• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.3
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• pp.1-12
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• 1992

Parallel Genetic Algorithms (GAs) are developed to solve a single machine n-job scheduling problem which is to minimize the sum of absolute deviations of completion times from a common due date. (0, 1) binary scheme is employed to represent the n-job schedule. Two selection methods, best individual selection and simple selection are examined. The effect of crossover operator, due date adjustment mutation and due date adjustment reordering are discussed. The performance of the parallel genetic algorithm is illustrated with some example problems.

• ETRI Journal
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• v.41 no.5
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• pp.637-647
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• 2019

Separating highly correlated users can reduce the loss caused by spatial correlation (SC) in multiuser multiple-input multiple-output (MU-MIMO) systems. However, few accurate analyses of the loss caused by SC have been conducted. In this study, we define signal-to-interference-plus-noise ratio (SINR) loss to characterize it in multiuser multiple-input single-output (MU-MISO) systems, and use coefficient of correlation (CoC) to describe the SC between users. A formula is deduced to show the accurate relation between SINR loss and CoC. Based on this relation, we propose a user selection method that utilizes CoC to minimize the average SINR loss of users in massive MU-MISO systems. Simulation results verify the correctness of the relation and show that the proposed user selection method is very effective at reducing the loss caused by SC in massive MU-MISO systems.

• Korean Journal of Mathematics
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• v.32 no.2
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• pp.213-218
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• 2024

Assume that at a certain locus there are three genotypes and that for every one progeny produced by an I^AI^A homozygote, the heterozygote I^AI^B produces. W. Choi found the adapted partial differential equations for the density and operator of the frequency for one gene and applied this adapted partial differential equations to several diploid model. Also, he found adapted partial differential equations for the diploid model against recessive homozygotes and in case that the alley frequency occurs after one generation of selection when there is no dominance. (see. [1, 2]). In this paper, we find the adapted partial equations for the model of selection against heterozygotes and in case that the allele frequency changes after one generation of selection when there is overdominance. Finally, we shall find the partial differential equation of general type of selection at diploid model and it also shall apply to actual examples. This is a very meaningful result in that it can be applied in any model.

• Korea Journal of Hospital Management
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• v.26 no.3
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• pp.68-83
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• 2021

Background: It is widely acknowledged that single bedrooms have many potential advantages compared to multiple bedrooms. However, Korea has a reimbursement system that patients have to pay the additional fee if they will use single bedroom or pay-bed (1-3 bedroom). There is little research on patients' bed selection and relationship between patient satisfaction and bed type in the Korean setting. Methods: Using the 2017 Korea Health Panel (KHP) Survey data, we modified bed type by two dichotomous variables: single bedroom vs. multiple bedroom (2+ bedrooms) and pay-bed (1-3 bedroom) vs. reimbursed bed (4+ bedroom). Multivariate logistic regression is performed to determine the factors affecting the patient's choice of room types. Multivariate regression analysis was conducted to examine how hospital room types are affecting patient satisfaction. Results: Single room and pay-bed (1-3 bedroom) use was influenced by patient age (19- years old), male, a person at work, hospitalizing in a clinic, and birth. After controlling variables of the behavioral model of medical utilization, the association between patient satisfaction and use of single bedroom & pay-bed (1-3 bedroom). Cause of hospitalization, major treatment, and recognition of unnecessary care are statistically significant variables on patient satisfaction. Conclusion: Although the single room is about the standard for newly built hospitals in western countries, it remains unclear that single room or pay-bed (1-3 bedroom) has positive effects on patient's outcomes and satisfaction. In this empirical study, the difference in patient satisfaction by bedroom type was not noticeable. In terms of bed management by hospital staff, securing patient credibility in hospital care is an important task. Patients' perception of whether medical staffs have encouraged unnecessary treatment or tests has a significant impact on patient's satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3524-3542
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• 2017

In a single-source and multi-relay amplify-forward (AF) cooperative network, the outage probability and the power allocation are two key factors to influence the performance of an entire system. In this paper, an optimized AF relay selection by an exclusive method and near optimal power allocation (NOPA) is proposed for both good outage probability and power efficiency. Given the same power at the source and the relay nodes, a threshold for selecting the relay nodes is deduced and employed to minimize the average outage probability. It mainly excludes the relay nodes with much higher thresholds over the aforementioned threshold and thus the remainders of the relay nodes participate in cooperative forwarding efficiently. So the proposed scheme can improve the utility of the resources in the cooperative multi-relay system, as well as reduce the computational complexity. In addition, based on the proposed scheme, a NOPA is also suggested to approach sub-optimal power efficiency with low complexity. Simulation results show that the proposed scheme obtains about 2.1dB and 5.8dB performance gain at outage probability of $10^{-4}$, when compared with the all-relay-forward (6 participated relays) and the single-relay-forward schemes. Furthermore, it obtains the minimum outage probability among all selective relay schemes with the same number of the relays. Meanwhile, it approaches closely to the optimal exhaustive scheme, thus reduce much complexity. Moreover, the proposed NOPA scheme achieves better outage probability than those of the equal power allocation schemes. Therefore, the proposed scheme can obtain good outage probability, low computational complexity and high power efficiency, which makes it pragmatic efficiently in the single-source and multi-relay AF based cooperative networks.

• Bulletin of the Korean Chemical Society
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• v.8 no.4
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• pp.338-340
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• 1987

The single-frequency two-photon absorption tensor is carefully rederived and examined. It is pointed out that the conventionally used tensor, which has been formally deduced from the different-frequency two-photon absorption tensor, can give an incorrect absolute two-photon absorption rate. The identity forbidded selection rule and the polarization ratio expressions are also examined with the new tensor.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1540-1549
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• 2017

Objective: Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP) markers strictly selected through least absolute shrinkage and selection operator (LASSO) feature selection. Methods: We performed farm tracing of domesticated pig (Sus scrofa) from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers. Results: We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs. Conclusion: The study represents a successful application of LASSO feature selection on multi-breed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability.

• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.65-73
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• 2003

Most web-based learning is furnishing single side and consistent training resource to learner. Research was gone to apply item analysis method or to introduce web-based learning system using studying pattern. But, we need complicated algorithm or parameter setting etc, for apply these method. Therefore, in this research, we design and implement an item selection system in consideration of learner's incorrectness rate and problem frequency selection rate about question of item selection attribute. Also, as that embody business logic about item selection by EJB, efficient system development is available and we improved maintenance and reusability.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.320-326
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• 2016

In existing literature on multiple-input multiple-output (MIMO) relaying communication systems, antenna selection is often implemented by maximizing the channel capacity or the output single-to-noise ratio (SNR). In this paper, we propose an energy-efficient low-complexity antenna selection scheme for MIMO relaying communication systems. The proposed algorithm is based on beamforming and maximizing the Frobenius norm to jointly optimize the transmit power, number of active antennas, and antenna subsets at the source, relaying and destination. We maximize the energy efficiency between the link of source to relay and the link of relay to destination to obtain the maximum energy efficiency of the system, subject to the SNR constraint. Compared to existing antenna selection methods forMIMO relaying communication systems, simulation results demonstrate that the proposed method can save more power in term of energy efficiency, while having lower computational complexity.