• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.572-578
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• 2020

The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.

• Journal of Korean Neurosurgical Society
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• v.63 no.5
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• pp.579-589
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• 2020

Objective : No optimum genetic rat Huntington model both neuropathological using an adeno-associated virus (AAV-2) vector vector has been reported to date. We investigated whether direct infection of an AAV2 encoding a fragment of mutant huntingtin (AV2-82Q) into the rat striatum was useful for optimizing the Huntington rat model. Methods : We prepared ten unilateral models by injecting AAV2-82Q into the right striatum, as well as ten bilateral models. In each group, five rats were assigned to either the 2×10¹² genome copies (GC)/mL of AAV2-82Q (×1, low dose) or 2×10¹³ GC/mL of AAV2-82Q (×10, high dose) injection model. Ten unilateral and ten bilateral models injected with AAV-empty were also prepared as control groups. We performed cylinder and stepping tests 2, 4, 6, and 8 weeks after injection, tested EM48 positive mutant huntingtin aggregates. Results : The high dose of unilateral and bilateral AAV2-82Q model showed a greater decrease in performance on the stepping and cylinder tests. We also observed more prominent EM48-positive mutant huntingtin aggregates in the medium spiny neurons of the high dose of AAV2-82Q injected group. Conclusion : Based on the results from the present study, high dose of AAV2-82Q is the optimum titer for establishing a Huntington rat model. Delivery of high dose of human AAV2-82Q resulted in the manifestation of Huntington behaviors and optimum expression of the huntingtin protein in vivo.

• Journal of the Korean Applied Science and Technology
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• v.37 no.2
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• pp.260-267
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• 2020

In this study, supercapacitor based on the all solid state electrolyte with PVA(polyvinyl alcohol), ionic liquid as a BMIMBF4(1-buthyl-3-methylimidazolium tetrafluoroborate) and activated carbon/Ni-MOF composite was fabricated and characterized its electrochemical properties with function of MOF. In order to analysis and comparison that electrochemical performances [including cyclic voltammetry(CV), electrochemical impedance spectroscopy(EIS) and galvanostatic charge/discharge test] of prepared supercapacitor based on activated carbon/Ni-MOF composite and all solid state electrolyte. As a result, specific capacitance of the supercapacitor without Ni-MOF was 380 F/g which value decreased to 340 F/g after adding Ni-MOF to activated carbon as a electrode material. This result exhibited that decreased electrochemical property of the supercapacitor effected on physical hinderance in the electrode. In further, it needs to optimization of the Ni-MOF amount (wt%) in the electrode composite to maximize its electrochemical performances.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.9
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• pp.641-647
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• 2014

In this study, sludge waste which has a difficulty in treating it was used to manufacture a fiber type of biosorbent. To solve the problems such as the release of organic pollutants and the difficulty in separating solid from treated water, entrapment method using Ca-alginate was used to immobilize sludge waste. Considering ease of manufacture as well as improvement of adsorptive ability, the biosorbent was manufactured in the form of fiber type. Optimum immobilization condition for minimizing the amount of alginate used and maximizing the performance of biosorbent was determined to be 10 g/L alginate concentration, 40 g/L sludge concentration, and 0.3-0.4 mm fiber diameter. The maximum Cd(II) uptake of the biosorbent was 60.73 mg/g. Pseudo-second-order kinetic model and Langmuir isotherm model adequately described the dynamic and equilibrium behaviors of Cd(II) biosorption onto the biosorbent, respectively. In conclusion, sludge waste generated from wastewater treatment process is a cheap raw material for the manufacture of biosorbent which can be used to remove toxic heavy metals from industrial wastewaters efficiently.

• Journal of the Korean Society for Railway
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• v.15 no.1
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• pp.9-16
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• 2012

The dynamic stability of railway vehicle has been one of the important issues in railway safety. The dynamic simulator has been used in the study about the dynamic stability of railway vehicle and wheel/rail interface optimization. Especially, a small scale simulator has been widely used in the fundamental study in the laboratory instead of full scale roller rig which is not cost effective and inconvenient to achieve diverse design parameters. But the technique for the design of the small scale simulator about the dynamic characteristics of the wheel-rail system and the bogie system has not been well developed in Korea. Therefore, the research using the small-scaled derailment simulator and the 1/5 scaled bogie has been conducted. In this paper, we did running stability test of 1/5 scaled bogie using small-scaled derailment simulator. Also, for the operation of the small scaled simulator, it is required to investigate the performance and characteristics of the simulator system. This could be achieved by a comparative study between an analysis and an experiment. This paper presented the analytical model which could be used for verifying the test results and understanding of the physical behavior of the dynamic system comprising the small- scaled derailment simulator and the 1/5 scaled bogie.

• Journal of Korea Water Resources Association
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• v.43 no.10
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• pp.893-909
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• 2010

The purpose of this study is to consider the interaction between surface water and groundwater in basin scale by developing TANK_GS model. The soil moisture structure of tank model with 3 tanks is improved to simulate the appropriate stream-aquifer interactions. Maximum likelihood method is applied to calibrate parameters with variance functions to deal with heteroscedasticity of residuals. The parameters of improved TANK_GS model and variance function are simultaneously estimated by Simulated Annealing method, a global optimization technique. The results of TANK-GE are compared to those of the SWMM-GE model which had been developed to consider the stream-aquifer interactions. The new TANK_GS model and SWMM-GE model are applied to Gapcheon basin, which belongs to Geum River basin. TANK_GS model showed better model performance compared to the original TANK model and characterized the relationship of stream-aquifer interactions as satisfactorily as the SWMM-GE model. The sustainable groundwater yield can be estimated for the regional water resources planning using the TANK_GS model

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.129-138
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• 2014

This paper proposes a fast intra prediction method to reduce encoding time for the HEVC(high-efficiency video coding) encoder. The proposed fast Intra prediction method uses quadtree structure and SATD(Sum of Absolute Transformed Differences). In HEVC, a $8{\times}8$ SATD value using $8{\times}8$ hadamard transform is used to calculate a SATD value for $8{\times}8$ or larger blocks. The proposed method calculates the best SATD value by using each $8{\times}8$ SATD result in $16{\times}16$ or larger blocks. After that, the proposed method removes a candidate mode for RDO(Rate-Distortion Optimization) based on comparing SATD of the candidate mode and the best SATD. By removing candidate modes, the proposed method reduces the operation of RDO and reduces total encoding time. In $8{\times}8$ block, the proposed method uses additional $4{\times}4$ SATD to calculat the best SATD. The experimental results show that the proposed method achieved 5.08% reduction in encoding time compared to the HEVC test model 12.1 encoder with almost no loss in compression performance.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.20 no.3
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• pp.97-102
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• 2014

It was mathematically analyzed at which steps to activate TTI in the cold chain for Alaska pollack, assuming that the performance of a commercial TTI product, and Fresh-check, could not always be optimized for the pollack. Three places were selected for the TTI activation, such as on fishing ship, Busan cooperative fish market, and mart. First, the kinetic and Arrhenius temperature dependent models were experimentally built under isothermal conditions. The color index of TTI and the level of Pseudomonas spp. of pollack were measured at time intervals. Second, the resultant models were used in the mathematical calculations for dynamic temperature conditions included in the cold chain. As a result, the TTI activated at the mart place showed the best agreement between the spoilage time of the pollack and the time for the TTI color to reach its end-point. It was therefore found that it is practically important to optimally select the TTI activation place or time when using a commercial TTI product.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.