• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3134-3155
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• 2020

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush-Kuhn-Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2543-2547
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• 2011

This paper suggests a new method for team-based learning which is focusing on the individual job assignment to solve the possible problems of free riders, dysfunctional conflicts, and hostility among students. Especially, it examines that if a method using job assignment could reduce the adverse effects of peer evaluation. This study was executed during one semester. Each team was organized with seven members and an independent job among seven different jobs was assigned to each member. At the end of the semester, it was examined that if individual job assignment had solved the possible problems of free riders, dysfunctional conflicts, and hostility among students. Finally, this study found that a new method of team-based learning using a job assignment lessened a problem of free riders, and reduced dysfunctional conflicts and hostility among students comparing with the peer evaluation method. The suggested method in this paper, which focuses on the job assignment of team project, could improve the efficiency of team-based learning in education course.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.64.1-64
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• 2002

$\textbullet$ Introduction $\textbullet$ CRA method for transient response control $\textbullet$ Benchmark problem and specifications $\textbullet$ Controller design using CRA method $\textbullet$ Concluding Remark

• Journal of Industrial Technology
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• v.11
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• pp.27-31
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• 1991

Consider a problem to keep half of the poles unchanged when some of the coefficients of stable characteristic polynomials are perturbed. A procedure was proposed for the problem. However, the pole assignment procedure has not been addressed. A simple algorithm for the procedure is proposed in this paper.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.481-491
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• 2014

In this paper, we discussed a problem for improving the throughput of a crankshaft manufacturing line in an automotive factory in which the budget for purchasing new machines and installing additional buffers is limited. We also considered the constraint of available space for both of machine and buffer. Although this problem seems like a kind of buffer allocation problem, it is different from buffer allocation problem because additional machines are also considered. Thus, it is not easy to calculate the throughput by mathematical model, and therefore simulation model was developed using $ARENA^{(R)}$ for estimating throughput. To determine the investment plan, a modified Arrow Assignment Rule under some constraints was suggested and it was applied to the real case.

• The Journal of the Acoustical Society of Korea
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• v.23 no.2
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• pp.109-116
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• 2004

This paper deals with data association using 3 sets of passive linear array sonars (PUS) geometrically positioned in a Y-shaped configuration, but fixed in an underwater environment. The data association problem is directly transformed into a 3-D assignment problem, which is known to be NP-hard. For generic passive sensors, it can be sotted using conventional algorithms, while it in PLAS becomes a formidable task due to the presence of bearing ambiguity. In particular, we proposed data association method robust to bearing measurements errors by incorporating frequency information and analyze a region of ghost problem by geometrical relation PUS and target. We analyzed the effectiveness of the proposed method by representative simulation in multi-target.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.335-345
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• 2005

In personalized marketing, it is important to maximize customer satisfaction and marketing efficiency. As personalized campaigns are frequently performed, several campaigns are frequently run simultaneously. The multiple recommendation problem occurs when we perform several personalized campaigns simultaneously. This implies that some customers may be bombarded with a considerable number of campaigns. We raise this issue and formulate the multi-campaign assignment problem to solve the issue. We propose dynamic programming method and various heuristic algorithms for solving the problem. With field data, we also present experimental results to verify the importance of the problem formulation and the effectiveness of the proposed algorithms.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.601-608
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• 2001

The automated assignment of multiple tasks to executing agents is a key problem in the area of multi-agent systems. In many domains, significant savings can be achieved by reallocating tasks among agents with different costs for handling tasks. The automation of task reallocation among self-interested agents requires that the individual agents use a common negotiation protocol that prescribes how they have to interact in order to come to an agreement on "who does what". In this paper, we introduce the multi-agent Traveling Salesman Problem(TSP) as an example of task reallocation problem, and suggest the Vickery auction as an interagent negotiation protocol for solving this problem. In general, auction-based protocols show several advantageous features: they are easily implementable, they enforce an efficient assignment process, and they guarantce an agreement even in scenarios in which the agents possess only very little domain-specific Knowledge. Furthermore Vickrey auctions have the additional advantage that each interested agent bids only once and that the dominant strategy is to bid one′s true valuation. In order to apply this market-based protocol into task reallocation among self-interested agents, we define the profit of each agent, the goal of negotiation, tasks to be traded out through auctions, the bidding strategy, and the sequence of auctions. Through several experiments with sample multi-agent TSPs, we show that the task allocation can improve monotonically at each step and then finally an optimal task allocation can be found with this protocol.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3831-3851
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• 2015

This paper is concerned with the problem of joint resource allocation and user-pairing in virtual MIMO SC-FDMA systems to improve service quality of experience (QoE). No-reference logarithmic model is introduced to quantify service experience for each user and the objective is to maximize sum of all user's mean of score (MOS). We firstly formulate the optimal problem into an S-dimensional (S-D) assignment problem. Then, to solve this problem, the modified Lagrangian relaxation algorithm is deduced to obtain the suboptimal result of joint user-paring and subchannel allocation. The merits of this solution are as follows. First, the gap between its results and the global optimal one can be quantified and controlled by balancing the complexity and accuracy, which merit the other suboptimal algorithms do not have. Secondly, it has the polynomial computational complexity and the worst case complexity is O(3LN³), where L is the maximum iteration time and N is the number of subchannels. Simulations also prove that our proposed algorithm can effectively improve quality of experience and the gap between our proposed and the optimal algorithms can be controlled below 8%.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.391-399
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• 2005

In this paper we present an algorithm based on network flow techniques which provides a solution for a combinatorial problem. Then, in order to provide all the solutions of this problem, we make use of an algorithm that given the bipartite graph $G=(V_1 {\cup}{V_2},\;E,\;{\omega})$ outputs the enumeration of all bipartite matchings of given cardinality v and cost c.