• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.3008-3028
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• 2022

In this paper, we investigate traffic scheduling for a delay-sensitive multi-hop relay network, and aim to minimize the priority-based end-to-end delay of different data packet via joint relay selection, subcarrier assignment, and power allocation. We first derive the priority-based end-to-end delay based on queueing theory, and then propose a two-step method to decompose the original optimization problem into two sub-problems. For the joint subcarrier assignment and power control problem, we utilize an efficient particle swarm optimization method to solve it. For the relay selection problem, we prove its convexity and use the standard Lagrange method to deal with it. The joint relay selection, subcarriers assignment and transmission power allocation problem for each hop can also be solved by an exhaustive search over a finite set defined by the relay sensor set and available subcarrier set. Simulation results show that both the proposed routing scheme and the resource allocation scheme can reduce the average end-to-end delay.

• Genomics & Informatics
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• 제21권3호
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• 지식경영연구
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• 제13권3호
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• pp.55-77
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• 2012

Selecting promising technology is becoming more and more difficult due to the increased number and complexity. In this study, we propose hybrid AHP/DEA-AR method and hybrid AHP/DEA-AR-G method to evaluate efficiency of technology alternatives based on ordinal rating data collected through survey to technology experts in a certain field and select efficient technology alternative as promising technology. The proposed method normalizes rating data and uses AHP to derive weights to improve the credibility of analysis, then in order to avoid basic DEA models' problems, use DEA-AR and DEA-AR-G to evaluate efficiency of technology alternatives. In this study, we applied the proposed methods to clean technology and compared with the basic DEA models. According to the result of the comparison, we can find that the both proposed methods are excellent in confirming most efficient technology, and hybrid AHP/DEA-AR method is much easier to use in the process of technology selection.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 춘계 학술논문 발표대회
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• pp.298-299
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• 2014

As a governmental plan for real estate revitalization, remodeling vertical extension has been permitted. Thus, the Ministry of Land, Infrastructure and Transport preannounced proclaiming the revised Housing Act and establishing the remodeling basic plan, and it is anticipated that the remodeling market will be revitalized in earnest after the enforcement of remodeling vertical extension(April 25th. 2014). As vertical extension is applicable up to 3 stories, the safety of building for remodeling is becoming important, so most remodeling construction works use various methods for structural reinforcement. In this process, the selection of structural reinforcement method has depended on structural engineer's experience and knowledge and there has been a limitation in selecting the optimum structural reinforcement method which considers remodeling project characteristics. Therefore, this study analyzed the factors to determine the kinds of structural reinforcement method in a remodeling project and suggested a process to select the best structural reinforcement method of remodeling construction.

• 디지털산업정보학회논문지
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• 제4권1호
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• pp.9-16
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• 2008

This paper presents a frequency selection algorithm for minimization power consumption of processor in Mobile System. The proposed algorithm has processor designed low power processor using clock gating method. Clock gating method has improved the power dissipation by control main clock through the bus which is embedded clock block applying the method of clock gating. Proposed method has compared power consumption considered the dynamic power for processor, selected frequency has considered energy gain and energy consumption for designed processor. Or reduced power consumption with decreased processor speed using slack time. This technique has improved the life time of the mobile systems by clock gating method, considered energy and using slack time. As an results, the proposed algorithm reduce average power saving up to 4% comparing to not apply processor in mobile system.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2000년도 학술발표회 발표논문집
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• pp.96-101
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• 2000

Nowadays, everyone emphasis the important of environment. it is found that try to apply using the naturally favorable method when arrange irrigation and drainage channel. But we have no accurate standard of region selection yet. so it is make a many problem. The purpose of this study is the making of standard which is optimal region selection for the creation of the naturally favorable waterfront area. We surveyed data of twenty site in korea where are managed by the KARICO(Korea Agricultural Rural Infrastructure Corporation). We analysed the data using suitable three method(simple adding point method, subjectivity decision method, checklist method) for purpose of this study.

• 대한기계학회논문집
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• 제12권6호
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• pp.1246-1251
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• 1988

본 연구에서는 균열 첨단의 극좌표를 직각좌표(x, y)로 변환시킨 후 슬랩상사 이론을 적용시켜 균열첨단 부근에서 많은 데이터의 측정이 가능한 새로운 해석법인 다 점 선정법(multi-point selection method)을 개발하였다.또 이 해석법으로 유한판 중앙 직선균열의 응력확대계수를 해석하여 종래 해석법의 결과와 비교 검토하여 그 유 용성을 확인하였다.

• 한국군사과학기술학회지
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• 제13권4호
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• pp.586-593
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• 2010

In this paper, I have proposed that the 1st and 2nd AC-DC transformation methods using multi-level pulsating currents and selection switches. Through making the rectified voltage of the proposed AC-DC translation which is similar to reference voltage by selecting from multi-level pulsating currents, the proposed translation has dramatically reduced the ripple voltage. I have compared the performance of the DC voltage, the ripple voltage and the peak to peak voltage of the proposed method with the conventional method. The simulation results show that the proposed 2nd method has the better performance than the 1st method in the point of average DC voltage drop and peak to peak voltage increase.