• 한국군사과학기술학회지
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• 제14권2호
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• pp.305-312
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• 2011

Bacillus anthracis(Ba) is a Gram-positive spore-forming bacterium that causes the disease anthrax. The feature of Ba is the presence of two large virulence plasmids, pXO1 and pXO2. Molecular genotyping of Ba has been difficult to the lack of polymorphic DNA marker. Ba isolated from Korea has been genotyped using various nucleotide analysis methods, such as 16s rDNA sequencing and multiple-locus variable-number tandem repeat (MLVA) analysis. We identified genotypes that represent a genetic lineage in the B1 cluster. This study emphasized the need to perform molecular genotyping when attempting to verify a strain-specific Ba.

• 한국멀티미디어학회논문지
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• 제12권12호
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• pp.1769-1777
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• 2009

With the advent of the ubiquitous technology age, the progress of network technology has enabled a robust sensor communication, not just in cities, but also in poor surroundings such as deserts, polar regions, or underwater environments. In this paper, we propose a Multiple Acknowledgement (MA) technique to replace the conventional Automatic Repeat request (ARQ) technique. The MA mechanism is to send an Ack to many receivers simultaneously. The CH (master, coordinator) of the unit cluster broadcasts a Beacon frame where Ack information of the previously transmitted data is included. This technique can reduce the number of transmissions and overhead significantly. The proposed technique is a scheme improving the efficiency of an underwater sensor network where the uplink data transmission is the mainstream. The Performance of the ARQ, Block Ack, Pervasive Block Ack and the proposed method were compared with one another and analyzed. The proposed method showed significant performance improvement as compared with the ARQ, BA, and PBA in its channel efficiency.

• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.129-135
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• 2019

This paper is part of research and development for stand-alone solar system without commercial power supply. It implements firmware of controller for operation of stand-alone solar system by applying IoT technology and also develops communication modules that allow multiple solar lamps to send and receive data through wireless network. The controller of the developed stand-alone solar system can effectively charge the power generated by the solar module, taking into account the battery's charge and discharge characteristics. It also has the advantage of attaching wireless communication modules to solar lamp posts to establish wireless communication networks without incurring communication costs. In addition, by establishing IoT gateway middleware platform for each installation site, it forms a foundation to operate multiple solar lamp posts into multiple clusters. And, it is expected that the data collected in each cluster will be used to enable configuration and control of operational information, thereby inducing convenience and efficiency of remote operation and management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

• 여성건강간호학회지
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• 제21권2호
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• pp.128-138
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• 2015

Purpose: This cross-sectional study was designed to identify anger-expression types of adolescent women and investigate the relationship between the identified anger-expression types and their problem behaviors and suicide probability. Methods: The participants were 942 students at two female high school located in Gwangju. The data were analyzed using descriptive statistics, cluster analysis, t-test, ANOVA, and $Scheff{\acute{e}}$ multiple comparison test. Results: Cluster analysis revealed 4 distinct anger expression types; Anger-out, Low anger expression, Anger-control, and Anger-in/out types. Female adolescent women had a higher level of trait anger or who frequently used the anger-in/out type reported internalized-externalized problem behaviors and suicide probability more frequently compared to those who frequently used the other three types of anger expression. Conclusion: Female adolescent women who had the low anger expression type and anger control type managed anger most effectively. The findings suggest the necessity of a development of the program for lowering the trait anger level and controlling the unfavorable anger expression types such as the anger-in/out.

• 융합보안논문지
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• 제8권1호
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• pp.9-18
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• 2008

고속으로 이동 중인 차량들 간의 긴급한 정보를 주고받아 고속도로에서의 다중충돌사고를 미연에 방지할 수 있는 차량 통신 프로토콜을 제안한다. 본 논문에서는, 도로위의 차량들은 서로 연계성이 없기 때문에 위치추적시스템인 GPS에서 수신한 거리 값을 연계하여 클러스터를 구성한다. 클러스터를 IEEE 802.11 MAC 계층에서 구성하기 때문에 경로 설정으로 인한 과부하문제를 해소 하였다. 논문에서 제안한 액티브 네트워크 기반의 멀티 홉 라우팅 프로토콜은, Active Network를 이용하여 안정된 통신이 이루어 질수 있도록 한다.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• 제어로봇시스템학회논문지
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• 제15권8호
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• pp.859-866
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• 2009

The article discusses an issue of estimating the airspeed of an autonomous flying vehicle. Airspeed is the difference between ground speed and wind speed. It is desirable to know any two among the three speeds for navigation, guidance and control of an autonomous vehicle. For example, ground speed and position are used to guide a vehicle to a target point and wind speed and airspeed are used to maximize flight performance such as a gliding range. However, the target vehicle has not an airspeed sensor but a ground speed sensor (GPS/INS). So airspeed or wind speed has to be estimated. Here, airspeed is to be estimated. A vehicle's dynamics and its dynamic parameters are used to estimate airspeed with attitude and angular speed measurements. Kalman filter is used for the estimation. There are also two major sources arousing a robust estimation problem; wind speed and altitude. Wind speed and direction depend on weather conditions. Altitude changes as a vehicle glides down to the ground. For one reference altitude, multiple model Kalman filters are pre-designed based on several reference airspeeds. We call this group of filters as a cluster. Filters of a cluster are activated simultaneously and probabilities are calculated for each filter. The probability indicates how much a filter matches with measurements. The final airspeed estimate is calculated by summing all estimates multiplied by probabilities. As a vehicle glides down to the ground, other clusters that have been designed based on other reference altitudes are activated. Some numerical simulations verify that the proposed method is effective to estimate airspeed.

• Animal Bioscience
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• 제34권2호
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• pp.185-191
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• 2021

Objective: The objective of this study was to estimate the genetic parameters for worm resistance (WR) and associated characteristics, using the linear-threshold animal model via Bayesian inference in single- and multiple-trait analyses. Methods: Data were collected from a herd of Santa Inês breed sheep. All information was collected with animals submitted to natural contamination conditions. All data (number of eggs per gram of feces [FEC], Famacha score [FS], body condition score [BCS], and hematocrit [HCT]) were collected on the same day. The animals were weighed individually on the day after collection (after 12-h fasting). The WR trait was defined by the multivariate cluster analysis, using the FEC, HCT, BCS, and FS of material collected from naturally infected sheep of the Santa Inês breed. The variance components and genetic parameters for the WR, FEC, HCT, BCS, and FS traits were estimated using the Bayesian inference under the linear and threshold animal model. Results: A low magnitude was obtained for repeatability of worm-related traits. The mean values estimated for heritability were of low-to-high (0.05 to 0.88) magnitude. The FEC, HCT, BCS, FS, and body weight traits showed higher heritability (although low magnitude) in the multiple-trait model due to increased information about traits. All WR characters showed a significant genetic correlation, and heritability estimates ranged from low (0.44; single-trait model) to high (0.88; multiple-trait model). Conclusion: Therefore, we suggest that FS be included as a criterion of ovine genetic selection for endoparasite resistance using the trait defined by multivariate cluster analysis, as it will provide greater genetic gains when compared to any single trait. In addition, its measurement is easy and inexpensive, exhibiting greater heritability and repeatability and a high genetic correlation with the trait of resistance to worms.

• 한국정보처리학회논문지
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• 제7권6호
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• pp.1834-1843
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• 2000

In this paper, we propose a modular neural network based classification method for handwritten numerals utilizing rotated and translated images of an input image. The whole numeral pattern space is divided into smaller spaces which overlap each other and form multiple clusters. On these multiple clusters, multiple multilayer perceptrons (MLP) neural networks, specialized in those clusters, are constructed. Thus, each MLP acts as an expert network on the corresponding cluster. An MLP is also used as a gating network functioning as a mediator among the multiple MLPs. In the learning phase, an input numeral image is dithered by tow geometric operations of translation and rotation so that new numeral images similar to original one are generated. In the recognition phase, we utilize not only input numeral image, but also nearly generated images through the rotation and the translation of the original image. Thus, multiple output values for those generated images were combined to make class decision by various combination methods. The experimental results confirm the validity of the proposed method.