• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.3
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• pp.191-201
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• 2015

Measuring and mapping connectivity among habitats is a key component of sustainable urban planning and design process. In this study, we examined how functional corridors connect forest bird habitats in a metropolitan area of Korea using graph theory-based techniques. High-quality forest habitat was defined as a function of forest cover, presence of residential areas, and road networks. We then constructed a network of high-quality forest habitats using the FunConn (functional connectivity) tools, and computed metrics ($T_i$) of patch importance based on the minimum ($Q_1$) and the 25th percentile ($Q_{25}$) rank least-cost distance values. We investigated the relative influence of two values of patch importance on forest bird species richness. As a result, the patch importance index based on the $Q_{25}$ effective distance threshold was most positively correlated with species richness (P < 0.001) after controlling for the area effect. Thus, using the $Q_{25}$ effective distance threshold, we mapped not only the locations of important habitat patches and functional corridors, but also the network backbone of forest bird habitats. The network developed in this study can help guide urban planning for biodiversity conservation.

• Smart Structures and Systems
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• v.22 no.2
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• pp.167-174
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• 2018

Digital input and output modules are widely used to connect digital sensors and actuators to automation systems. Digital I/O modules provide flexible connectivity extension to numerous sensors and actuators and protect systems from high voltages and currents by isolation. Components in digital I/O modules are inevitably affected by operating and environmental conditions, such as high voltage, high current, high temperature, and temperature cycling. Because digital I/O modules transfer signals or isolate the systems from unexpected voltage and current transients, their failures may result in signal transmission failures and damages to sensitive circuitry leading to system malfunction and system shutdown. In this study, the lifetime of optocouplers, one of the critical components in digital I/O modules, was predicted using Bayesian tracking approaches. Accelerated degradation tests were conducted for collecting the critical performance parameter of optocouplers, current transfer ratio (CTR), during their lifetime. Bayesian tracking approaches, including extended Kalman filter and particle filter, were applied to predict the failure. The performance of each prognostic algorithm was then compared using accuracy and robustness-based performance metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.888-908
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• 2011

The increasing demand from passengers in vehicles to improve safety, traffic efficiency, and comfort has lead to the growing interest of Wi-Fi based vehicle-to-infrastructure (V2I) communications. Although the V2I system provides fast and cost-effective Internet connectivity to vehicles via roadside Wi-Fi access points (APs), it suffers from frequent handoffs due to the high mobility of vehicles and the limited coverage of Wi-Fi APs. Recently, the Mobile AP (MAP) platform has emerged as a promising solution that overcomes the problem in the V2I systems. The main advantage is that MAPs may yield longer service duration to the nearby vehicles that have similar mobility patterns, yet they provide smaller link capacities than the roadside APs. In this paper, we present a new association control technique that harnesses available connection duration as well as achievable link bandwidth in high-speed vehicular network environments. We also analyze the tradeoff between two association metrics, namely, available connection duration and achievable link bandwidth. Extensive simulation studies based on real traces demonstrate that our scheme significantly outperforms the previous methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.87-97
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• 2009

A Mobile Ad hoc Network (MANET) is characterized by multi-hop wireless connectivity, frequently changing network topology with mobile nodes and the efficiency of the dynamic routing protocol plays an important role in the performance of the network. In this paper, the performance of five routing protocols for MANET is compared by using OPNET modeler: AODV, DSR, GRP, OLSR and TORA. The various performance metrics are examined, such as packet delivery ratio, end-to-end delay and routing overhead with varying data traffic, number of nodes and mobility. In our simulation results, OLSR shows the best performance in terms of data delivery ratio in static networks, while AODV has the best performance in mobile networks with moderate data traffic. When comparing proactive protocols (OLSR, GRP) and reactive protocols (AODV, DSR) with varying data traffic in the static networks, proactive protocols consistently presents almost constant overhead while the reactive protocols show a sharp increase to some extent. When comparing each of proactive protocols in static and mobile networks, OLSR is better than GRP in the delivery ratio while overhead is more. As for reactive protocols, DSR outperforms AODV under the moderate data traffic in static networks because it exploits caching aggressively and maintains multiple routes per destination. However, this advantage turns into disadvantage in high mobility networks since the chance of the cached routes becoming stale increases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1213-1237
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• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.223-232
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• 2017

Purpose: To report the use of multiband accelerated echo-planar imaging (EPI) for resting-state functional MRI (rs-fMRI) to achieve rapid high temporal resolution at 3T compared to conventional EPI. Materials and Methods: rs-fMRI data were acquired from 20 healthy right-handed volunteers by using three methods: conventional single-band gradient-echo EPI acquisition (Data 1), multiband gradient-echo EPI acquisition with 240 volumes (Data 2) and 480 volumes (Data 3). Temporal signal-to-noise ratio (tSNR) maps were obtained by dividing the mean of the time course of each voxel by its temporal standard deviation. The resting-state sensorimotor network (SMN) and default mode network (DMN) were estimated using independent component analysis (ICA) and a seed-based method. One-way analysis of variance (ANOVA) was performed between the tSNR map, SMN, and DMN from the three data sets for between-group analysis. P < 0.05 with a family-wise error (FWE) correction for multiple comparisons was considered statistically significant. Results: One-way ANOVA and post-hoc two-sample t-tests showed that the tSNR was higher in Data 1 than Data 2 and 3 in white matter structures such as the striatum and medial and superior longitudinal fasciculus. One-way ANOVA revealed no differences in SMN or DMN across the three data sets. Conclusion: Within the adapted metrics estimated under specific imaging conditions employed in this study, multiband accelerated EPI, which substantially reduced scan times, provides the same quality image of functional connectivity as rs-fMRI by using conventional EPI at 3T. Under employed imaging conditions, this technique shows strong potential for clinical acceptance and translation of rs-fMRI protocols with potential advantages in spatial and/or temporal resolution. However, further study is warranted to evaluate whether the current findings can be generalized in diverse settings.

• Journal of Environmental Policy
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• v.10 no.1
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• pp.93-127
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• 2011

Investment on technology to obtain green energy is prevailing all over the world. The technology development project is more likely to involve multiple sub-projects, each of which is related to develop elementary technology when the project is larger and nation-wide. However, the methodologies identifying optimal combination of elementary technologies among the candidates have been very few. Hence, the purpose of this paper is to propose a novel methodology which provides an optimal combination of green energy technologies. To do so, to-be developed technologies are clustered with multiple categories. Among the technologies, based on Delphi method, the experts select a representative technology, which is indispensible to the green energy system and has the highest connectivity with other elementary technologies. Then the methodology selects an elementary technology from each technology category based on two metrics: Relatedness with representative technology and project risk. To show the feasibility of the proposed methodology, we applied the methodology to an actual windmill development project.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.10
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• pp.785-800
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• 2013

In MANET, AODV(Ad hoc On-demand Distance Vector) has its advantages as on-demand approach but it also has a disadvantage that the control packet overhead is high compared to other routing protocols. This paper improves the problem caused by Hello messages that are broadcasted periodically to detect the local connectivity and maintain neighbor list. Periodic hello messages reduce the Packet delivery ratio and the efficiency in the limited bandwidth. And its increased Control packet overhead leads to decrease the Residual battery capacity and the Network lifetime. Further, non-reactive nature of periodic hello messages in AODV has also been the source of numerous controversies. In order to solve these problems, this paper improves the performance by using the interrupt driven approach which removes periodic hello messages and decreases the Control packet overhead. Performance comparisons between the traditional AODV and proposed mod_AODV done with network simulator QualNet 5.0 show that the mod_AODV performs better in most performance metrics under scenarios with various values of simulation parameters.