• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.246-252
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• 2017

In the defense acquisition, a company's goal is to maximize profits, and the government's goal is to allocate budgets efficiently. Each year, the government estimates the ratio of indirect cost sector to defense companies, and estimates the ratio to be applied when calculating cost of the defense articles next year. The defense industry environment is changing rapidly, due to the increasing trend of defense acquisition budgets, the advancement of weapon systems, the effects of the 4th industrial revolution, and so on. As a result, the cost structure of defense companies is being diversifying. The purpose of this study is to find an alternative that can enhance the rationality of the current methodology for estimating the ratio of indirect cost sector of defense companies. To do this, we conducted data analysis using the R language on the cost data of defense companies over the past six years in the Defense Integrated Cost System. First, cluster analysis was conducted on the cost characteristics of defense companies. Then, we conducted a regression analysis of the relationship between direct and indirect costs for each cluster to see how much it reflects the cost structure of defense companies in direct labor cost-based indirect cost rate estimates. Lastly a new ratio prediction model based on regularized regression analysis was developed, applied to each cluster, and analyzed to compare performance with existing prediction models. According to the results of the study, it is necessary to estimate the indirect cost ratio based on the cost character group of defense companies, and the direct labor cost based indirect cost ratio estimation partially reflects the cost structure of defense companies. In addition, the current indirect cost ratio prediction method has a larger error than the new model.

• Structural Engineering and Mechanics
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• v.72 no.4
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• pp.469-477
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• 2019

The understanding of tool-rock interaction mechanism is of high essence for improving the rock breaking efficiency and optimizing the drilling parameters in mechanical rock breaking. In this study, the tool-rock interaction models of indentation and cutting are carried out by employing the discrete element method (DEM) to examine the rock failure modes of various brittleness rocks and critical indentation and cutting depths of the ductile to brittle failure mode transition. The results show that the cluster size and inter-cluster to intra-cluster bond strength ratio are the key factors which influence the UCS magnitude and the UCS to BTS ratio. The UCS to BTS strength ratio can be increased to a more realistic value using clustered rock model so that the characteristics of real rocks can be better represented. The critical indentation and cutting depth decrease with the brittleness of rock increases and the decreasing rate reduces dramatically against the brittleness value. This effort may lead to a better understanding of rock breaking mechanisms in mechanical excavation, and may contribute to the improvement in the design of rock excavation machines and the related parameters determination.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.89-99
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• 2015

In this research, 1,032 data of precise safety inspection from 2004 to 2013 are gathered and constructed for finding effective safety inspection systems. Items are extracted from constructed data and factors for typology are decided with statistical method such as principle component analysis and cluster analysis. For factor decision, we extruded independent characteristics such as morphological and geographical characteristic, and deleted items which can be expressed by combination of independent characteristics. Four factors such as total storage, watershed ratio, levee length ratio, and spillway length ratio are extracted in this process. In cluster analysis, levee length ratio is excluded because it is not separated as cluster. Finally nine types of agricultural reservoir are extruded by total storage, watershed ratio, and spillway length ratio with frequency analysis.

• Journal of Periodontal and Implant Science
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• v.51 no.1
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• pp.30-39
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• 2021

Purpose: The present study was undertaken to examine whether periodontal probe visibility (PV) accurately reflects gingival thickness (GT) and to identify factors affecting PV using cluster and multivariate analyses. Methods: The clinical characteristics of the maxillary central incisors (n=90 subjects) were examined. Clinical photographs, sex, PV, probing depth, gingival width, papilla height, GT as measured with an ultrasonic device, and the ratio of crown width to crown length were recorded. Multivariate analysis, using multinomial baseline-category logistic regression, was used to identify factors predictive of PV. Cluster analysis was used to identify gingival biotypes. Results: In the multivariate analysis, sex was the only significant predictor of PV (odds ratio, 6.48). Two clusters of subjects were created based on morphometric parameters. The mean GT among cluster A subjects was significantly lower than that among cluster B subjects (P=0.015). No significant difference was found between cluster A and B subjects in terms of PV score (P=0.583). Conclusions: Periodontal PV was not associated with GT as measured directly using an ultrasonic device. Sex was a highly significant predictor of periodontal PV. GT was found to be correlated with morphological characteristics of the periodontium.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4644-4661
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• 2015

Wireless sensor networks are often organized in the form of clusters leading to the new framework of WSN called cluster or hierarchical WSN where each cluster head is responsible for its own cluster and its members. These hierarchical WSN are prone to various routing layer attacks such as Black hole, Gray hole, Sybil, Wormhole, Flooding etc. These routing layer attacks try to spoof, falsify or drop the packets during the packet routing process. They may even flood the network with unwanted data packets. If one cluster head is captured and made malicious, the entire cluster member nodes beneath the cluster get affected. On the other hand if the cluster member nodes are malicious, due to the broadcast wireless communication between all the source nodes it can disrupt the entire cluster functions. Thereby a scheme which can detect both the malicious cluster member and cluster head is the current need. Abnormal energy consumption of nodes is used to identify the malicious activity. To serve this purpose a learning based energy prediction algorithm is proposed. Thus a two level energy prediction based intrusion detection scheme to detect the malicious cluster head and cluster member is proposed and simulations were carried out using NS2-Mannasim framework. Simulation results achieved good detection ratio and less false positive.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.145-151
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• 2011

This study assumed the competitive structure based on genre classification for Korean online game contents market, and carried out the analysis on the degree and characteristics of the competition that appear differently in each sub market classified according to the genre. First, to analyze the market power of the rank 1 and 2 game contents in each genre, using the play time share ratio and standard deviation statistics values in the genre, ANOVA analysis and Cluster analysis were carried out for each genre. According to ANOVA analysis result, in the rank 1 game share ratio in each genre, there was a relationship of 'FPS/Racing > RST/Sports > Poker > Go-stop > RPG > Arcade > Board', and in the play time total share ratio of rank 1 and 2 games, the relationship of 'RTS > FPS/Racing > Sports > RPG > Go-stop > Poker > Arcade > Board' was verified. And in Cluster analysis, the groups of the genres with the degree of market power tendency and the variability at similar level were classified and stated.

• Environmental Engineering Research
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• v.20 no.3
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• pp.285-289
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• 2015

Complicated and expensive seawater desalination technology is a big challenge in boron removal process. Conventional seawater desalination process of coagulation utilized for pre-treatment is difficult to remove boron. Boron can be removed more effectively in Reverse Osmosis (RO) process than any other processes. In this study, a coagulant with the name Mineral Cluster was examined its boron removal ability. Boron removal efficiency of Mineral Cluster depended on pH value and Mineral Cluster dosage. Desalination process combines the pre-treatment process with Mineral cluster diluted at the ratio of 1:2500 and the RO membrane process. The original sea water could be desalinated to drinking water quality, 1 mg/L, without any pH adjustments. Therefore, if the Mineral cluster is added without any other chemicals for adjusting pH, the desalination process would be much safer, efficient and economical.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.57-61
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• 2014

This study measured the stream data from the several sensors, and stores the database in MapReduce framework environment, and it aims to design system with the small performance and cluster analysis error rate through the KMSVM algorithm. Through the KM-SVM algorithm, the cluster analysis effective data was used for U-health system. In the results of experiment by using 2003 data sets obtained from 52 test subjects, the k-NN algorithm showed 79.29% cluster analysis accuracy, K-means algorithm showed 87.15 cluster analysis accuracy, and SVM algorithm showed 83.72%, KM-SVM showed 90.72%. As a result, the process speed and cluster analysis effective ratio of KM-SVM algorithm was better.

• Journal of Communications and Networks
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• v.17 no.1
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• pp.84-92
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• 2015

Multi-hop relay networks have been widely considered as a promising solution to extend the coverage area and to reduce the deployment cost by deploying the relay stations (RSs) in mobile communication systems. Suitable deployment for the RSs is one of the most important features of the demand nodes (DNs) to obtain a high data transmission rate in such systems. Considering a tradeoff among the network throughput, the deployment budget, and the overall coverage of the systems, efficient RS deployment schemes and corresponding algorithms must be developed and designed. A novel cluster-based RS deployment scheme is proposed in this paper to select the appropriate deployment locations for the relay stations from the candidate positions. To make an ideal cluster distribution, the distances between the DNs are calculated when deploying the RSs. We take into account the traffic demands and adopt a uniform cluster concept to reduce the data transmission distances of the DNs. On the basis of the different candidate positions, the proposed scheme makes an adaptive decision for selecting the deployment sites of the RSs. A better network throughput and coverage ratio can be obtained by balancing the network load among the clusters. Simulation results show that the proposed scheme outperforms the previously known schemes in terms of the network throughput and the coverage ratio. Additionally, a suitable deployment budget can be implemented in multi-hop relay networks.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.185-194
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• 2020

Designing of a hierarchical clustering algorithm is one of the numerous approaches to minimize the energy consumption of the Wireless Sensor Networks (WSNs). In this paper, a homogeneous and randomly deployed sensor nodes is considered. These sensors are energy constrained elements. The nominal selection of the Cluster Head (CH) which falls under the clustering part of the network protocol is studied and compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. CHs in this proposed process is the function of total remaining energy of each node as well as total average energy of the whole arrangement. The algorithm considers initial energy, optimum value of cluster heads to elect the next group of cluster heads for the network as well as residual energy. Total remaining energy of each node is compared to total average energy of the system and if the result is positive, these nodes are eligible to become CH in the very next round. Analysis and numerical simulations quantify the efficiency and Average Energy Ratio (AER) of the proposed system.