• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.79-86
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• 2019

For decades, in-memory data structures have been designed for DRAM-based main memory that provides symmetric read/write performances and has no limited write endurance. However, such data structures provide sub-optimal performance for NVM as it has different characteristics to DRAM. With this motivation, we rethink a conventional red-black tree in terms of its efficacy under NVM settings. The original red-black tree constantly rebalances sub-trees so as to export fast access time over dataset, but it inevitably increases the write traffic, adversely affecting the performance for NVM with a long write latency and limited endurance. To resolve this problem, we present a variant of the red-black tree called a hierarchical balanced binary search tree. The proposed structure maintains multiple keys in a single node so as to amortize the rebalancing cost. The performance study reveals that the proposed hierarchical binary search tree effectively reduces the write traffic by effectively reaping the high capacity of NVM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3329-3350
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• 2017

Installing caching and computing facilities in mobile edge networks is a promising solution to cope with the challenging capacity and delay requirements imposed on future mobile communication systems. The problem of optimal facility placement in mobile edge networks has not been fully studied in the literature. This is a non-trivial problem because the mobile edge network has a unidirectional topology, making existing solutions inapplicable. This paper considers the problem of optimal placement of a fixed number of facilities in a mobile edge network with an arbitrary tree topology and an arbitrary demand distribution. A low-complexity sequential algorithm is proposed and proved to be convergent and optimal in some cases. The complexity of the algorithm is shown to be $O(H^2{\gamma})$, where H is the height of the tree and ${\gamma}$ is the number of facilities. Simulation results confirm that the proposed algorithm is effective in producing near-optimal solutions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1C
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• pp.20-25
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• 2006

In this paper, the performance of Tree-LDPC code is presented based on the min-sum algorithm with scaling and the asymptotic performance in the water fall region is shown by density evolution. We presents that the Tree-LDPC code show a significant performance gain by scaling with the optimal scaling factor which is obtained by density evolution methods. We also show that the performance of min-sum with scaling is as good as the performance of sum-product while the decoding complexity of min-sum algorithm is much lower than that of sum-product algorithm. The Tree-LDPC decoder is implemented on a FPGA chip with a small interleaver size.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3138-3150
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• 2021

Multimedia services on the Internet are continuously increasing. Accordingly, the demand for a technology for efficiently delivering multimedia traffic is also constantly increasing. The multicast technique, that delivers the same content to several destinations, is constantly being developed. This technique delivers a content from a source to all destinations through the multicast tree. The multicast tree with low cost increases the utilization of network resources. However, the finding of the optimal multicast tree that has the minimum link costs is very difficult and its calculation complexity is the same as the complexity of the Steiner tree calculation which is NP-complete. Therefore, we need an effective way to obtain a multicast tree with low cost and less calculation time on SDN-based smart network platforms. In this paper, we propose a new multicast tree generation algorithm which produces a multicast tree using an agent trained by model-based meta reinforcement learning. Experiments verified that the proposed algorithm generated multicast trees in less time compared with existing approximation algorithms. It produced multicast trees with low cost in a dynamic network environment compared with the previous DQN-based algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1609-1615
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• 2006

Overlay multicast technique is an effective way as an alternative to IP multicast. Traditional IP multicast is not widely deployed because of the complexity of IP multicast technology and lack of application. But overlay multicast can be easily deployed by effectively reducing complexity of network routers. Because overlay multicast resides on top of densely connected IP network, In case of multimedia streaming service over overlay multicast tree, real-time data is sensitive to end-to-end delay. Therefore, moderate algorithm's development to this network environment is very important. In this paper, we are interested in minimizing maximum end-to-end delay in overlay multicast tree. The problem is formulated as a degree-bounded minimum delay spanning tree, which is a problem well-known as NP-hard. We develop tabu search heuristic with intensification and diversification strategies. Robust experimental results show that is comparable to the optimal solution and applicable in real time

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.10-15
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• 2012

This paper considers an economic design problem of a tree-based network which is a kind of computer network. This problem can be modeling to be an objective function to minimize installation costs, on the constraints of spanning tree and maximum traffic capacity of sub tree. This problem is known to be NP-hard. To efficiently solve the problem, a tabu list coupled genetic algorithm approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a genetic algorithm approach.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.9-18
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• 1997

Abstract This paper presents a heuristic method for optimal design of water distribution system with multiple sources and potential links. In multiple source pipe network, supply rate at each source node affects the total cost of the system because supply rates are not uniquely determined. The Linear Minimum Cost Flow (LMCF) model may be used to a large scale pipe network with multiple sources to determine supply rate at each source node. In this study the heuristic method based on the LMCF is suggested to determine supply rate at each source node and then to optimize the given layout. The heuristic method in turn perturbs links in the longest path of the network to obtain the supply rates which make the optimal design of the pipe network. Once the best tree network is obtained, the frequency count of reconnecting links by considering link failure is in turn applied to form loop to enhance the reliability of the best tree network. A sample pipe network is employed to test the proposed method. The results show that the proposed method can yield a lower cost design than the LMCF alone and that the proposed method can be efficiently used to design irrigation systems or rural water distribution systems.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.4
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• pp.259-275
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• 2021

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.

• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.169-178
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• 2011

Recently, data sharing between enterprises or organizations is required matter for task cooperation. In this process, when the enterprise opens its database to the affiliates, it can be occurred to problem leaked sensitive information. To resolve this problem it is needed to hide sensitive information from the database. Previous research hiding sensitive information applied different heuristic algorithms to maintain quality of the database. But there have been few studies analyzing the effects on the items modified during the hiding process and trying to minimize the hided items. This paper suggests eFP-Tree(Extended Frequent Pattern Tree) based FP-Tree(Frequent Pattern Tree) to hide sensitive frequent itemsets. Node formation of eFP-Tree uses border to minimize impacts of non sensitive frequent itemsets in hiding process, by organizing all transaction, sensitive and border information differently to before. As a result to apply eFP-Tree to the example transaction database, the lost items were less than 10%, proving it is more effective than the existing algorithm and maintain the quality of database to the optimal.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.