• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.252-263
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• 2008

This study was conducted to derive the optimal regionalization of the precipitation data which can be classified on the basis of climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. A total of 65 rain gauges were used to regional analysis of precipitation. Annual maximum series for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr were used for various statistical analyses. K-means clustering mettled is used to identify homogeneous regions all over the regions. Five homogeneous regions for the precipitation were classified by the K-means clustering. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the generalized extreme value (GEV) distribution among applied distributions. The regional and at-site parameters of the generalized extreme value distribution were estimated by the linear combination of the probability weighted moments, L-moment. The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE were computed and compared with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.213-220
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• 2015

Wireless sensor network(WSN) is made up of a lot of battery operated inexpensive sensors that, once deployed, can not be replaced. Therefore, energy efficiency of WSN is essential. Among the methods for energy efficiency of the network, clustering algorithms, which divide a WSN into multiple smaller clusters and separate all sensors into cluster heads and their associated member nodes, are very energy efficient routing technique. The first cluster-based routing protocol, LEACH, randomly elects the cluster heads in accordance with the probability. However, if the distribution of selected cluster heads is not good, uniform energy consumption of cluster heads is not guaranteed and it is possible to decrease the number of active nodes. Here we propose a new routing scheme that, by comparing the remaining energy of all nodes in a cluster, selects the maximum remaining energy node as a cluster head. Because of decrease in energy gap of nodes, the node that was a cluster head operates as a member node much over. As a result, the network lifespan is increased and more data arrives at base station.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.375-388
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• 2021

This paper aims to help prevent the spread of COVID-19 by analyzing confirmed cases of COVID-19 in Daejeon. A high volume of visitors, downtown areas, and psychological fatigue with prolonged social distancing were considered as risk factors associated with the spread of COVID-19. We considered the weekly confirmed cases in each administrative district as a response variable. Explanatory variables were the number of passengers getting off at a bus station in each administrative district and the elapsed time since the Korean government had imposed distancing in daily life. We employed a mixed-effects zero-inflated Poisson regression model because the number of cases was repeatedly measured with excess zero-count data. We conducted k-means clustering to identify three groups of administrative districts having different characteristics in terms of the number of bars, the population size, and the distance to the closest college. Considering that the number of confirmed cases might vary depending on districts' characteristics, the clustering information was incorporated as a categorical explanatory variable. We found that Covid-19 was more prevalent as population size increased and a district is downtown. As the number of passengers getting off at a downtown district increased, the confirmed cases significantly increased.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.576-579
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• 2011

Wireless sensor network are widely all over different fields. Because of its distinguished characteristics, we must take account of the factor of energy consumed when designing routing protocol. Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, energy efficiency is a key design issue that needs to be enhanced in order to improve the life span of the network. In BCDCP, all sensors sends data from the CH (Cluster Head) and then to the BS (Base Station). BCDCP works well in a smallscale network however is not preferred in a large scale network since it uses much energy for long distance wireless communication. TBRP can be used for large scale network, but it weakness lies on the fact that the nodedry out of energy easily since it uses multi-hops transmission data to the Base Station. Here, we proposed a routing protocol. A Cluster Based Energy Efficient Tree Routing Protocol (CETRP) in Wireless Sensor Networks (WSNs) to prolong network life time through the balanced energy consumption. CETRP selects Cluster Head of cluster tree shape and uses maximum two hops data transmission to the Cluster Head in every level. We show CETRP outperforms BCDCP and TBRP with several experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1273-1287
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• 2009

PEGASIS, a chain-based protocol, forms chains from sensor nodes so that each node transmits and receives from a neighbor. In this way, only one node (known as a HEAD) is selected from that chain to transmit to the sink. Although PEGASIS is able to balance the workload among all of the nodes by selecting the HEAD node in turn, a considerable amount of energy may be wasted when nodes which are far away from sink node act as the HEAD. In this study, DERP (Distance-based Energy-efficient Routing Protocol) is proposed to address this problem. DERP is a chain-based protocol that improves the greedy-algorithm in PEGASIS by taking into account the distance from the HEAD to the sink node. The main idea of DERP is to adopt a pre-HEAD (P-HD) to distribute the energy load evenly among sensor nodes. In addition, to scale DERP to a large network, it can be extended to a multi-hop clustering protocol by selecting a "relay node" according to the distance between the P-HD and SINK. Analysis and simulation studies of DERP show that it consumes up to 80% less energy, and has less of a transmission delay compared to PEGASIS.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.185-190
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• 2007

The efficient node energy utilization is one of important performance factors in wireless sensor networks because sensor nodes operate with limited battery power. To extend the lifetime of the wireless sensor networks, maintaining balanced power consumption between sensor nodes is more important than reducing each energy consumption of the sensor node in the network. In this paper, we proposed a cluster formation algorithm to extend the lifetime of the networks and to maintain a balanced energy consumption of nodes. To obtain it, we add a tiny slot in a round frame, which enables to exchange the residual energy messages between the base station (BS). cluster heads, and nodes. The performance of the proposed protocol has been examined and evaluated with the NS 2 simulator. As a result of simulation, we have confirmed that our proposed algorithm show the better performance in terms of lifetime than LEACH. Consequently, our proposed protocol can effectively extend the network lifetime without other critical overhead and performance degradation.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.3
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• pp.37-50
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• 2013

The purpose of this study is to determine the indicators and the criteria to classify types of train delays of high-speed rail in South Korea. Types of train delays have divided into the chronic delays and the knock-on delays. The Indicators based on relevance, reliability, and comparability were selected with arrival delay rate of over five minutes, median of arrival delays of preceding train and following train, knock-on delay rate of over five minutes, correlation of delay between preceding train and following train on intermediate and last stations, average train headway, average number of passengers per train, and average seat usages. Types of train delays were separated using the Ward's hierarchical cluster analysis. The criteria for classification of train delay were presented by the Fisher's linear discriminant. The analysis on the situational characteristics of train delays is as follows. If the train headway in last station is short, the probability of chronic delay is high. If the planned running times of train is short, the seriousness of chronic delay is high. The important causes of train delays are short headway of train, shortly planned running times, delays of preceding train, and the excessive number of passengers per train.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.715-723
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• 2015

In this paper, we propose a context-awareness routing algorithm DDV (Dynamic Direction Vector)-hop algorithm in Mobile Ad Hoc Networks. The existing algorithm in MANET, it has a vulnerability that the dynamic network topology and the absence of network expandability of mobility of nodes. The proposed algorithm performs cluster formation using a range of direction and threshold of velocity for the base-station, we calculate the exchange of the cluster head node probability using the direction and velocity for maintaining cluster formation. The DDV algorithm forms a cluster based on the cluster head node. As a result of simulation, our scheme could maintain the proper number of cluster and cluster members regardless of topology changes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5B
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• pp.801-808
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• 2010

LEACH, one of hierarchical based routing protocols, was proposed for energy efficiency which is the most important requirement of Wireless Sensor Network(WSN). LEACH protocol is composed of a cluster of certain large number of clusters, which have a cluster head and member nodes. Member nodes send sensing data to their cluster heads, and the cluster heads aggregate the sensing data and transmit it to BS. The challenges of LEACH protocol are that cluster heads are not evenly distributed, and energy consumption to transmit aggregated data from Cluster heads directly to BS is excessive. This study, to improve LEACH protocol, suggests LECEEP that transmit data to contiguity cluster head that is the nearest and not far away BS forming chain between cluster head, and then the nearest cluster head from BS transmit aggregated data finally to BS. According to simulation, LECEEP consumes less energy and retains more number of survival node than LEACH protocol.