• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.749-754
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• 1999

The cost is known to be proportional to the size of sample. We consider a cost function of the form Cost=c1np+c2npmq where c1, c2 p, and q are all positive constants. This cost function is to be used in finding an optimal allocation in two-stage cluster sampling. The optimal allocations of n and m gives the properties of uniqueness under some conditions and of monotonicity with p>0 when q=1.

• The Korean Journal of Financial Management
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• v.2 no.1
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• pp.55-75
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• 1986

This article is to analyse the economic efficiency of capital market, which plays a role of resource allocation in terms of financial claims such as stock and bond. It provides various contributions to the welfare theoretical aspects of modern capital market theory. The key feature that distinguishes the theory described here from traditional welfare theory is the presence of uncertainty. Securities has time dimensions and the state and outcome of the future are really uncertain. This problem resulting from this uncertainty can be solved by complete market, but it has a weak power to explain real stock market. Capital Market is faced with the uncertainity because it is a kind of incomplete market. Individuals and firms in capital market made their consumption-investment decision by their own criteria, i. e. the maximization of expected utility form intertemporal consumption and the maximization of the market value of firm. We noted that allocative decisions that had to be made in the economy could be naturally subdivided into two groups. One set of decisions concerned the allocation of first-period resources among consumption $C_i$, investment in risky firms $I_j$, and riskless investment M. The other decisions concern the distribution among individuals of income available in the second period $Y_i(\theta)$. Corresponing to this grouping, the theoretical analysis of efficiency has also been dichotomized. The optimality of the distribution of output in the second period is distributive efficiency" and the optimality of the allocation of first-period resources is 'the efficiency of investment'. We have found in the distributive efficiency that the conditions for attainability is the same as the conditions for market optimality. The necessary and sufficient conditions for attainability or market optimality is that (1) all utility functions are such that -$\frac{{U_i}^'(Y_i)}{{U_i}^"(Y_i)}={\mu}_i+{\lambda}Y_i$-linear risk tolerance function where the coefficients ${\mu}_i$ and $\lambda$ are independent of $Y_i$, and (2) there are homogeneous expectations, i. e. ${\Large f}_i(\theta)={\Large f}(\theta)$ for every i. On the other hand, the efficiency of investment has disagreement about optimal investment level. The investment level for market rule will not generally lead to Pareto-optimal allocation of investment. This suboptimality is caused by (1)the difference of Diamond's decomposable production function and mean-variance valuation model and (2) the selection of exelusive investment or competitive investment. In conclusion, this article has made an analysis of conditions and processes of Pareto-optimal allocation of resources in capital marker and tried to connect with significant issues in modern finance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.917-922
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• 2016

Recently, energy harvesting, in which energy is collected from RF signals, has been regarded as a promising technology to improve the lifetime of sensors by alleviating the lack of power supply problem. In this paper, we try to propose an efficient algorithm for simultaneous wireless information and power transfer. At first, we find the lower bound of water-level using the probability density function of channel, and derive the solution of power allocation in energy harvesting networks. In addition, we derive an efficient power splitting method for satisfying the minimum required harvested energy constraint. The simulation results confirm that the proposed scheme improves the average data rate while guaranteeing the minimum required harvested energy constraint, compared with the conventional scheme. In addition, the proposed algorithm can reduce the computational complexity remarkably with insignificant performance degradation less than 10%, compared to the optimal solution.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.9
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• pp.691-701
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• 2020

An actuator mixer design using convex optimization technique situation where the propulsion system of an electric VTOL UAV during vertical take-off and landing maneuvers is proposed. The attainable control set to analyze the impact from failure of each motor and propeller can be calculated and illustrated using the properties of the convex function. The control allocation can be defined as a convex function optimization problem to obtain an optimal solution in real time. The mixer is implemented using a convex optimization solver, and the performance of the control allocation methods is compared to the attainable control set. Finally, the proposed mixer is compared with other techniques with nonlinear sux degree-of-freedom simulation.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.136-145
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• 2007

The selection of suppliers and the determination of order quantities to be placed with those suppliers are important decisions in a supply chain. In this research, a non-linear mixed integer programming model is presented to select suppliers and determine the order quantities. The model considers the purchasing cost which takes into account quantity discount, the cost of transportation, the fixed cost for establishing suppliers, the cost for holding inventory, and the cost of receiving poor quality parts. The capacity constraints for suppliers, quality and lead-time requirements for the parts are also taken into account in the model. Since the purchasing cost, which is a decreasing step function of order quantities, introduces discontinuities to the non-linear objective function, it is not easy to employ traditional optimization methods. Thus, a heuristic algorithm, called particle swarm optimization (PSO), is used to find the (near) optimal solution. However, PSO usually generates initial solutions randomly. To improve the PSO solution quality, a heuristic procedure is proposed to find an initial solution based on the average unit cost including transportation, purchasing, inventory, and poor quality part cost. The results show that PSO with the proposed initial solution heuristic provides better solutions than those with PSO algorithm only.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.4
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• pp.38-49
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• 1998

A fossil power plant can be modeled by a lot of algebraic equations and differential equations. When we simulate a large, complicated fossil power plant by a computer such as workstation or PC, it takes much time until overall equations are completely calculated. Therefore, new processing systems which have high computing speed is ultimately needed for real-time or high-speed(faster than real-time) simulators. This paper presents an enhanced strategy in which high computing power can be provided by parallel processing of DSP processors with communication links. DSP system is designed for general purpose. Parallel DSP system can be easily expanded by just connecting new DSP modules to the system. General urpose DSP modules and a VME interface module was developed. New model and techniques for the task allocation are also presented which take into account the special characteristics of parallel I/O and computation. As a realistic cost function of task allocation, we suggested 'simulation period' which represents the period of simulation output intervals. Based on the development of parallel DSP system and realistic task allocation techniques, we cound achieve good efficiency of parallel processing and faster simulation speed than real-time.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.54-64
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• 2011

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. The human resource allocation problem (hRAP) under consideration contains two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decisionbased genetic algorithm (P-mdGA). During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach is used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we use fuzzy logic controller for fine-tuning of genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA is applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.2039-2043
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• 2007

Like other major river basin systems in the West of the United States the Platte River Basin are faced with the challenges of allocating more water for plant and animal species. A part of the Central Platte River was designated as critical habitat for the whooping crane in 1978. The water allocation system in the Platte River Basin is dominated by the Prior Appropriation Doctrine, which allocates water according to the priorities based on the date of water use. The Platte River Basin segregated into five subregions for purpose of analysis. 24 years of historic records of monthly flow and all the demands were complied. The simulation of river basin modeling includes physical operation of the system including water allocation by water rights and interstate compact agreements, reservoir operations, and diversion with consumptive use and return flow. MODSIM, a generalized river basin network model, was used for estimating the timing and magnitude of impacts on river flows and diversions associated with water transfers from each region. A total of 20 alternatives were considered, covering transfers from each of the five regions of basin with several options. The result shows that the timing and availability of augmented water at the critical habitat is not only a function of use by junior appropriators, but also of river losses, and timing of return flows.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2311-2319
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• 1999

We present two heuristic algorithms for the task allocation problem (NP-complete problem) in parallel computing. The problem is to find an optimal mapping of multiple communicating tasks of a parallel program onto the multiple processing nodes of a distributed-memory multicomputer. The purpose of mapping these tasks into the nodes of the target architecture is the minimization of parallel execution time without sacrificing solution quality. Many heuristic approaches have been employed to obtain satisfactory mapping. Our heuristics are based on genetic algorithms and simulated annealing. We formulate an objective function as a total computational cost for a mapping configuration, and evaluate the performance of our heuristic algorithms. We compare the quality of solutions and times derived by the random, greedy, genetic, and annealing algorithms. Our experimental findings from a simulation study of the allocation algorithms are presented.