• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.61-80
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• 2018

Location information of individual nodes is important in the implementation of necessary network functions. While extensive studies focus on localization techniques in 2D space, few approaches have been proposed for 3D positioning, which brings the location closer to the reality with more complex calculation consumptions for high accuracy. In this paper, an effective range-free localization scheme is proposed for 3D space localization, and the sensitivity of parameters is evaluated. Firstly, we present an improved algorithm (MDV-Hop), that the average distance per hop of the anchor nodes is calculated by root-mean-square error (RMSE), and is dynamically corrected in groups with the weighted RMSE based on group hops. For more improvement in accuracy, we expand particle swarm optimization (PSO) of intelligent optimization algorithms to MDV-Hop localization algorithm, called PMDV-hop, in which the parameters (inertia weight and trust coefficient) in PSO are calculated dynamically. Secondly, the effect of various localization parameters affecting the PMDV-hop performance is also present. The simulation results show that PMDV-hop performs better in positioning accuracy with limited energy.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.7
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• pp.328-334
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• 2002

The DNA chips are devices associating the specific recognition properties of two DNA single strands through hybridization process with the performances of the microtechnology. In the literature, the "Gene chip" or "DNA chip" terminology is employed in a wide way and includes macroarrays and microarrays. Standard definitions are not yet clearly exposed. Generally, the difference between macro and microarray concerns the number of active areas and their size, Macroarrays correspond to devices containing some tens spots of 500$\mu$m or larger in diameter. microarrays concern devices containing thousnads spots of size less than 500$\mu$m. The key technical parameters for evaluating microarray-manufacturing technologies include microarray density and design, biochemical composition and versatility, repreducibility, throughput, quality, cost and ease of prototyping. Here we report, a new method in which minute particles are arranged in a random fashion on a chip pattern using random fluidic self-assembly (RFSA) method by hydrophobic interaction. We intend to improve the stability of the particles at the time of arrangement by establishing a wall on the chip pattern, besides distinction of an individual particle is enabled by giving a tag structure. This study demonstrates the fabrication of a chip pattern, immobilization of DNA to the particles and arrangement of the minute particle groups on the chip pattern by hydrophobic interaction.ophobic interaction.

• Particle and aerosol research
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• v.10 no.4
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• pp.131-136
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• 2014

The importance of waste treatments is increasing because of the lack of resources and environmental problems resulted from economic growth policy. Especially, the pollutant dust which is one of the wastes should be treated considerately because it could cause secondary damages on the human health as well as environmental systems. Recently, massive amount of coal fly-ash is being produced in thermoelectric power plants. In this study, we compared two general methods used in estimating the amount of unburned carbon in fly-ashes to categorize the coal fly-ashes into several groups following their carbon contents. One is the "loss on ignition(KS L 5405) method" which estimates the change of mass after combustion, and it is generally used. Another one is measuring $CO_2$ gas content by burning solid carbon in the fly-ash, and it is called "$CO_2$ analysis method."

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.881-885
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• 2017

This work presents a new technique for discriminating between alpha particles of different energy levels. In a first study, two groups of alpha particles emitted from radium-226 and americium-241 sources were successfully separated using a CR-39 microfilm of appropriate thickness. This thickness was adjusted by chemical etching before and after irradiation so that lower-energy particles were stopped within the detector, while higher-energy particles were revealed on the back side of the detector. The number of tracks on the front side of the microfilm represented all alpha particles incident on that side from the two sources. However, the number of tracks on the back side of the microfilm represented only the long-range alpha particles of higher energy that arrived at that side. Therefore, by subtracting the number of tracks on the back side from the number of tracks on the front side, one could easily determine the number of tracks for the short-range alpha particles of lower energy that remained embedded in the microfilm. Discrimination of the two energy levels is thus achieved in a simple, fast, and reliable process.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.76-83
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• 2018

The electron energy probability function (EEPF) is of significant importance since the plasma chemistry such as the rate of ionization is determined by the electron energy distribution function. It is usually assumed to be Maxwell distribution for 0-D global model. Meanwhile, it has been observed experimentally that the form of EEPF of Ar plasma changes from being two-temperature to Druyvesteyn like as the gas pressure increases. Thus, to apply the 0-D global model of Maxwellian distribution to the non-Maxwellian plasma, we investigated the relative contribution of two distinct electrons with different temperatures. The contributions of cold/hot electrons to the equilibrium state of the plasma have attracted interest and been researched. The contributions to the power and particle balance of cold/hot electrons were studied by comparing the result of the global model considering all combinations of electron temperatures with that of 1-D Particle-in-Cell and Monte Carlo collision (PIC-MCC) simulation and the results of studies were analyzed physically. Furthermore, comparisons term by term for variations of the contribution of cold/hot electrons at different driving currents are presented.

• Korean Journal of Food Science and Technology
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• v.37 no.3
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• pp.418-424
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• 2005

The physicochemical properties of raw noodle flours (n = 11) commercially produced from Australian Standard White (ASW) (Group 1, n = 8) and blonds (Group 2, n = 3) of ASW and Australian hard, western white or hard red winter were investigated. Protein and ash contents were lower in Group 1. The tristimulus color values, mean particle size, flour swelling volume (FSV) and rheological parameters of farinograph and extensigraph were not different between two flour groups. Peak viscosity measured with Rapid Visco Analyzer was higher in Group 1. The protein content was positively correlated with mean particle size, dough stability and dough extensibility, and negatively correlated with FSV and peak viscosity. The FSV wag positively correlated with the peak viscosity. The rheological parameters of dough did not show any correlations with FSV and peak viscosity.

• Journal of Korean Society on Water Environment
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• v.24 no.3
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• pp.365-377
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• 2008

Large artificial dam reservoirs and associated downstream ecosystems are under increased pressure from long-term negative impacts of turbid flood runoff. Despite various emerging issues of reservoir turbidity flow, turbidity modeling studies have been rare due to lack of experimental data that can support scientific interpretation. Modeling suspended sediment (SS) dynamics, and therefore turbidity ($C_T$), requires provision of constitutive relationships ($SS-C_T$) and accounting for deposition of different SS size fractions/types distribution in order to display this complicated dynamic behavior. This study explored the performance of a coupled two-dimensional (2D) hydrodynamic and particle dynamics model that simulates the fate and transport of a turbid density flow in a negatively buoyant density flow regime. Multiple groups of suspended sediment (SS), classified by the particle size and their site-specific $SS-C_T$ relationships, were used for the conversion between field measurements ($C_T$) and model state variables (SS). The 2D model showed, in overall, good performance in reproducing the reservoir thermal structure, flood propagation dynamics and the magnitude and distribution of turbidity in the stratified reservoir. Some significant errors were noticed in the transitional zone due to the inherent lateral averaging assumption of the 2D hydrodynamic model, and in the lacustrine zone possibly due to long-term decay of particulate organic matters induced during flood runoffs.

• The Journal of Advanced Prosthodontics
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• v.5 no.4
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• pp.382-387
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• 2013

PURPOSE. Although several surface treatments have been recently investigated both under in vitro and in vivo conditions, controversy still exists regarding the selection of the most appropriate zirconia surface pre-treatment. The purpose of this study was to evaluate the effect of alumina (Al) and aluminium nitride (AlN) coating on the shear bond strength of adhesive resin cement to zirconia core. MATERIALS AND METHODS. Fifty zirconia core discs were divided into 5 groups; air particle abrasion with 50 ${\mu}m$ aluminum oxide particles ($Al_2O_3$), polishing + Al coating, polishing + AlN coating, air particle abrasion with 50 ${\mu}m$ $Al_2O_3$ + Al coating and air particle abrasion with 50 ${\mu}m$ $Al_2O_3$ + AlN coating. Composite resin discs were cemented to each of specimens. Shear bond strength (MPa) was measured using a universal testing machine. The effects of the surface preparations on each specimen were examined with scanning electron microscope (SEM). Data were statistically analyzed by one-way ANOVA (${\alpha}$=.05). RESULTS. The highest bond strengths were obtained by air abrasion with 50 ${\mu}m$ $Al_2O_3$, the lowest bond strengths were obtained in polishing + Al coating group (P<.05). CONCLUSION. Al and AlN coatings using the reactive magnetron sputtering technique were found to be ineffective to increase the bond strength of adhesive resin cement to zirconia core.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Journal of the Korean Institute of Gas
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• v.16 no.1
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• pp.26-32
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• 2012

The autoignition temperature is one of the most important physical properties used to determine the flammability characteristics of chemical substances. Despite the needs of the experimental autoignition temperature data for the design of chemical plants, it is not easy to get the data. This study have built and compared partial least squares (PLS) and support vector machine (SVM) models to predict the autoignition temperatures of 503 organic compounds out of DIPPR 801. As the independent variables of the models, 59 functional groups were chosen based on the group contribution method. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, particle swarm optimization was used to get three parameters of SVM model. The PLS and SVM results of the average absolute errors for the whole data range from 58.59K and 29.11K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.