• Journal of information and communication convergence engineering
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• 제22권3호
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• pp.207-214
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• 2024

In wireless sensor networks, the implementation of routing protocols is crucial owing to their limited computational capacities. Tree routing is a suitable method in wireless sensors owing to its minimal routing overhead. However, single-hop metric schemes, such as hop count, cause congestion at specific nodes, whereas multiple metric schemes cannot control dynamically changing network environments. To address these issues, we propose a scheme to implement enhanced tree routing with adaptive metrics based on hop count. This approach assigns different weights to metrics to select suitable parent nodes based on hop count. The parent-selection algorithm utilizes hop count, buffer occupancy, and received signal strength indicator (RSSI) as metrics. This study evaluates the performance through simulation scenarios to analyze whether improvements can be made to address problems encountered in traditional tree routing. The performance metrics include packet delivery speed, throughput, and end-to-end delay, which vary depending on the volume of network traffic.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권12호
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• pp.623-634
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• 2016

현존하는 빈발 패턴 마이닝 방법은 대부분 시간 효율성을 목표로 하고, 물리적 메모리 사용에 매우 의존적이다. 하지만 빅데이터 시대가 도래함에 따라 실제 세상의 데이터베이스는 급속도로 증가하고 있으며, 그에 따라 기존의 방법으로 현실적인 거대한 양의 데이터를 마이닝하기에 물리적 메모리 공간이 부족한 실정이다. 이러한 문제를 해결하기 위해, 빈발 패턴 마이닝의 메모리 의존성을 줄이기 위한 보조저장장치 기반의 연구들이 진행되었으나, 메모리 기반의 방법들에 비해 처리 시간이 너무 많이 소비된다는 한계가 있었다. 따라서 확장성을 가지며, 기존의 디스크 기반의 방법들에 비해 시간효율성을 높인 새로운 빈발 패턴 마이닝이 필요하게 되었다. 본 논문에서는 빅데이터로부터 빈도 아이템 집합들을 마이닝하기 위해 메모리와 디스크를 함께 사용하는 스택 기반의 새로운 접근법인 PPFP 알고리즘을 제안하였다. PPFP는 빈발 패턴 마이닝 접근법 중 가장 인기 있고 효율적인 접근법 중 하나인 FP-growth를 기반으로 하고 있다. PPFP 마이닝 방법은 다음과 같이 두 단계로 진행된다. (1) IFP-tree 구축: FP-tree를 생성한 후, 새로운 인덱스 번호 부여 방법으로 FP-tree의 각 노드에 인덱스 번호를 부여하고, 이 인덱스 번호가 부여된 FP-tree(IFP-tree)를 테이블로 변환하여(IFP-table) 디스크에 저장한다. (2) PPFP 알고리즘을 이용한 빈발 패턴 마이닝: 스택 기반의 PUSH-POP 방식으로 패턴을 확장시켜 나가며 빈발 패턴을 마이닝한다. 이러한 방식을 통해 메모리 기반의 방법에 비해 반복적으로 많은 시간이 소모되는 연산에 매우 적은 양의 메모리를 활용하여 확장성과 함께 시간효율성 또한 향상시킬 수 있었다. 그리고 기존의 연구 방법들과 비교 실험을 통해 새로운 알고리즘의 성능을 증명하였다.

• Journal of Forest and Environmental Science
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• 제32권2호
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• pp.129-139
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• 2016

Study was conducted to investigate tree diversity and its population and regeneration status in homegardens of upper Assam, Northeast India through field study by quadrat method. A total of 154 tree species have been recorded from 135 studied homegardens under 109 genera 53 families. Most of these species (79%) are indigenous to our country, while the rest (21%) arealiens (naturalized and cultivated exotics) by origin. Tree species richness per homegarden varies greatly in different homegardens and is ranged from 5 to 52 tree species with a mean of 22 ($SE{\pm}0.58$). A. malaccensis is the most dominant tree species in the studied homegardens contributed 34% of the total tree density of the documented trees. The tree density is much higher with 4,259 individuals $ha^{-1}$ but, basal area ($36.32m^2ha^{-1}$) is very less. Based on the number of individuals present, very rare species is accounted for 10%, rare species 39%, common species 19%, dominants 14% and predominant species 18% in the present study. The population density of 154 tree species is 4,259 (individuals $ha^{-1}$) for adults (>3.18 cm DBH), 5,902 (individuals $ha^{-1}$) for saplings and 38,164 (individuals $ha^{-1}$) for seedlings. The density of seedlings>saplings>adults represents good regeneration status of tree species in studied homegardens. The population structure study showed that about 8% tree species have good regeneration status, 9% have fair regeneration status, 48% have poor regeneration status and 34% tree species have no regeneration. Study suggests that research and development action is needed to stimulate regeneration of those tree species which having high importance value indices but showing poor or no regeneration. Based on present observation, it can be conclude that homegarden can emerge as an effective means for both economic well-being and biodiversity conservation in upper Assam, Northeast India.

• 한국정보과학회논문지:정보통신
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• 제28권4호
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• pp.611-620
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• 2001

최선의 전송(Best Effort) 서비스를 제공하는 현재의 IP 멀티캐스트 서비스의 특성상 신뢰전송을 요구하는 각종 응용들의 요구를 만족시키기 위해서는 추가적인 신뢰전송 프로토콜이 필요하다. 이러한 요구에 따라 신뢰적인 멀티캐스트 전송 프로토콜에 대한 연구가 수행되고 있는 가운데, 계층적 트리를 구축하여 신뢰성을 보장하는 노력이 활발하게 진행되고 있다. 계층적 트리 기반의 방식은 높은 확장성을 보장하면서 효율적으로 신뢰성을 보장하지만 전송단계에서 효율적인 논리 트리 구축 방안이 제공되어야 한다. 논리적인 제어 트리 구축은 수신자 기반의 상향식 (bottom-up) 방식이 주로 사용되어 왔으나 이 방법은 병렬적 트리 구성을 통하여 신속한 트리 구축을 할 수 있다는 장점이 있지만 제어 트리 상에 루프 (loop)가 발생할 수 있다는 단점과 메시지 부하가 커지는 단점이 있다. 이에 본 논문은 하향식(top-down) 기반의 제어 트리 구축 방안을 제안한다. 하향식 방식은 단계적인 트리 구축을 통하여 루프 발생을 방지 할 수 있다. 또한 성능평가를 통하여 메시지 부하를 줄일 수 있다는 것을 보였다. 본 논문은 응용의 요구 사항에 맞추어 상향식과 하향식을 선택적으로 사용할 것을 제안한다.

• Journal of information and communication convergence engineering
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• 제10권3호
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• pp.264-268
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• 2012

Tree-based structures offer assured optimal paths from the data source to the sink. Shortest routes are disregarded since these do not consider the remaining energy level of the nodes. This shortens the lifetime of the whole network. Most tree-based routing protocols, although aware of the nodes' energy, do not consider an energy aware sleep scheduling scheme. We propose an energy-aware sleep scheduling (EASS) scheme that will improve the sleep scheduling scheme of an existing tree-based routing protocol. An energy harvesting structure will be implemented on the wireless sensor network. The depth of sleep of every node will be based on the harvested energy.

• Journal of Information Processing Systems
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• 제14권2호
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• Journal of Power Electronics
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• 제8권1호
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• pp.101-107
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• 2008

In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.

• 한국물환경학회지
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• 제31권1호
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• pp.19-28
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• 2015

The purpose of this research is reconstruction of annual precipitation based on Tree-ring series at Seorak mountain and examine its effectiveness. To do so we performed nonlinear time series characteristics test of Tree-ring series and reconstructed annual precipitation of Gangneung from 1687 to 1911 using Artificial neural network and Nonlinear autoregressive exogeneous input (NARX) model which reflects stochastic properties. As a result, Tree-ring series at Seorak Mountain shows nonlinear time series property and reconstructed annual precipitation series drawn from NARX is similar in statistical characteristics of observed annual time series.

• 경영과학
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• 제23권1호
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• pp.63-74
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• 2006

Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.432-439
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• 2022

This paper introduces deliberative behavior tree using utility theory. The proposed approach combine the strengths of behavior trees and utility theory to implement complex behavior of AI agents in an easier and more concise way. To achieve this goal, we devised and implemented three types of additional behavior tree nodes, which evaluate utility values of its own node or its subtree while traversing and selecting its child nodes based on the evaluated values. In order to validate our approach, we implemented a sample scenario using conventional behavior tree and our proposed deliberative tree respectively. And then we compared and analyzed the simulation results.