의 궤적 정보를 유지할 수 있도록 하기 위해서 시간적으로 연결되지 않은 각 interval을 연결하기 위한 기법을 제시하고 있다. 또한. interval을 색인에 삽입할 때 연결정보를 유지하기 위해 이전 interval을 효율적으로 검색할 수 있는 방법을 제시하고 있다.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.63-71
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• 2011

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.415-420
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• 2016

A graph G=(V,E) is called an interval graph with a set V of vertices representing intervals on a line such that there is an edge $(i,j){\in}E$ if and only if intervals i and j intersect. In this paper, we are concerned in the vertex connectivity, one of various characteristics of the graph. Specifically, the vertex connectivity of an interval graph is represented by the overlapping of intervals. Also we propose an efficient algorithm to compute the vertex connectivity on the fully dynamic environment in which the vertices or the edges are inserted or deleted. Using a special kind of interval tree, we show how to compute the vertex connectivity and to maintain the tree in O(logn) time when a new interval is added or an existing interval is deleted.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2750-2767
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• 1996

In this papr, a new lossless compression method is presented based on the Binary Adaptive Arithmetic Coder(BAAC). A simple unbalanced binary tree is created by recursively dividing the BAAC unit interval into two probability sub-inervals. On the tree the More Probable Predicted Value(MPPV) and Less Probable Predicated Value(LPPV) estimated by local statistics of the image pixels are arranged in decreasing order. The BAAC or Huffman coder is thus applied to the branches of the tree. The proposed method allows the coder be directly applied to the full bit-plane medical image without a decomposition of the full bit-planes into a series of binary bit-planes. The use of the full bit model template improves the compresion ratio. In addition, a fast computation for adjusting the interval is possible since a simple arithmetic operation based on probability interval estimation state machine is used for interval sub-division within the BAAC unit interval.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.394-405
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• 2001

Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.255-263
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• 2009

In this paper, we propose an adaptive beacon scheduling algorithm to control a reception period of actual data according to variation of amount of traffic in IEEE 802.15.4 LR-WPAN(Low Rate-Wireless Personal Area Network) with the cluster-tree topology. If a beacon interval is shortened, the amount of the traffic a node receives can be increased while consumption of the energy can be also increased. In this sense, we can achieve optimal on orgy consumption by controlling the beacon interval when the amount of data to be received is being decreased. The result of simulation using NS-2 shows that the proposed algorithm improves performances in terms of packet loss rate and end-to-end delay compared with algorithm using a fixed beacon interval. For a design of cluster-tree based LR-WPAN managing delay-sensitive services, the proposed algorithm and the associated results can be applied usefully.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.500-509
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• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.507-509
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• 2012

심전도 질환 데이터는 일반적으로 분류기를 사용한 실험이 많다. 심전도 신호는 QRS-Complex와 R-R interval을 추출하는 경우가 많은데 본 실험에서는 R-R interval을 추출하여 실험하였다. 심전도 데이터의 분류 실험은 일반적으로 SVM(Support Vector Machine)과 MLP(Multilayer Perceptron)으로 실험되지만 본 실험은 Decision Tree를 사용하여 정확도 향상을 추구하였다. 그리고 정확도 비교 분석을 위해 SVM과 MLP 분류기 실험을 같이 수행하였고, 동일한 데이터와 간격으로 실험한 타 논문의 결과와 비교해 보았다. Decision Tree를 다른 분류기와 타 논문의 결과와 비교해 보니 정확도 부분에서는 Decision Tree가 가장 우수하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.143-146
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• 2008

For tracing tag locations, a trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry and we should extend the region of a query to find the tag that remains in a reader. In this paper, we propose an interval data model of tag's trajectory in order to solve the problem. For the interval data model. we propose a new index scheme called the IR-tree(Interval R-tree) and algorithms of insert and split for processing query efficiently. We also evaluate the performance of the proposed index scheme and compare it with the previous indexes.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1367-1369
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• 2013

Applying Application-Level Multicast technology (ALM) to Distributed Interactive Applications (DIAs) becomes more and more popular. Especially for DIAs embedded priority that the sender forwards data to receivers due to their respective priorities. The priority-based directed minimum spanning tree (PST) algorithm was designed for these DIAs. However, the PST has no efficient priority selection and filtering mechanism. The system will consume a tremendous amount of resource for reconstructing distribution tree and becomes unstable and unscalable. In this paper, First, We propose a novel priority-based application level multicast algorithm: Predict-and-Quantize for Priority with directed minimum Spanning Tree (PQPST), which can efficiently predict efficient priorities for the receivers and quantize the predicted priorities to build the multicast distribution tree. Second, we propose Priority Discrepancy Heuristic Mechanism (PDHM), which sets different thresholds of priority discrepancy within the priority discrepancy interval to control the distribution tree construction can efficiently decrease the repeated distribution tree construction, and we get the best heuristic priority discrepancy interval by PQPST. According to the simulation results, the PQPST and PDHM can efficiently improve the performance of the PST algorithm.