• Title/Summary/Keyword: Tree-Based Network

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개미 시스템을 기반으로 한 Ad hoc 네트워크 멀티캐스팅

  • 이세영;김중항;장형수
    • Proceedings of the Korean Information Science Society Conference
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    • 2004.10c
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    • pp.1-3
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    • 2004
  • 본 논문에서는 Core Based Tree(CBT) 알고리즘과 개미 집단 알고리즘의 특성을 융합하여 Mobile Ad hoc Network(MANET)에 맞는 멀티캐스팅 알고리즘, Ad hoc network Multicasting with Ant System (ANMAS)을 제안한다. ANMAS는 개미 알고리즘의 간접적 정보 전달 및 평가 방법을 통해 멀티캐스팅에 필요한 위상정보를 수집하여 견고한 멀티캐스팅 그룹을 형성함으로서 기존의 알고리즘에 비해 효율적이며 실험결과를 통해 이를 확인할 수 있다.

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Semijoin-Based Spatial Join Processing in Multiple Sensor Networks

  • Kim, Min-Soo;Kim, Ju-Wan;Kim, Myoung-Ho
    • ETRI Journal
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    • v.30 no.6
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    • pp.853-855
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    • 2008
  • This paper presents an energy-efficient spatial join algorithm for multiple sensor networks employing a spatial semijoin strategy. For optimization of the algorithm, we propose a GR-tree index and a grid-ID-based spatial approximation method, which are unique to sensor networks. The GR-tree is a distributed spatial index over the sensor nodes, which efficiently prunes away the nodes that will not participate in a spatial join result. The grid-ID-based approximation provides great reduction in communication cost by approximating many spatial objects in simpler forms. Our experiments demonstrate that the algorithm outperforms existing methods in reducing energy consumption at the nodes.

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Communication Protocol to Support Mobile Sinks by Multi-hop Clusters in Wireless Sensor Networks (무선 센서 네트워크에서 멀티-홉 클러스터를 통한 이동 싱크 지원 통신 프로토콜)

  • Oh, Seung-Min;Jung, Ju-Hyun;Lee, Jeong-Cheol;Park, Ho-Sung;Yim, Yong-Bin;Kim, Sang-Ha
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.3A
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    • pp.287-295
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    • 2010
  • In wireless sensor networks(WSNs), the studies that support sink mobility without global position information exploit a Backbone-based Virtual Infrastructure (BVI) which considers one-hop clusters and a backbone-based tree. Since the clusters of a sink and a source node are connected via flooding into the infrastructure, it causes high routing cost. Although the network could reduce the number of clusters via multi-level clusters, if the source nodes exist at nearest clusters from the cluster attached by the sink and they are in different branches of the tree, the data should be delivered via detour paths on the tree. Therefore, to reduce the number of clusters, we propose a novel multi-hop cluster based communication protocol supporting sink mobility without global position information. We exploit a rendezvous cluster head for sink location service and data dissemination but the proposed protocol effectively reduces data detour via comparing cluster hops from the source. Simulation shows that the proposed protocol is superior to the existing protocols in terms of the data delivery hop counts.

Identification of Cardiovascular Disease Based on Echocardiography and Electrocardiogram Data Using the Decision Tree Classification Approach

  • Tb Ai Munandar;Sumiati;Vidila Rosalina
    • International Journal of Computer Science & Network Security
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    • v.23 no.9
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    • pp.150-156
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    • 2023
  • For a doctor, diagnosing a patient's heart disease is not easy. It takes the ability and experience with high flying hours to be able to accurately diagnose the type of patient's heart disease based on the existing factors in the patient. Several studies have been carried out to develop tools to identify types of heart disease in patients. However, most only focus on the results of patient answers and lab results, the rest use only echocardiography data or electrocardiogram results. This research was conducted to test how accurate the results of the classification of heart disease by using two medical data, namely echocardiography and electrocardiogram. Three treatments were applied to the two medical data and analyzed using the decision tree approach. The first treatment was to build a classification model for types of heart disease based on echocardiography and electrocardiogram data, the second treatment only used echocardiography data and the third treatment only used electrocardiogram data. The results showed that the classification of types of heart disease in the first treatment had a higher level of accuracy than the second and third treatments. The accuracy level for the first, second and third treatment were 78.95%, 73.69% and 50%, respectively. This shows that in order to diagnose the type of patient's heart disease, it is advisable to look at the records of both the patient's medical data (echocardiography and electrocardiogram) to get an accurate level of diagnosis results that can be accounted for.

STO-based Cluster Header Election Algorithm (STO 기반 클러스터 헤더 선출 알고리즘)

  • Yoon, Jeong-Hyeon;Lee, Heon-Guk;Kim, Seung-Ku
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.587-590
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    • 2019
  • This paper is about to improve the network life's reduction due to the deviation of sensor node and frequently change of network, the main problem of sensor network. The existing Scalable Topology Organization(STO)-based ZigBee Tree Topology Control Algorithm did not consider ways to consume power so the network lifetime is too short. Accordingly, per each round, electing a new parent node and consisting of the new network topology technique, The Cluster Header Selection, extending the network's overall lifetime. The OMNet++ Simulator yielded results from the existing STO Algorithm and the proposed Cluster Header Selection Technique in the same experimental environment, which resulted in an increase in overall network life by about 40% and an improvement of about 10% in performance in the remaining portion of the battery.

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A Data Gathering Scheme using Dynamic Branch of Mobile Sink in Wireless Sensor Networks (무선 센서망에서 이동 싱크의 동적 브랜치를 통한 데이터 수집 방안)

  • Lee, Kil-Hung
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.11 no.1
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    • pp.92-97
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    • 2012
  • This paper suggests a data gathering scheme using dynamic branch tree in wireless sensor networks. A mobile sink gathers data from each sensor node using a dynamic data gathering tree rooted at the mobile sink node. As the sink moves, a tree that has multiple branch is formed and changed dynamically as with the position of the sink node. A hop-based scope filter and a restricted flooding scheme of the tree are also suggested. Simulation results show that the proposed data gathering scheme has better results in data arrival rate, the end-to-end delay and energy saving characteristics compared with the previous scheme.

Comparison of Directory Structures for SAN Based Very Large File Systems (SAN 환경 대용량 파일 시스템을 위한 디렉토리 구조 비교)

  • 김신우;이용규
    • The Journal of Society for e-Business Studies
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    • v.9 no.1
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    • pp.83-104
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    • 2004
  • Recently, information systems that require storage and retrieval of huge amount of data are becoming used widely. Accordingly, research efforts have been made to develop Linux cluster file systems in the SAN environment in which clients themselves can manage metadata and access data directly. Also a semi-flat directory structure based on extendible hashing has been proposed to support fast retrieval of files[1]. In this research, we have designed and implemented the semi-flat extendible hash directory under the Linux system. In order to evaluate the practicality of the directory, we have also implemented the B+-tree based directory and experimented the performance. According to the performance comparisons, the extendible hash directory has the better performance at insert, delete, and search operations. On the other hand, the B+-tree directory is better at sorting files.

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Channel Allocation Strategies for Interference-Free Multicast in Multi-Channel Multi-Radio Wireless Mesh Networks

  • Yang, Wen-Lin;Hong, Wan-Ting
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.2
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    • pp.629-648
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    • 2012
  • Given a video stream delivering system deployed on a multicast tree, which is embedded in a multi-channel multi-radio wireless mesh network, our problem is concerned about how to allocate interference-free channels to tree links and maximize the number of serviced mesh clients at the same time. In this paper, we propose a channel allocation heuristic algorithm based on best-first search and backtracking techniques. The experimental results show that our BFB based CA algorithm outperforms previous methods such as DFS and BFS based CA methods. This superiority is due to the backtracking technique used in BFB approach. It allows previous channel-allocated links to have feasibility to select the other eligible channels when no conflict-free channel can be found for the current link during the CA process. In addition to that, we also propose a tree refinement method to enhance the quality of channel-allocated trees by adding uncovered destinations at the cost of deletion of some covered destinations. Our aim of this refinement is to increase the number of serviced mesh clients. According to our simulation results, it is proved to be an effective method for improving multicast trees produced by BFB, BFS and DFS CA algorithms.

Speech emotion recognition based on genetic algorithm-decision tree fusion of deep and acoustic features

  • Sun, Linhui;Li, Qiu;Fu, Sheng;Li, Pingan
    • ETRI Journal
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    • v.44 no.3
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    • pp.462-475
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    • 2022
  • Although researchers have proposed numerous techniques for speech emotion recognition, its performance remains unsatisfactory in many application scenarios. In this study, we propose a speech emotion recognition model based on a genetic algorithm (GA)-decision tree (DT) fusion of deep and acoustic features. To more comprehensively express speech emotional information, first, frame-level deep and acoustic features are extracted from a speech signal. Next, five kinds of statistic variables of these features are calculated to obtain utterance-level features. The Fisher feature selection criterion is employed to select high-performance features, removing redundant information. In the feature fusion stage, the GA is is used to adaptively search for the best feature fusion weight. Finally, using the fused feature, the proposed speech emotion recognition model based on a DT support vector machine model is realized. Experimental results on the Berlin speech emotion database and the Chinese emotion speech database indicate that the proposed model outperforms an average weight fusion method.

Accuracy Measurement of Image Processing-Based Artificial Intelligence Models

  • Jong-Hyun Lee;Sang-Hyun Lee
    • International journal of advanced smart convergence
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    • v.13 no.1
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    • pp.212-220
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    • 2024
  • When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.