• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.41-47
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• 2009

Routing through a backbone, which is responsible for performing and managing multipoint communication, reduces the communication overhead and overall energy consumption in wireless sensor networks. However, the backbone nodes will need extra functionality and therefore consume more energy compared to the other nodes. The power consumption imbalance among sensor nodes may cause a network partition and failures where the transmission from some sensors to the sink node could be blocked. Hence optimal construction of the backbone is one of the pivotal problems in sensor network applications and can drastically affect the network's communication energy dissipation. In this paper a distributed algorithm is proposed to generate backbone trees through robust multi-hop clusters in wireless sensor networks. The main objective is to form a properly designed backbone through multi-hop clusters by considering energy level and degree of each node. Our improved cluster head selection method ensures that energy is consumed evenly among the nodes in the network, thereby increasing the network lifetime. Comprehensive computer simulations have indicated that the newly proposed scheme gives approximately 10.36% and 24.05% improvements in the performances related to the residual energy level and the degree of the cluster heads respectively and also prolongs the network lifetime.

• Journal of Distribution Science
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• v.17 no.3
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1016-1023
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• 2009

A license plate recognition system consists of image processing in which characters and features are extracted, and pattern recognition in which extracted characters are classified. Feature extraction plays an important role in not only the level of data reduction but also performance of recognition. Thus, in this paper, we focused on the recognition of numeral characters especially on the feature extraction of numeral characters which has much effect in the result of plate recognition. We suggest a hybrid statistical feature model which assures the best dispersion of input data by reassignment of clustering property of input data. And we verify the effectiveness of suggested model using multi-layer perceptron and learning vector quantization neural networks. The results show that the proposed feature extraction method preserves the information of a license plate well and also is robust and effective for even noisy and external environment.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.25-33
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• 2016

In order to visualize point based Location-Based Social Network Services(LBSNS) data on multi-scaled tile map effectively, it is necessary to apply tile-based clustering method. Then determinating reasonable numbers and size of tiles is required. However, there is no such criteria and the numbers and size of tiles are modified based on data type and the purpose of analysis. In other words, researchers' subjectivity is always involved in this type of study. This is when Modifiable Areal Unit Problem(MAUP) occurs, that affects the results of analysis. Among LBSNS, geotagged Twitter data were chosen to find the influence of MAUP in scale effects perspective. For this purpose, the degree of spatial autocorrelation using spatial error model was altered, and change of distributions was analyzed using Morna's I. As a result, positive spatial autocorrelation showed in the original data and the spatial autocorrelation was decreased as the value of spatial autoregressive coefficient was increasing. Therefore, the intensity of the spatial autocorrelation of Twitter data was adjusted to five levels, and for each level, nine different size of grid was created. For each level and different grid sizes, Moran's I was calculated. It was found that the spatial autocorrelation was increased when the aggregation level was being increased and decreased in a certainpoint. Another tendency was found that the scale effect of MAUP was decreased when the spatial autocorrelation was high.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.303-312
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• 2018

The university facility is made up of multiple buildings and has many maintenance items. In addition, administrative constraints need to be handled within a limited period. Most maintenance work is small scale and multi-work construction, such as waterproofing, needs to be organized. The facility manager makes annual unit price contract with a maintenance company and carries out the maintenance work. On the other hand, delay and rework is occurring because existing maintenance work performed without scheduling based on the manpower input. This study proposed a scheduling model that can support the facility manager to manage maintenance works of multiple university facilities at the program level. The model consists of three stages in order. In object analysis, details of the maintenance items were analyzed and the quantity is calculated based on the quantity takeoff sheet. In resource analysis, the craftsmen and construction period of detailed works are derived for the effective input of craftsmen. In scheduling, the priority of each work and the optimal manpower input are derived. The optimal schedule is selected according to the goodness of fit. The applicability and effectiveness of the prototype was evaluated through a case study and interviews with case participants. The model was found to be an effective tool to support the scheduling of maintenance works for the facility manager.

• 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.