• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2A
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• pp.107-112
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• 2009

Spectral efficiency of current two-phase cooperative communications protocols is low since in the second time the relay forwards the same signal received from the source to the destination, the source keeps silent in this time. In this paper, we propose a novel cooperative communications protocol where the signal needed to transmit to the destination is sent in both phases, the source and the relay also transmit different signal to the destination thus no loss of spectral efficiency. This protocol performs signal selection based on log-likelihood ratio (LLR) at relay and maximum likelihood (ML) detection at destination. While existing protocols pay for a worse performance than direct transmission in the low SNR regime which is of special interest in ad hoc networks, ours is better over the whole range of SNR. In addition, the proposal takes advantages of bandwidth efficiency, long delay and interference among many terminals in ad hoc network. Simulation results show that the proposed protocol can significantly save total energy for wireless ad hoc networks.

• Journal of the Korean Graphic Arts Communication Society
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• v.13 no.1
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• pp.93-104
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• 1995

Liquid crystal-polymer composite(LCPC) films promising new materials for both projection displays and vision product. LCPC films consist of a continous liquid crystal phase embedded in a three- dimensional network of polymer matrix. The liquid crystal in these LC phases can be elecrtrically switched giving rise to an opaque scattering off-stats and a transparent, non-scatting on- stats. In this work. a premixture is composed of LC, UV-curable monomer and photonitiator. LCPC films are formed by photopolymerization induced phase separation from this premixture. In conclusion, structure and electro-optical properties of LCPC films strongly depends on the selection of monomer, LC content and curing rate.

• Journal of the Korean Graphic Arts Communication Society
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• v.13 no.2
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• pp.19-32
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• 1995

Liquid crystal-polymer composite(LCPC) films are promising new materials for both projection displays and vision products. LCPC films consist of a continuous liquid crystal phase embedded in a three- dimensional network of polymer matrix. The liquid crystal in these LC phases can be electrically switched giving rise to an opaque scattering off-state and a transparent, non- scattering on-state. In this work, a premixture is composed of LC, UV-curable monomer and photonitiator. LCPC films are formed by photopolymerization induced phase separation from this premixture. In conclusion, structure and electro-optical properties of LCPC films strongly depends on the selection of monomer, LC content and curing rate.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.259-259
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• 2007

The low temperature sintering of dolomite type borates, $CaZr(BO_3)_2$[CZB] ceramics and their microwave dielectric properties were investigated The sintering temperature of CZB ceramics could be reduced from $1150^{\circ}C$ to $925^{\circ}C$ by the addition of sintering additive. $CaZrO_3$, $ZrO_2$ and $CaB_2O_4$ second phases were found in the CZB ceramics. The syntheses, sintering properties, microstructures, and dielectricnproperties of dolomite-type borates were examined by XRD, thermal analysis, electron microscopy, network analyzer, and the results are discussed intensively. The compatibility with silver electrode was also explored.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.115-131
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• 1997

The problem addressed in this paper is to minimize the deviations of workloads assigned to worker and to maximize the total utilizations(degree of skill) between workers and machines simultaneously. Each worker can handle the set of machines with the different degree of skill and each machine has the workloads needed to be processed. Also, each worker has to be assigned to at least one machine with the minimum workload deviation. This problem can be formulated as a preemptive goal programming with generalized assignment constraints. The proposed algorithm consists of two phases. First, a capacitated circulation network is constructed to assign the workers to machines with the maximum total utilizations while considering workloads balance. Then, a refinement process is applied to the split machines to satisfy the feasibility condition. The real industrial application in a plastic extrusion manufacturer is included along with several computational experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.5
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• pp.222-238
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• 2008

In this paper, the random key pre-distribution scheme introduced in ACM CCS'02 by Eschenauer and Gligor is reexamined, and a generalized form of key establishment is introduced. As the communication overhead is one of the most critical constraints of any successful protocol design, we introduce an alternative scheme in which the connectivity is maintained at the same level as in the original work, while the communication overhead is reduced by about 40% of the original overhead, for various carefully chosen parameters. The main modification relies on the use of a two-level key pool design and two round assignment/key establishment phases. Further analysis demonstrates the efficiency of our modification.

• Annual Conference of KIPS
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• 2017.04a
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• pp.677-679
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• 2017

Data convergecast is an indispensable task for any WSN applications. Typically, scheduling in the WSN consists of two phases: tree construction and scheduling. The optimal tree structure and scheduling for the network is proven NP-hard. This paper focuses on the delay optimality while constructing the data convergecast tree. The algorithm can take any tree as the input, and by performing the switches (i.e. a node changes its parent), the expected aggregation delay is potentially reduced. Note that while constructing the tree, only the in-tree collisions between the child nodes sending data to their common parent is considered.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.442-445
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• 1997

We present a hybrid self-tuning method of fuzzy inference systems with hyper elliptic Gaussian membership functions using genetic algorithm(GA) and back-propagation algorithm. The proposed self-tuning method has two phases : one is the coarse tuning process based on GA and the other is the fine tuning process based on back-propagation. But the parameters which is obtained by a GA are near optimal solutions. In order to solve the problem in GA applications, it uses a back-propagation algorithm, which is one of learning algorithms in neural networks, to finely tune the parameters obtained by a GA. We provide Box-Jenkins time series to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.