• Journal of Environmental Science International
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• v.27 no.9
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• pp.753-762
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• 2018

The present study set out to investigate the adsorption of Cd(II) ions in an aqueous solution by using Peanut Husk Biochar (PHB). An FT-IR analysis revealed that the PHB contained carboxylic and carbonyl groups, O-H carboxylic acids, and bonded-OH groups, such that it could easily adsorb heavy metals. The adsorption of Cd(II) using PHB proved to be a better fit to the Langmuir isotherm than to the Freundlich isotherm. The maximum Langmuir adsorption capacity was 33.89 mg/g for Cd(II). The negative value of ${\Delta}G^o$ confirm that the process whereby Cd(II) is adsorbed onto PHB is feasible and spontaneous in nature. In addition, the value of ${\Delta}G^o$ increase with the temperature, suggesting that a lower temperature is more favorable to the adsorption process. The negative value of ${\Delta}H^o$ indicates that the adsorption phenomenon is exothermic while the negative value of ${\Delta}S^o$ suggests that the process is enthalpy-driven. As an alternative to commercial activated carbon, PHB could be used as a low-cost and environmentally friendly adsorbent for removing Cd(II) from aqueous solutions.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.2788-2802
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• 2021

Multigroup cross section (MG XS) generation by the UNIST in-house Monte Carlo (MC) code MCS for fast reactor analysis using nodal diffusion codes is reported. The feasibility of the approach is quantified for two sodium fast reactors (SFRs) specified in the OECD/NEA SFR benchmark: a 1000 MW_th metal-fueled SFR (MET-1000) and a 3600 MW_th oxide-fueled SFR (MOX-3600). The accuracy of a few-group XSs generated by MCS is verified using another MC code, Serpent 2. The neutronic steady-state whole-core problem is analyzed using MCS/RAST-K with a 24-group XS set. Various core parameters of interest (core k_eff, power profiles, and reactivity feedback coefficients) are obtained using both MCS/RAST-K and MCS. A code-to-code comparison indicates excellent agreement between the nodal diffusion solution and stochastic solution; the error in the core k_eff is less than 110 pcm, the root-mean-square error of the power profiles is within 1.0%, and the error of the reactivity feedback coefficients is within three standard deviations. Furthermore, using the super-homogenization-corrected XSs improves the prediction accuracy of the control rod worth and power profiles with all rods in. Therefore, the results demonstrate that employing the MCS MG XSs for the nodal diffusion code is feasible for high-fidelity analyses of fast reactors.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.127-135
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• 2015

Universal design is defined as the design process of products and environments usable by all people to the greatest extent possible, without the need for adaptation or specialized design. The benefits of universal design have been promoted primarily through illustrative 'success stories' of public, residential and occupational environments and products. While case examples may be informative, they may unfortunately be limited in terms of generality to other designs or tasks. Therefore, design methods and criteria that can be applied systematically in a range of situations to encourage universal design are needed. In addition, the seven principles of universal design are intended to guide the design process. The principles provide a framework that allows a systematic evaluation of new or existing designs and assists in educating both designers and consumers about the characteristics of more usable products and environments. However, exactly how these principles are incorporated into the design process has beenleft up to the designer. Since the introduction of universal design, designers have become familiar with the principles of universal design, and they have developed many products based on universal design. However, the principles of universal design are qualitative, which means designers cannot quantitatively evaluate their designs. Some have worked to develop more systematic ways to evaluate products and environments by providing design guidelines for each of the principles. However, recommendations have not yet been made regarding how to integrate performance measures of universal design into the product design process before the product is mass produced. Furthermore, there are sets of requirements regarding each user group that has different age and ability. Consequently, there is an urgent need for design methods, based on a better understanding of age and ability related factors, which will lead to a universally designed product or environment. The authors have proposed the PSD (Preference Set-Based Design) method that can generate a ranged set of feasible solutions (i.e., robust and flexible solution set) instead of single point solution that satisfies changing sets of design targets. The objective of this paper is to develop a general method for systematically supporting the universal design process. This paper proposes the applicability of PSD method to universal design. Here, the proposed method is successfully illustrated with a universal design problem.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.95-105
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• 2003

Because the basic assumptions of deterministic user equilibrium assignment that all network users have perfect information of network condition and determine their routes without errors are known to be unrealistic, several stochastic assignment models have been proposed to relax this assumption. However. it is not easy to solve such stochastic assignment models due to the probability distribution they assume. Also. in order to avoid all path enumeration they restrict the number of feasible path set, thereby they can not preciously explain the travel behavior when the travel cost is varied in a network loading step. Another problem of the stochastic assignment models is stemmed from that they use heuristic approach in attaining optimal moving size, due to the difficulty for evaluation of their objective function. This paper presents a logit-based stochastic assignment model and its solution algorithm to cope with the problems above. We also provide a stochastic user equilibrium condition of the model. The model is based on path where all feasible paths are enumerated in advance. This kind of method needs a more computing demand for running the model compared to the link-based one. However, there are same advantages. It could describe the travel behavior more exactly, and too much computing time does not require than we expect, because we calculate the path set only one time in initial step Two numerical examples are also given in order to assess the model and to compare it with other methods.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.70-84
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• 2006

We propose a new distributed QoS routing scheme called pre-computation based selective probing (PCSP). The PCSP scheme is designed to provide an exact solution to the constrained optimization problem with moderate overhead, considering the practical environment where the state information available for the routing decision is not exact. It does not limit the number of probe messages, instead, employs a qualitative (or conditional) selective probing approach. It considers both the cost and QoS metrics of the least-cost and the best-QoS paths to calculate the end-to-end cost of the found feasible paths and find QoS-satisfying least-cost paths. It defines strict probing condition that excludes not only the non-feasible paths but also the non-optimal paths. It additionally pre-computes the QoS variation taking into account the impreciseness of the state information and applies two modified QoS-satisfying conditions to the selection rules. This strict probing condition and carefully designed probing approaches enable to strictly limit the set of neighbor nodes involved in the probing process, thereby reducing the message overhead without sacrificing the optimal properties. However, the PCSP scheme may suffer from high message overhead due to its conservative search process in the worst case. In order to bound such message overhead, we extend the PCSP algorithm by applying additional quantitative heuristics. Computer simulations reveal that the PCSP scheme reduces message overhead and possesses ideal success ratio with guaranteed optimal search. In addition, the quantitative extensions of the PCSP scheme turn out to bound the worst-case message overhead with slight performance degradation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.13-21
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• 2017

Airline schedules are highly dependent on various factors of uncertainties such as unfavorable weather conditions, mechanical problems, natural disaster, airport congestion, and strikes. If the schedules are not properly managed to cope with such disturbances, the operational cost and performance are severely affected by the delays, cancelations, and so forth. This is described as a disruption. When the disruption occurs, the airline requires the feasible recovery plan returning to the normal operations in a timely manner so as to minimize the cost and impact of disruptions. In this research, an Ant Colony Optimization (ACO) algorithm with re-timing strategy is developed to solve the recovery problem for both aircraft and passenger. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during a recovery period. The suggested algorithm is based on an existing ACO algorithm that aims to reflect all the downstream effects by considering the passenger recovery cost as a part of the objective function value. This algorithm is complemented by re-timing strategy to effectively manage the disrupted passengers by allowing delays even on some of undisrupted flights. The delays no more than 15 minutes are accepted, which does not influence on the on-time performance of the airlines. The suggested method is tested on the real data sets from 2009 ROADEF Challenge, and the computational results are compared with the existing ones on the same data sets. The method generates the solution for most of problem set in 10 minutes, and the result generated by re-timing strategy is discussed for its impact.

• Bulletin of the Society of Naval Architects of Korea
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• v.13 no.1
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• pp.17-23
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• 1976

Some methods of analysis of rectangular plates under distributed load of various intensity with interior supports are presented herein. Analysis of many structures such as bottom, side shell, and deck plate of ship hull and flat slab, with or without internal supports, Floor systems of bridges, included crthotropic bridges is a problem of plate with elastic supports or continuous edges. When the four edges of rectangular plate is simply supported, the double Fourier series solution developed by Navier can represent an exact result of this problem. If two opposite edges are simply supported, Levy's method is available to give an "exact" solution. When the loading condition and supporting condition of a plate does not fall into these cases, no simple analytic method seems to be feasible. Analysis of a simply supported rectangular plate under irregularly distributed loads of various intensity with internal supports is carried out by applying Navier solution well as the "Principle of Superposition." Finite difference technique is used to solve plates under irregularly distributed loads of various intensity with internal supports and with various boundary conditions. When finite difference technique is applied to the Lagrange's plate bending equation, any of fourth order derivative term in this equation produces at least five pivotal points leading to some troubles when the resulting linear algebraic equations are to be solved. This problem was solved by reducing the order of the derivatives to two: the fourth order partial differential equation with one dependent variable, namely deflection, is changed to an equivalent pair of second order partial differential equations with two dependent variables. Finite difference technique is then applied to transform these equations to a set of simultaneous linear algebraic equations. Principle of Superposition is then applied to handle the problems caused by concentrated loads and interior supports. This method can be used for the cases of plates under irregularly distributed loads of various intensity with arbitrary conditions such as elastic supports, or continuous edges with or without interior supports, and this method can also be solve the influence values of deflection, moment and etc. at arbitrary position of plates under the live load.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.12-19
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• 1993

In general, the line balancing problem is defined as of finding an assignment of the given jobs to the workstations under the precedence constraints given to the set of jobs. Usually, the objective is either minimizing the cycle time under the given number of workstations or minimizing the number of workstations under the given cycle time. In this paper, we present a new type of an assembly line balancing problem which occurs in an electronics company manufacturing home appliances. The main difference of the problem compared to the general line balancing problem lies in the structure of the precedence given to the set of jobs. In the problem, the set of jobs is partitioned into two disjoint subjects. One is called the set of fixed jobs and the other, the set of floating jobs. The fixed jobs should be processed in the linear order and some pair of the jobs should not be assigned to the same workstations. Whereas, to each floating job, a set of ranges is given. The range is given in terms of two fixed jobs and it means that the floating job can be processed after the first job is processed and before the second job is processed. There can be more than one range associated to a floating job. We present a procedure to find an approximate solution to the problem. The procedure consists of two major parts. One is to find the assignment of the floating jobs under the given (feasible) assignment of the fixed jobs. The problem can be viewed as a constrained bin packing problem. The other is to find the assignment of the whole jobs under the given linear precedence on the set of the floating jobs. First problem is NP-hard and we devise a heuristic procedure to the problem based on the transportation problem and matching problem. The second problem can be solved in polynomial time by the shortest path method. The algorithm works in iterative manner. One step is composed of two phases. In the first phase, we solve the constrained bin packing problem. In the second phase, the shortest path problem is solved using the phase 1 result. The result of the phase 2 is used as an input to the phase 1 problem at the next step. We test the proposed algorithm on the set of real data found in the washing machine assembly line.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1336-1356
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• 2017

The spectrum scarcity crisis has resulted in a shortage of resources for many emerging wireless services, and research on dynamic spectrum management has been used to solve this problem. Game theory can allocate resources to users in an economic way through market competition. In this paper, we propose a bidding game-based spectrum allocation mechanism in cognitive radio network. In our framework, primary networks provide heterogeneous wireless service and different numbers of channels, while secondary users have diverse bandwidth demands for transmission. Considering the features of traffic and QoS demands, we design a weighted interference graph-based grouping algorithm to divide users into several groups and construct the non-interference user-set in the first step. In the second step, we propose the dynamic bidding game-based spectrum allocation strategy; we analyze both buyer's and seller's revenue and determine the best allocation strategy. We also prove that our mechanism can achieve balanced pricing schema in competition. Theoretical and simulation results show that our strategy provides a feasible solution to improve spectrum utilization, can maximize overall utility and guarantee users' individual rationality.