• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1719-1726
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• 1996

In the OR-parallel execution of logic programs, binding environments have a critical impact on the performance. Particularly, this is true for distributed execution on parallel systems with a non-single address space. The reason is that in such systems, the remote accesses across processing elements deteriorate the performance. To solve this problem, some binding methods were previously proposed specifically for a non-single address space. However, compared with the binding methods for a single address space, they are far less efficient due to the overhead of newly introduced operations such as environment closing and back-unification, In this paper, we propose a new binding environment is a hybrid that combines both the binding methods for a single address space and those for anon-single address space. It acomplishes high efficiency by making closing operations unnecessary both at unification and at back-unification, while mainthing the restricted accesses.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.427-431
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• 2007

Applications in present-day mobile communication systems particularly require miniaturized dimensions and low-profiles of antenna in order to meet the mobile units. Thus, size reductions and bandwidth enhancements are becoming crucial design considerations for practical applications of microstrip antennas. The motivation of further experiments have been stepped to follow those studies for achieving compact and broadband, even multiplied operation modes, which are greatly increased with much attentions recently. To obtain broadband, single-feed, circularly polarized characteristics of microstrip antennas, a design with feed-line ought to be a factor of two. Usually, diagonally balanced-line feeds with hybrid coupler are employed to attain circular polarizations. We firstly formulated DGS (Defected Ground Structures) based operation principles of the entire microstrip components and therefore were able to derive impedance variance of feed-lines. After verifying corresponding experimental results, we targeted the frequency bands of UHF RFID (Ultra High Frequency Radio Frequency IDentification) and approximately of 0.4-2.4GHz have exhibited remarkable two resonance amplitudes as a dual band antenna. Our secondary researches were aimed to design quad band microstrip antenna which represents four resonance characteristics within the identical frequency bands as well. Microstrip patch has been meandered to lengthen the electrical paths, and the other design criteria with respecting physical parameters including radiation patterns and impedance bandwidths measurements will be described for verification. Advisable applications of these antennas can be GSM850, GSM900, GPS (L1-1575 and L2-1227) and UMTS-2110 of cellular systems, which extremely desire multiband and minimum size.

• Journal of KIISE
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• v.42 no.3
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• pp.379-389
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• 2015

With the advent of non-volatile memories such as Phase Change Memory (PCM or PRAM) and Magneto Resistive RAM (MRAM), active studies have been carried out on how to replace Dynamic Random-Access Memory (DRAM) with PRAM. In this paper, we study both endurance and performance issues of existing join algorithms that are based on PRAM-based computer systems and have been widely used until now: Block Nested Loop Join, Sort-Merge Join, Grace Hash Join, and Hybrid Hash Join. Our experimental results show that the existing join algorithms need to be redesigned to improve both the endurance and performance of PRAMs. To the best of our knowledge, this is the first research to scientifically study the results of the four join algorithms running on PRAM-based systems. In this work, our main contribution is the modeling and implementation of a PRAM-based simulator for a comparative study of the existing join algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.7
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• pp.139-148
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• 2012

An ensemble of classifiers is to employ a set of individually trained classifiers and combine their predictions. It has been found that in most cases the ensembles produce more accurate predictions than the base classifiers. Combining outputs from multiple classifiers, known as ensemble learning, is one of the standard and most important techniques for improving classification accuracy in machine learning. An ensemble of classifiers is efficient only if the individual classifiers make decisions as diverse as possible. Bagging is the most popular method of ensemble learning to generate a diverse set of classifiers. Diversity in bagging is obtained by using different training sets. The different training data subsets are randomly drawn with replacement from the entire training dataset. The random subspace method is an ensemble construction technique using different attribute subsets. In the random subspace, the training dataset is also modified as in bagging. However, this modification is performed in the feature space. Bagging and random subspace are quite well known and popular ensemble algorithms. However, few studies have dealt with the integration of bagging and random subspace using SVM Classifiers, though there is a great potential for useful applications in this area. The focus of this paper is to propose methods for improving SVM performance using hybrid ensemble strategy for bankruptcy prediction. This paper applies the proposed ensemble model to the bankruptcy prediction problem using a real data set from Korean companies.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.1
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• pp.73-87
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• 2016

Combining power generation and water production by desalination is economically advantageous. Most desalination projects use fossil fuels as an energy source, and thus contribute to increased levels of greenhouse gases. Environmental concerns have spurred researchers to find new sources of energy for desalination plants. The coupling of nuclear power production with desalination is one of the best options to achieve growth with lower environmental impact. In this paper, we will per-form a sensitivity study of coupling nuclear power to various combinations of desalination technology: {1} thermal (MSF [Multi-Stage Flashing], MED [Multi-Effect Distillation], and MED-TVC [Multi-Effect Distillation with Thermal Vapour Compression]); {2} membrane RO [Reverse Osmosis]; and {3} hybrid (MSF-RO [Multi-Stage Flashing & Reverse Osmosis] and MED-RO [Multi-Effect Distillation & Reverse Osmosis]). The Korean designed reactor plant, the APR1400 will be modeled as the energy production facility. The economical evaluation will then be executed using the computer program DEEP (Desalination Economic Evaluation Program) as developed by the IAEA. The program has capabilities to model several types of nuclear and fossil power plants, nuclear and fossil heat sources, and thermal distillation and membrane desalination technologies. The output of DEEP includes levelized water and power costs, breakdowns of cost components, energy consumption, and net saleable power for any selected option. In this study, we will examine the APR1400 coupled with a desalination power plant in the Kingdom of Saudi Arabia (KSA) as a prototypical example. The KSA currently has approximately 20% of the installed worldwide capacity for seawater desalination. Utilities such as power and water are constructed and run by the government. Per state practice, economic evaluation for these utilities do not consider or apply interest or carrying cost. Therefore, in this paper the evaluation results will be based on two scenarios. The first one assumes the water utility is under direct government control and in this case the interest and discount rate will be set to zero. The second scenario will assume that the water utility is controlled by a private enterprise and in this case we will consider different values of interest and discount rates (4%, 8%, & 12%).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.11
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• pp.1004-1013
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• 2012

The legacy voice broadcast systems are used to broadcast the voice over an entire space or a specific zone. these broadcast systems generate unnecessary noise and waste of resources. In this paper, we propose a ubiquitous voice message broadcast system called uPaging, by combining the technique of location-awareness and the voice message delivery service in ubiquitous sensor network environment. In uPaging system, the wire/wireless hybrid network is used to implement the network system. Also, in order to actualize the location-awareness service, we use the Bidirectional Location ID-Exchange protocol was suggested by our previous research. the uPaging system can deliver the voice to a selected user or the location in which the user is present by this location awareness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.314-322
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• 2007

This paper proposes a new optimization algorithm named by GBNSGA(Goal-Pareto Based Non-dominated Sorting Genetic Algorithm) which determines the best configuration for CR(Cognitive Radio) communication systems. Conventionally, in order to select the proper radio configuration, genetic algorithm has been introduced so as to alleviate computational burden along the execution of the cognition cycle proposed by Mitola. This paper proposes a novel optimization algorithm designated as GBNSGA for cognitive engine which can be described as a hybrid algorithm combining well-known Pareto-based NSGA(Non-dominated Sorting Genetic Algorithm) as well as GP(Goal Programming). By conducting computer simulations, it will be verified that the proposed method not only satisfies the user's service requirements in the form of goals. It reveals the fast optimization capability and more various solutions rather than conventional NSGA or weighted-sum approach.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.159-167
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• 2018

In this paper, we propose an algorithm to optimize energy efficiency in a multi-user decoupled RF energy harvesting network and experiment on the trend of energy efficiency change assuming users' various geographical distribution scenarios. In the RF energy harvesting network where both wireless data transmission and RF energy harvesting are simultaneously performed, the energy efficiency is a key indicator of network performance, and it is necessary to investigate how various factors can affect the energy efficiency. In order to increase energy efficiency effectively, we can confirm that users' distributions are important factors in the RF energy harvesting network from the simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.98-106
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• 1998

This paper proposes an efficient learning algorithm for improving the training performance of the neural network. The proposed method improves the training performance by applying the backpropagation algorithm of a global optimization method which is a hybrid of a stochastic approximation and a conjugate gradient method. The approximate initial point for f a ~gtl obal optimization is estimated first by applying the stochastic approximation, and then the conjugate gradient method, which is the fast gradient descent method, is applied for a high speed optimization. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to those of the conventional backpropagation and the backpropagation algorithm which is a hyhrid of the stochastic approximation and steepest descent method.