• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.2
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• pp.173-180
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• 2003

Effects of the electric conductivity of particles were studied for the aggregation process of charged particles with a Brownian dynamic simulation in the free molecular regime. A periodic boundary condition was used for the calculation of the aggregation process in each cell with 500 primary particles of 16 nm in diameter. We considered two extreme cases, a perfect conductor and a perfect nonconductor. The electrostatic force on a particle in the simulation cell was considered as a sum of electrostatic forces from other particles in the original cell and its replicate cells. We assumed that aggregates were only charged with pre-charged primary particles. The morphological shape of aggregates was described in terms of the fractal dimension. The fractal dimension for the uncharged aggregate was D$_{f}$= 1.761. However, the fractal dimension decreased from 1.694 to 1.360 for the case of the perfect conductor, and from 1.610 to 1.476 for the case of the perfect nonconductor, with the increase of the average number of charges on the primary particle from 0.2 to 0.3. These values were smaller than that of the centered charge case.e.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12A
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• pp.945-950
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• 2003

The link aggregation between 10GbE switches requires an advanced frame distribution method to be properly and efficiently applied. The fixed or dynamic frame distribution methods, formerly proposed, cannot fully utilize the aggregated links, where the receiving terminal only attaches to a pre-specified link among multiple physical links. A frame distribution method using tagging is proposed for the link aggregation between 10GbE switches to solve this problem. We compared the performance of the proposed method with those of the fixed and dynamic frame distribution methods. As a result, the proposed method shows a better performance when the applied load is below 0.7 and the average length of the frames is longer than 954 bytes.

• Bulletin of the Korean Chemical Society
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• v.21 no.6
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• pp.623-627
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• 2000

Aggregation of hydrophobically end-capped poly(ethylene oxide)s: HEURs, denoted as $C_8$$EO_{380}$$C_8$, $C_12$$CO_{600}$$C_{12}$, and $C_{18}$$EO_{860}$$C_{18}$,are described using static fluorescence, dynamic light scattering, and atomic force microscope (AFM) techniques. The CAC (critical aggregation concentration) was determined by com-paring two fluorescent peaks which were influenced by the polarity of the probe dye molecules, pyrene. The aggregation occurs in concentrations higher than 10 g/L of $C_8$$EO_{380}$$C_8$ and the CAC decreases by increasing the side chain length. The dynamic light scattering experiment shows fast mode and slow mode decays, and both are diffusive. The fast mode does not depend on the concentration, but the slow mode shows concentration dependence influenced by the formation of an aggregated structure. The hydrophobic end groups effect more dominantly than the main chains for the formation of HEUR micelles. By increasing the concentration, the HEUR micelles change their structure from spheres to rodlike micelles, and finally make fused structures, which were visualized with atomic force microscopy.

• Environmental Analysis Health and Toxicology
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• v.28
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• pp.6.1-6.7
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• 2013

Objectives In this study, we investigated the influence of ionic strength and natural organic matter (NOM) on aggregation and soil adsorption of citrate-coated silver nanoparticles (AgNPs). Methods Time-resolved dynamic light scattering measurements and batch adsorption experiments were used to study their aggregation and soil adsorption behaviors, respectively. Results The aggregation rate of AgNPs increased with increasing ionic strength and decreasing NOM concentration. At higher ionic strength, the AgNPs were unstable, and thus tended to be adsorbed to the soil, while increased NOM concentration hindered soil adsorption. To understand the varying behaviors of AgNPs depending on the environmental factors, particle zeta potentials were also measured as a function of ionic strength and NOM concentration. The magnitude of particle zeta potential became more negative with decreasing ionic strength and increasing NOM concentration. These results imply that the aggregation and soil adsorption behavior of AgNPs were mainly controlled by electrical double-layer repulsion consistent with the Derjaguin-Landau-Verwey-Overbeek theory. Conclusions This study found that the aggregation and soil adsorption behavior of AgNPs are closely associated with environmental factors such as ionic strength and NOM and suggested that assessing the environmental fate and transport of nanoparticles requires a thorough understanding of particle-particle interaction mechanisms.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.5
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• pp.571-579
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• 2013

The aggregation behaviors of silver nanoparticles (AgNPs) and titanium dioxide ($TiO_2$) nanoparticles were investigated. Time-resolved dynamic light scattering (DLS) was used to study the initial aggregation of AgNPs and $TiO_2$ over a range of mono (NaCl) and divalent ($CaCl_2$) electrolyte concentrations. The effects of pH, initial concentration of NPs and natural organic matters (NOM) on the aggregation of NPs were also investigated. The aggregation of both nanoparticles showed classical Derjaguin-Landau-Verwey-Overbeek (DLVO) type behavior. Divalent electrolyte was more efficient in destabilize the AgNPs and $TiO_2$ than monovalent electrolyte. The effect of pH on the aggregation of AgNPs was not significant. But the aggregation rate of $TiO_2$ was much higher with increasing pH. Higher NPs concentration leads to faster aggregation. Natural organic matter (NOM) was found to substantially hinder the aggregation of both AgNPs and $TiO_2$. This study found that the aggregation behavior of AgNPs and $TiO_2$ are closely associated with environmental factors such as ionic strength, pH, initial concentration of NPs and NOM.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.933-937
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• 2009

This paper proposes a dynamic timeout scheduling for energy efficient and accurate aggregation by analyzing the single hop delay in wireless sensor networks based on IEEE 802.15.4. The proposed scheme dynamically configures the timeout value depending on both the number of nodes sharing a channel and the type of wireless media, with considering the results of delay analysis of the single hop delay. The timeout of proposed scheme is much smaller than the maximum single hop delay which is used as the timeout of traditional data aggregation schemes. Therefore the proposed scheme considerably reduces the energy consumption of idle monitoring for waiting messages. Also, the proposed scheme maintains the data accuracy by guaranteeing the reception ratio required by the sensor network applications. Extensive simulation has revealed that proposed scheme enhances energy consumption by 30% with maintaining data accuracy, as compared with the TAG data aggregation.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.2
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• pp.227-237
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• 2002

A numerical technique for simulating the aggregation of charged particles was presented with a Brownian dynamic simulation in the free molecular regime. The Langevin equation was used for tracking each particle making up an aggregate. A periodic boundary condition was used for calculation of the aggregation process in each cell with 500 primary particles of 16 nm in diameter. We considered the thermal force and the electrostatic force for the calculation of the particle motion. The electrostatic force on a particle in the simulation cell was considered as a sum of electrostatic forces from other particles in the original cell and its replicate cells. We assumed that the electric charges accumulated on an aggregate were located on its center of mass, and aggregates were only charged with pre-charged primary particles. The morphological shape of aggregates was described in terms of the fractal dimension. In the simulation, the fractal dimension for the uncharged aggregate was D$\_$f/ = 1.761. The fractal dimension changed slightly for the various amounts of bipolar charge. However, in case of unipolar charge, the fractal dimension decreased from 1.641 to 1.537 with the increase of the average number of charges on the particles from 0.2 to 0.3 in initial states. In the bipolar charge state, the average sizes of aggregates were larger than that of the uncharged state in the early and middle stages of aggregation process, but were almost the same as the case of the uncharged state in the final stage. On the other hand, in the unipolar charge state, the average size of aggregates and the dispersion of particle volume decreased with the increasing of the charge quantities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.1-8
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• 2017

Diverse types of malicious code such as evasive Server-side Polymorphic are developed and distributed in third party open markets. The suspicious new type of polymorphic malware has the ability to actively change and morph its internal data dynamically. As a result, it is very hard to detect this type of suspicious transaction as an evidence of Server-side polymorphic mobile malware because its C&C server was shut downed or an IP address of remote controlling C&C server was changed irregularly. Therefore, we implemented Simulated C&C Server to aggregate activated events perfectly from various Server-side polymorphic mobile malware. Using proposed Simulated C&C Server, we can proof completely and classify veiled server-side polymorphic malicious code more clearly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.653-669
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• 2024

Dynamic scene deblurring is a complex computer vision problem owing to its difficulty to model mathematically. In this paper, we present a novel approach for image deblurring with the help of the sharp reference image, which utilizes the reference image for high-quality and high-frequency detail results. To better utilize the clear reference image, we develop an encoder-decoder network and two novel modules are designed to guide the network for better image restoration. The proposed Reference Extraction and Aggregation Module can effectively establish the correspondence between blurry image and reference image and explore the most relevant features for better blur removal and the proposed Spatial Feature Fusion Module enables the encoder to perceive blur information at different spatial scales. In the final, the multi-scale feature maps from the encoder and cascaded Reference Extraction and Aggregation Modules are integrated into the decoder for a global fusion and representation. Extensive quantitative and qualitative experimental results from the different benchmarks show the effectiveness of our proposed method.