• 한국통신학회논문지
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• 제31권11A호
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• pp.1037-1043
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• 2006

본 논문은 인터넷 상에서 동기화된 실시간 미디어 멀티캐스트 서비스에 적합한 오버레이 멀티캐스트 트리를 구성하는 알고리즘을 제안한다. 제안하는 알고리즘은 멀티캐스트 송신 단에서 각 종단 시스템 사이의 전송 지연에 대한 평균과 변이를 최소화하는 트리 구성을 찾는다. 그러나 위의 문제는 NP-완전하므로, 이러한 문제의 계산 복잡도를 낮추면서, 근사해를 찾기 위한 방법으로 OGA (Orthogonal Genetic Algorithm)을 이용하였다. 실험 결과에서 제안하는 알고리즘이 기존 알고리즘에 비해 통기화된 실시간 미디어 데이터 전송을 위한 효과적인 트리를 구성한다는 것을 보인다.

• 대한산업공학회지
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• 제26권2호
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• pp.171-182
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• 2000

For the last five years, data mining has drawn much attention by researchers and practitioners because of its many applicable domains. This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database. Among many data mining methods, intelligent decision tree building algorithm is especially of interest in the sense that it enables the automatic generation of decision rules from the tree, facilitating the construction of expert system. On the basis of experiment using semiconductor etching machine, it has been verified that our model outperforms previously proposed decision tree models.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Geomechanics and Engineering
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• 제29권5호
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.160-165
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• 2022

This paper proposes an efficient method of reconstructing interconnections when the terminals of each plane change in real-time situations where randomly divided planes are interconnected. To connect all terminals when the terminals of each plane are changed, we usually reconstruct the interconnections between all terminals. This ensures a minimum connection length, but it takes considerable time to reconstruct the interconnection for the entire terminal. This paper proposes a solution to obtain an optimal tree close to the minimum spanning tree (MST) in a short time. The construction of interconnections has been used in various design-related areas, from networks to architecture. One of these areas is an ad hoc network that only consists of mobile hosts and communicates with each other without a fixed wired network. Each host of an ad hoc network may appear or disappear frequently. Therefore, the heuristic proposed in this paper may expect various cost savings through faster interconnection reconstruction using the given information in situations where the connection target is changing.

• 한국BIM학회 논문집
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• 제6권4호
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.

• 한국통신학회논문지
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• 제34권12B호
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• pp.1399-1406
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• 2009

인터넷 방송에서 그룹간의 통신을 위해서는 효율적이고 확장 가능한 멀티캐스트 메커니즘이 필요하다. 오버레이 멀티캐스트의 성능 향상을 위해서는 멀티캐스트 트리의 최적화가 요구된다. 이러한 최적화 문제는 NP-complete로 알려져 있다. 따라서 오버레이 멀티캐스트 트리의 각 노드들이 out-degree가 제한되어 있을 경우, 새로운 참여자는 이미 그룹에 참여된 사용자들 중 자신에게 적합한 부모노드를 효율적으로 찾아 그룹참여를 하여야 한다. 본 논문에서는 트리기반의 오버레이 멀티캐스트 구성 시, 새로운 사용자는 루트노드와의 지연시간을 측정하여 level을 설정한다. 이 후 새로운 사용자는 ACK-SEND기법을 사용하여 후보 부모노드를 효과적으로 찾고 level값을 비교하여 자신에 적합한 위치를 찾아 참여하게 된다. 각각의 노드들은 제공자 노드와 가까운 노드일수록 트리 깊이가 낮은 곳에 위치하게 된다. 또한 장애 발생 시, ACK-SEND기법을 사용하여 빠른 복구를 보장할 수 있다. 결국 신규 노드는 효율적이고 빠르게 멀티캐스트 트리에서 적합한 위치를 찾아 참여가 이뤄지는 장점이 있다.

• 정보처리학회논문지C
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• 제8C권1호
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• pp.60-67
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• 2001

인터넷에서 멀티미디어 서비스를 지원하기 위한 멀티캐스트 프로토콜은 트리 구성 방식에 따라 공유 트리 방식과 소스 기반 트리 방식으로 나눌 수 있는데, 공유 트리 방식이 확장성 측면에서 보다 우수하다고 알려져 있다. 공유 트리 방식에서 QoS를 만족시키기 위해 고려해야 할 핵심 사항 중 하나인 RP (Rendezvous Point) 선정에 대하여 일반적으로 QoS 제약 조건에 따라 별도의 과정을 거쳐 RP를 계산하는 방식으로 연구가 되고 있다. 또한 멀티캐스트 그룹 멤버가 동적으로 가입 또는 탈퇴를 할 경우에는 초기에 설정된 RP가 신규 멤버까지는 QoS를 만족시키지 못하는 경우가 발생하게 되므로, 신규멤버 까지 QoS를 보장하는 새로운 RP를 재선정할 필요가 있는데, RP재선정에 관한 기존 연구는 매우 미흡한 실정이다. 본 논문에서는 공유 트리 방식에서 RP 초기 선정 방식과 RP 재선정 방식에 RTCP(Real Time Control Protocol) 패킷을 이용하는 새로운 기법을 제안한다. 본 논문에서 제안하는 방식은 멀티미디어 서비스를 제공할 때 이용하는 RTCP 패킷을 그대로 활용함으로써 RP계산을 위한 별도의 정보 수집 과정을 필요로 하지 않는다. 모의 실험을 통하여 제안된 방식이 초기 RP선정 시 에는 임의 선정, 위상 기반 선정 방식보다 40∼50% 정도, 멀티캐스트 그룹멤버의 동적 변화 시에는 초기 RP를 그대로 이용하는 방식보다 50% 정도의 개선 효과를 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권12호
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• pp.3219-3236
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• 2012

A tree routing structure is often adopted for many-to-one data gathering and aggregation in sensor networks. For real-time scenarios, considering lossy wireless links, it is an important issue how to construct a maximum-lifetime data gathering tree with delay constraint. In this work, we study the problem of lifetime-preserving and delay-constrained tree construction in unreliable sensor networks. We prove that the problem is NP-complete. A greedy approximation algorithm is proposed. We use expected transmissions count (ETX) as the link quality indicator, as well as a measure of delay. Our algorithm starts from an arbitrary least ETX tree, and iteratively adjusts the hierarchy of the tree to reduce the load on bottleneck nodes by pruning and grafting its sub-tree. The complexity of the proposed algorithm is $O(N^4)$. Finally, extensive simulations are carried out to verify our approach. Simulation results show that our algorithm provides longer lifetime in various situations compared to existing data gathering schemes.

• Journal of Computing Science and Engineering
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• 제9권2호
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• pp.83-97
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• 2015

IEEE 802.15.4 is a global standard designed for emerging applications in low-rate wireless personal area networks (LR-WPANs). The standard provides beneficial features, such as a beacon-enabled mode and guaranteed time slots for realtime data delivery. However, how to optimally operate those features is still an open issue. For the optimal operation of the features, this paper proposes a holistic optimization method that jointly optimizes three cross-related problems: cluster-tree construction, nodes' power configuration, and duty-cycle scheduling. Our holistic optimization method provides a solution for those problems so that all the real-time packets can be delivered within their deadlines in the most energy-efficient way. Our simulation study shows that compared to existing methods, our holistic optimization can guarantee the on-time delivery of all real-time packets while significantly saving energy, consequently, significantly increasing the lifetime of the network. Furthermore, we show that our holistic optimization can be extended to take advantage of the spatial reuse of a radio frequency resource among long distance nodes and, hence, significantly increase the entire network capacity.