• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.1-7
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• 2023

This paper proposes a small cell resource partitioning allocation method to solve interference to machine type communication devices (MTCD) and improve performance in 5G heterogeneous networks (HetNet) where macro base station (MBS) and many small cell base stations (SBS) are overlaid. In the 5G HetNet, since various types of MTCDs generate data traffic, the load on the MBS increases. Therefore, in order to reduce the MBS load, a cell range expansion (CRE) method is applied in which a bias value is added to the received signal strength from the SBS and MTCDs satisfying the condition is connected to the SBS. More MTCDs connecting to the SBS through the CRE will reduce the load on the MBS, but performance of MTCDs will degrade due to interference, so a method to solve this problem is needed. The proposed small cell resource partitioning allocation method allocates resources with less interference from the MBS to mitigate interference of MTCDs newly added in the SBS with CRE, and improve the overall MTCD performace using separating resources according to the performance of existing MTCDs in the SBS. Through simulation results, the proposed small cell resource partitioning allocation method shows performance improvement of 21% and 126% in MTCDs capacity connected to MBS and SBS respectively, compared to the existing resource allocation methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.164-171
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• 2013

XRF spectrum data of a material include a lot of background signals which are not related to its components. Since an XRF analyzer analyzes components and concentrations of an analyte using the locations and magnitudes of gaussian-shaped peaks extracted from a spectrum, its background signals need to be removed completely from the spectrum for the accurate analysis. Morphology-based method, SNIP-based method and thresholding-based method have been used to remove background signals. In the paper, a background removal method, an improved version of an interval-thresholding-based method, is proposed. The proposed method consists of interval partitioning, interval classifying, and background estimation. Experimental results show that the proposed method has better performance on background removal from the spectrum than the existing methods, morphology-based method and SNIP-based method.

• Korean Management Science Review
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• v.21 no.1
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• pp.39-55
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• 2004

Efficient crew scheduling for cockpit crew is important in airline industry due to operational safety and cost associated with the flight duty time. Because of complexity of regulations imposed to the cockpit crew. it is complicated to generate an efficient schedule. Schedule of cockpit crew can be generated through two steps; selecting of flight patterns. and scheduling of them to the specific time horizon. Heuristic method is developed and applied with massive data in a limited time of computation. A set of flight patterns is selected from all possible flight patterns. which are generated by composing the flight leg based on regulations. by using the set partitioning problem with objective function of oversea stay cost. The selected set of flight patterns found at the first step is allocated to 4 week crew schedule to minimize the variance of total fight time assigned to each crew. The crew schedules obtained are evaluated and compared with the ones currently used in one of major airline company.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.465-468
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• 1996

We deal with facility layout problems with shape constraints. A simulated annealing algorithm is developed for the problems. In the algorithm, a solution is encoded as a matrix that has information about relative locations of the facilities in the floor. A block layout is constructed by partitioning the floor into a set of rectangular blocks according to the information while satisfying areas of facilities. In this paper, three methods are suggested for the partitioning procedure and they are employed in the simulated annealing algorithm. Results of computational experiments show that the proposed algorithm performs better than existing algorithms, especially for problems with tight shape constraints.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.70-81
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• 1997

In this paper, we propose HMCNN(hybrid multiple component neural networks) that enhance performance of MCNN by adapting new pattern partitioning algorithm which can cluster many input patterns efficiently. Added neural network performs similar learning procedure that of kohonen network. But it dynamically determine it's number of output neurons using algorithms that decide self-organized number of clusters and patterns in a cluster. The proposed network can effectively be applied to problems of large data as well as huge networks size. As a sresutl, proposed pattern partitioning network can enhance performance results and solve weakness of MCNN like generalization capability. In addition, we can get more fast speed by performing parallel learning than that of other supervised learning networks.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.557-560
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• 1988

In this paper, we present a new k-way partitioning algorithm for a graph of an electrical circuit wherein nodes and edges are regarded as cells (modules) and nets, respectively. In contrast to the previous work, our method is based upon a linearly ordered partition paradigm. We also claim that the maximum number of netcuts mostly governs the performance of k-way partitioning, thus having influence on the construction of a new cost function. In addition, our approach elaborates upon balancing the partition size. Our experiments show excellent results in comparison with previous k-way partitioning algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.112-114
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• 1993

This paper presents a partitioning technique for equivalent models to large scale power system. The proposed partitioning technique is utilized by using a heuristic approach based on distributed array processor and the coherent groupings of generator buses. In this paper the reduced Ward-PV method considering the characteristics of buses is used to equivalent models of external system. The technique is demonstrated on New England system with 39 buses, 46 lines and 10 generators.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.401-405
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• 2004

Column subtraction, originally proposed by Harche and Thompson(]994), is an exact method for solving large set covering, packing and partitioning problems. Since the constraint set of ship scheduling problem(SSP) have a special structure, most instances of SSP can be solved by LP relaxation. This paper aims at applying the column subtraction method to solve SSP which can not be solved by LP relaxation. For remained instances of unsolvable ones, we subtract columns from the finale simplex table to get another integer solution in an iterative manner. Computational results having up to 10,000 0-1 variables show better performance of the column subtraction method solving the remained instances of SSP than complex branch-and-bound algorithm by LINDO.

• Journal of Navigation and Port Research
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• v.28 no.2
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• pp.129-133
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• 2004

Column subtraction, originally proposed by Harche and Thompson(1994), is an exact method for solving large set covering, packing and partitioning problems. Since the constraint set of ship scheduling problem(SSP) have a special structure, most instances of SSP can be solved by LP relaxation This paper aim, at applying the column subtraction method to solve SSP which am not be solved by LP relaxation For remained instances of unsolvable ones, we subtract columns from the finale simplex table to get another integer solution in an iterative manner. Computational results having up to 10,000 0-1 variables show better performance of the column subtraction method solving the remained instances of SSP than complex branch and-bound algorithm by LINDO.