• 제목/요약/키워드: tree network

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Constructing Algorithm for Optimal Edge-Disjoint Spanning Trees in Odd Interconnection Network $O_d$ (오드 연결망 $O_d$에서 에지 중복 없는 최적 스패닝 트리를 구성하는 알고리즘)

  • Kim, Jong-Seok;Lee, Hyeong-Ok;Kim, Sung-Won
    • Journal of KIISE:Computer Systems and Theory
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    • v.36 no.5
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    • pp.429-436
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    • 2009
  • Odd network was introduced as one model of graph theory. In [1], it was introduced as a class of fault-tolerant multiprocessor networks and analyzed so many useful properties such as simple routing algorithms, maximal fault tolerance, node axsjoint path, etc. In this paper, we sauw a construction algorithm of edge-axsjoint spanning trees in Odd network $O_d$. Also, we prove that edge-disjoint spanning tree generated by our algorithm is optimal edge-disjoint spanning tree.

A Minimun-diameter Spanning Tree with Bounded Degrees (제한된 분지수를 가지는 최소지름 신장트리)

  • 안희갑;신찬수
    • Journal of KIISE:Computer Systems and Theory
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    • v.31 no.1_2
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    • pp.78-85
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    • 2004
  • Given a set S of n points in the plane, a minimum-diameter spanning tree(MDST) for the set might have a degree up to n-1. This might cause the degradation of the network performance because the node with high degree should handle much more requests than others relatively. Thus it is important to construct a spanning tree network with small degree and diameter. This paper presents an algorithm to construct a spanning tree for S satisfying the following four conditions: (1) the degree is controled as an input, (2) the tree diameter is no more than constant times the diameter of MDST, (3) the tree is monotone (even if arbitrary point is fixed as a root of the tree) in the sense that the Euclidean distance from the root to any node on the path to any leaf node is not decreasing, and (4) there are no crossings between edges of the tree. The monotone property will play a role as an aesthetic criterion in visualizing the tree in the plane.

Research on improving correctness of cardiac disorder data classifier by applying Best-First decision tree method (Best-First decision tree 기법을 적용한 심전도 데이터 분류기의 정확도 향상에 관한 연구)

  • Lee, Hyun-Ju;Shin, Dong-Kyoo;Park, Hee-Won;Kim, Soo-Han;Shin, Dong-Il
    • 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.

Heart Disease Prediction Using Decision Tree With Kaggle Dataset

  • Noh, Young-Dan;Cho, Kyu-Cheol
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.5
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    • pp.21-28
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    • 2022
  • All health problems that occur in the circulatory system are refer to cardiovascular illness, such as heart and vascular diseases. Deaths from cardiovascular disorders are recorded one third of in total deaths in 2019 worldwide, and the number of deaths continues to rise. Therefore, if it is possible to predict diseases that has high mortality rate with patient's data and AI system, they would enable them to be detected and be treated in advance. In this study, models are produced to predict heart disease, which is one of the cardiovascular diseases, and compare the performance of models with Accuracy, Precision, and Recall, with description of the way of improving the performance of the Decision Tree(Decision Tree, KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and DNN (Deep Neural Network) are used in this study.). Experiments were conducted using scikit-learn, Keras, and TensorFlow libraries using Python as Jupyter Notebook in macOS Big Sur. As a result of comparing the performance of the models, the Decision Tree demonstrates the highest performance, thus, it is recommended to use the Decision Tree in this study.

Pre-layout Clock Analysis with Static Timing Analysis Algorithm to Optimize Clock Tree Synthesis (Static Timing Analysis (STA) 기법을 이용한 Clock Tree Synthesis (CTS) 최적화에 관한 연구)

  • Park, Joo-Hyun;Ryu, Seong-Min;Jang, Myung-Soo;Choi, Sea-Hawon;Choi, Kyu-Myung;Cho, Jun-Dong;Kong, Jeong-Taek
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.391-393
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    • 2004
  • For performance and stability of a synchronized system, we need an efficient Clock Tree Synthesis(CTS) methodology to design clock distribution networks. In a system-on-a-chip(SOC) design environment, CTS effectively distributes clock signals from clock sources to synchronized points on layout design. In this paper, we suggest the pre-layout analysis of the clock network including gated clock, multiple clock, and test mode CTS optimization. This analysis can help to avoid design failure with potential CTS problems from logic designers and supply layout constraints so as to get an optimal clock distribution network. Our new design flow including pre-layout CTS analysis and structural violation checking also contributes to reduce design time significantly.

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A Survey on Design Modelling of Networks with Three Configuration (트리(Tree) 구조를 갖는 망설계 문제의 정식화에 관한 조사연구)

  • Tcha, D.W.;Yoon, M.G.
    • Journal of the Korean Operations Research and Management Science Society
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    • v.15 no.1
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    • pp.1-22
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    • 1990
  • This susrvey is on modelling of various network design problems with tree configuration, which have a wide variety of practical applications, particularly in communication, which have a wide a variety of practical applications, particularly in communication and transportation network planning. Models which can be classified as either minimum spanning tree of Steiner tree, are investigated. Various important variants of each basic model are then classified according to model structurs. In addition to the calssification, the typical solution method for each problem is briefly sketched, along with some remarks on further research issues.

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Selection of Important Variables in the Classification Model for Successful Flight Training (조종사 비행훈련 성패예측모형 구축을 위한 중요변수 선정)

  • Lee, Sang-Heon;Lee, Sun-Doo
    • IE interfaces
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    • v.20 no.1
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    • pp.41-48
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    • 2007
  • The main purpose of this paper is cost reduction in absurd pilot positive expense and human accident prevention which is caused by in the pilot selection process. We use classification models such as logistic regression, decision tree, and neural network based on aptitude test results of 505 ROK Air Force applicants in 2001~2004. First, we determine the reliability and propriety against the aptitude test system which has been improved. Based on this conference flight simulator test item was compared to the new aptitude test item in order to make additional yes or no decision from different models in terms of classification accuracy, ROC and Response Threshold side. Decision tree was selected as the most efficient for each sequential flight training result and the last flight training results predict excellent. Therefore, we propose that the standard of pilot selection be adopted by the decision tree and it presents in the aptitude test item which is new a conference flight simulator test.

A Looping Problem in the Tree-Based Mobility Management for Mobile IP Supported Ad Hoc Networks

  • Han, Trung-Dinh;Oh, Hoon
    • Journal of Communications and Networks
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    • v.13 no.4
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    • pp.385-392
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    • 2011
  • A loop can take place in the process of managing tree topology for mobility management of mobile nodes in infrastructure-based mobile ad hoc networks. The formation of a loop degrades an effective bandwidth of the wireless network by passing an identical message repeatedly within the same loop. Therefore, the loop should be resolved to revert the system back to the normal state. In this paper, we propose a simple and novel mechanism that detects and resolves a loop quickly by tracking the depth of trees. The mobility management approach that employs the loop resolution method is evaluated comparatively with the original tree-based one and the hybrid one. It is shown that the proposed approach far outperforms the other approaches, and it is robust against the rapid changes in network topology.

Axial load prediction in double-skinned profiled steel composite walls using machine learning

  • G., Muthumari G;P. Vincent
    • Computers and Concrete
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    • v.33 no.6
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    • pp.739-754
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    • 2024
  • This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

Improving IPTV Forwarding Masechanism in IEEE 802.16j MMR Networks Based on Aggregation

  • Brahmia, Mohamed-El-Amine;Abouaissa, Abdelhafid;Lorenz, Pascal
    • ETRI Journal
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    • v.35 no.2
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    • pp.234-244
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    • 2013
  • Internet protocol television (IPTV) service depends on the network quality of service (QoS) and bandwidth of the broadband service provider. IEEE 802.16j mobile multihop relay Worldwide Interoperability for Microwave Access networks have the opportunity to offer high bandwidth capacity by introducing relay stations. However, to actually satisfy QoS requirements for offering IPTV services (HDTV, SDTV, Web TV, and mobile TV) for heterogeneous users' requests, providers must use a video server for each IPTV service type, which increases the network load, especially bandwidth consumption and forwarding time. In this paper, we present a solution for forwarding IPTV video streaming to diverse subscribers via an 802.16j broadband wireless access network. In particular, we propose a new multicast tree construction and aggregation mechanism based on the unique property of prime numbers. Performance evaluation results show that the proposed scheme reduces both bandwidth consumption and forwarding time.