• Progress in Superconductivity and Cryogenics
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• v.5 no.1
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• pp.13-16
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• 2003

Adhesion of $SrZrO_3$ resistive oxide barrier on Ag sheathed Bi(2223) tapes prepared by the sol-gel and dip-coating method was evaluated with an aid of Taguchi method and Lie($2^1{\times}3^7$) orthogonal arrays to determine the optimal process combination of levels of factors that best satisfy the bigger is better quality characteristic (QC=B). For analyses of results statistical calculations such as average and analysis of variance (ANOVA) were employed to analyze the results for improving the performance qualities of the dip-coated $SrZrO_3$ film. Experimentally, the performance of the films was evaluated in terms of bond strength by varying Sr/Zr moi ratio (A), amount of organic vehicle additives (B), drying temperature (C) and time (D), heat treatment temperature (E) and time (F), respectively. The optimal combination of levels of factors was determined to be $A_3B_2C_3D_2E_1F_3$ having a 90% confidence level.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.396-402
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• 2005

In an electricity market with imperfect competition, participants devise bidding plans and transaction strategies to maximize their own profits. The market price and the quantity are concerned with the operation reserve as well as the bidding system and demand curves in an electricity market. This paper presents a market model combined by an energy market and an operating reserve market. The competition of the generation producers in the combined market is formulated as a gaming of selecting bid parameters such as intersections and slopes in bid functions. The Nash Equilibrium (NE) is analyzed by using bi-level optimization; maximization of Social Welfare (SW) and maximization of the producers' profits.

• Journal of Korea Port Economic Association
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• v.22 no.1
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• pp.175-198
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• 2006

This paper presents a network capacity model that can be used as an analytical tool for strategic planning and resource allocation for multimodal transportation systems. In the context of freight transportation, the multimodal network capacity problem (MNCP) is formulated as a mathematical model of nonlinear bi-level optimization problem. Given network configuration and freight demand for multiple origin-destination pairs, the MNCP model is designed to determine the maximum flow that the network can accommodate. To solve the MNCP, a heuristic solution algorithm is developed on the basis of a linear approximation method. A hypothetical exercise shows that the MNCP model and solution algorithm can be successfully implemented and applied to not only estimate the capacity of multimodal network, but also to identify the capacity gaps over all individual facilities in the network, including intermodal facilities. Transportation agencies and planners would benefit from the MNCP model in identifying investment priorities and thus developing sustainable transportation systems in a manner that considers all feasible modes as well as low-cost capacity improvements.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.175-184
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• 2008

Uncertainties are unavoidable in engineering applications. In this paper we propose an alpha reliable multi-variable network design problem under demand uncertainty. In order to decide the optimal capacity enhancement, three performance measures based on 3E(Efficiency, Equity, and Environmental) are considered. The objective is to minimize the total budget required to satisfy alpha reliability constraint of total travel time, equity ratio, and total emission, while considering the route choice behavior of network users. The problem is formulated as the chance-constrained model for application of alpha confidence level and solved as a lexicographic optimization problem to consider the multi-variable. A simulation-based genetic algorithm procedure is developed to solve this complex network design problem(NDP). A simple numerical example ispresented to illustrate the features of the proposed NDP model.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.374-384
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• 2016

The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

• Journal of the Korean Magnetics Society
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• v.23 no.1
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• pp.1-6
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• 2013

Spin-orbit coupling (SOC) effect is known to be the physical origin for various exotic magnetic phenomena in the low-dimensional systems. Recently, SOC also draws lots of attention in the study on magnetically doped thermoelectric alloys to determine their properties as the thermoelectric application as well as the topological insulator via the exact electronic structures determination near the Fermi level. In this research, aiming to investigate the spin-orbit coupling effect on the structural properties such as the lattice constants and the bulk modulus of the most widely investigated thermoelectric host material, $Bi_2Te_3$, we carried out the first-principles electronic structure calculation using the all-electron FLAPW (full-potential linearized augmented plane-wave) method. Employing both the local density approximation (LDA) and the generalized gradient approximation (GGA), the structural optimization is achieved by varying the in-plane lattice constant fixing the perpendicular lattice constant and vice versa, to find that the SOC effect increases the equilibrium lattices slightly in both directions while it markedly reduces the bulk modulus value implying the strong orientational dependence, which are attributed to the material's intrinsic structural anisotropy.