• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.453-458
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• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.264-267
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• 2002

This paper proposed a effective priority selection algorithm named weighted round-robin algorithm and show the implementation result of DMAC priority selection module using prosed weighted round-robin algorithm. I parameterize timing constraints of each functional module, which decide the effectiveness of system. Proposed weighted round-robin algorithm decide the most effective module for data transmission using parameterize timing constraints and update timing parameter of each module for next transmission module selection. I implement DMAC priority selection module using this weighted round-robin algorithm and can improve the timing effective for data transmission from memory to functional module or one functional module to another functional module.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.179-189
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• 2023

Hybrid sufficient dimension reduction (SDR) methods to a weighted mean of kernel matrices of two different SDR methods by Ye and Weiss (2003) require heavy computation and time consumption due to bootstrapping. To avoid this, Park et al. (2022) recently develop the so-called cross-distance selection (CDS) algorithm. In this paper, two variations of the original CDS algorithm are proposed depending on how well and equally the covk-SAVE is treated in the selection procedure. In one variation, which is called the larger CDS algorithm, the covk-SAVE is equally and fairly utilized with the other two candiates of SIR-SAVE and covk-DR. But, for the final selection, a random selection should be necessary. On the other hand, SIR-SAVE and covk-DR are utilized with completely ruling covk-SAVE out, which is called the smaller CDS algorithm. Numerical studies confirm that the original CDS algorithm is better than or compete quite well to the two proposed variations. A real data example is presented to compare and interpret the decisions by the three CDS algorithms in practice.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8B
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• pp.657-667
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• 2012

In the MPR selection algorithm of Optimized Link State Routing (OLSR), each node selects own MPRs independently, so most of nodes are selected to MPR at least once. To cope with this problem, the MPR candidate selection algorithm was proposed. The MPR candidate selection algorithm can reduce the number of MPRs, but the efficiencies of route and connectivity decline due to decreased number of MPRs. So, in this paper, we propose the Significant Multi-Point Relay (S-MPR) selection algorithm which can enhance the performance of ad hoc network by improving the MPR selection algorithm of OLSR. In proposed S-MPR selection algorithm, each node selects the most important node to S-MPR to guarantee the connectivity then selects remaining MPRs in MPR candidates. So proposed S-MPR selection algorithm can reduce the overhead of many MPRs without decline of routing performance. To show the performance gain of proposed S-MPR selection algorithm, we simulate the proposed S-MPR selection algorithm by using OPNET.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3501-3523
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• 2017

It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

• Journal of Information Processing Systems
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• v.2 no.2
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• pp.67-75
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• 2006

In order to acquire a precise and quick response to an analytical query, proper selection of the views to materialize in the data warehouse is crucial. In traditional view selection algorithms, all relations are considered for selection as materialized views. However, materializing all relations rather than a part results in much worse performance in terms of time and space costs. Therefore, we present an improved algorithm for selection of views to materialize using the clustering method to overcome the problem resulting from conventional view selection algorithms. In the presented algorithm, ASVMRT (Algorithm for Selection of Views to Materialize using Reduced Table), we first generate reduced tables in the data warehouse using clustering based on attribute-values density, and then we consider the combination of reduced tables as materialized views instead of a combination of the original base relations. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs are approximately 1.8 times better than conventional algorithms.

• Journal of Construction Engineering and Project Management
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• v.7 no.2
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• pp.1-10
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• 2017

The Clonal Selection Algorithm (CSA) is an algorithm inspired by the human immune system mechanism. In CSA, several parameters needs to be optimized by large amount of sensitivity analysis for the optimal results. They limit the accuracy of the results due to the uncertainty and subjectivity. Adaptive Clonal Selection (ACS), a modified version of CSA, is developed as an algorithm without controls by pre-defined parameters in terms of selection process and mutation strength. In this paper, we discuss the ACS in detail and present its implementation in construction site utilization planning (CSUP). When applied to a developed model published in research literature, it proves that the ACS are capable of searching the optimal layout of temporary facilities on construction site based on the result of objective function, especially when the parameterization process is considered. Although the ACS still needs some improvements, obtaining a promising result when working on a same case study computed by Genetic Algorithm and Electimze algorithm prove its potential in solving more complex construction optimization problems in the future.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1157-1160
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• 2003

We proposed a study on optimal clock period selection algorithm for low power RTL design. The proposed algorithm use the way of maintaining the throughput by reducing supply voltage after improve the system performance in order to minimize the power consumption. In this paper, it select the low power to use pipeline in the transformation of architecture. Also, the algorithm is important the clock period selection in order to maximize the resource sharing. however, it execute the optimal clock period selection algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.213-214
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• 2011

This paper presents a distributed relay selection algorithm for cooperative communication. The algorithm separates the decision making into two simple steps, decision making for employing cooperative communication and decision making for relay selection.