• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• Parasites, Hosts and Diseases
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• v.41 no.4
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• pp.209-219
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• 2003

The evolutionary course of the CsRn1 long-terminal-repeat (LTR) retrotransposon was predicted by conducting a phylogenetic analysis with its paralog LTR sequences. Based on the clustering patterns in the phylogenetic tree, multiple CsRn1 copies could be grouped into four subsets, which were shown to have different integration times. Their differential sequence divergences and heterogeneous integration patterns strongly suggested that these subsets appeared sequentially in the genome of C. sinensis. Members of recently expanding subset showed the lowest level of divergence in their L TR and reverse transcriptase gene sequences. They were also shown to be highly polymorphic among individual genomes of the trematode. The CsRn1 element exhibited a preference for repetitive, agenic chromosomal regions in terms of selecting integration targets. Our results suggested that CsRn1 might induce a considerable degree of intergenomic variation and, thereby, have influenced the evolution of the C. sinensis genome.

• Earthquakes and Structures
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• v.16 no.2
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• pp.165-175
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• 2019

Incremental dynamic analysis (IDA), as an accurate method to evaluate the parameters of structural performance levels, requires many non-linear time history analyses, using a set of ground motion records which are scaled to different intensity levels. Therefore, this method is very computationally demanding. In this study, a new method is presented to estimate the summarized (16%, 50%, and 84% fractiles) IDA curves of a first-mode dominated structure using discrete wavelet transform and bees optimization algorithm. This method reduces the number of required ground motion records for the prediction of the summarized IDA curves. At first, a subset of first list ground motion records is decomposed by means of discrete wavelet transform which have a low dispersion estimating the summarized IDA curves of equivalent SDOF system of the main structure. Then, the bees algorithm optimizes a series of factors for each level of detail coefficients in discrete wavelet transform. The applied factors change the frequency content of original ground motion records which the generated ground motions records can be utilized to reliably estimate the summarized IDA curves of the main structure. At the end, to evaluate the efficiency of the proposed method, the seismic behavior of a typical 3-story special steel moment frame, subjected to a set of twenty ground motion records is compared with this method.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.304-310
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• 2021

The paper presents problems of optimization of the synthesis of embedded systems, in particular Pareto optimization. The model of such a system for its design for high-level of abstract is based on the classic approach known from the theory of task scheduling, but it is significantly extended, among others, by the characteristics of tasks and resources as well as additional criteria of optimal system in scope structure and operation. The metaheuristic algorithm operating according to this model introduces a new approach to system synthesis, in which parallelism of task scheduling and resources partition is applied. An algorithm based on a genetic approach with simulated annealing and Boltzmann tournaments, avoids local minima and generates optimized solutions. Such a synthesis is based on the implementation of task scheduling, resources identification and partition, allocation of tasks and resources and ultimately on the optimization of the designed system in accordance with the optimization criteria regarding cost of implementation, execution speed of processes and energy consumption by the system during operation. This paper presents examples and results for multi-criteria optimization, based on calculations for specifying non-dominated solutions and indicating a subset of Pareto solutions in the space of all solutions.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.167-174
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• 2022

In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1527-1532
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• 2023

The noise reduction algorithm using the non-local means (NLM) approach is very efficient in nuclear medicine imaging. In this study, the applicability of the NLM noise reduction algorithm in single-photon emission computed tomography (SPECT) images with a brain phantom and the optimization of the NLM algorithm by changing the smoothing factors according to various reconstruction methods are investigated. Brain phantom images were reconstructed using filtered back projection (FBP) and ordered subset expectation maximization (OSEM). The smoothing factor of the NLM noise reduction algorithm determined the optimal coefficient of variation (COV) and contrast-to-noise ratio (CNR) results at a value of 0.020 in the FBP and OSEM reconstruction methods. We confirmed that the FBP- and OSEM-based SPECT images using the algorithm applied with the optimal smoothing factor improved the COV and CNR by 66.94% and 8.00% on average, respectively, compared to those of the original image. In conclusion, an optimized smoothing factor was derived from the NLM approach-based algorithm in brain SPECT images and may be applicable to various nuclear medicine imaging techniques in the future.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.3
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• pp.161-168
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• 2002

A secret sharing scheme is a cryptographic protocol to share a secret among a set of participants P in the way that only qualified subsets of P can reconstruct the secret whereas any other subset of P, non-qualified to know the secret, cannot determine anything about the secret. In this paper, we propose a new secret sharing scheme in hierarchical groups, whose hierarchy can be represented as a tree structure. In the tree structure, participants of higher levels have priorities to reconstruct the secret over participants of lower levels. In the absence of the participant of a higher level, it is possible for this participant to delegate the ability to reconstruct the secret to the child nodes of the next lower level through the transfer of his delegation ticket. This scheme has a dynamic access structure through the recursive delegation process from the root to lower levels where participants aren't absent.

• Journal of Korean Biological Nursing Science
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• v.20 no.3
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• pp.187-195
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• 2018

Purpose: The purpose of this study was to identify associations among the sleep disturbance, fatigue, job stress, and blood and body fluid (BBF) exposure of shift-work nurses. Methods: A total of 299 shift-work nurses from two tertiary hospitals were enrolled in this study. We used the Insomnia Severity Index (ISI), and the Fatigue Severity Scale (FSS), and the Korean Occupational Stress Scale-Short Form (KOSS-SF) to evaluate sleep disturbance, fatigue, and job stress, respectively. The data were analyzed using t-test or chi-squared test and Logistic regression analysis using the SPSS 23.0 program. Results: We found that 43.8% of participants reported BBF exposure over the past year. Splash or exposure to broken skin of BBF were most frequent (56.9%), and followed by needlestick injuries (30.4%) and sharp injuries (12.8%). Age, hospital, working period, level of stress, sleep disturbance ($ISI{\geq}15$), fatigue (FSS $score{\geq}4$), job demand and organizational climate subset in KOSS-SF were significantly associated with BBF exposure in shift-work nurses. In multivariate analysis after adjusting age and hospital, the risk factors of BBF exposure in shift-work nurses were the level of stress and fatigue (FSS $score{\geq}4$). Conclusion: Fatigue and job stress were related to BBF exposure in shift-work nurses. Our results suggest that management of sleep disturbance, fatigue, and high job stress in shift-work nurses is needed to reduce risk of BBF exposure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.33-38
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• 2016

In Android platform, as applications invoke various service functions through IPC (Inter-Process Communication), IPC performance is critical to the responsiveness in Android. However, Android offers long IPC latency of hundreds of micro-seconds due to complicated software stacks between the kernel Binder and the user-level process Context Manager. This separation provides modularity and flexibility, but degrades the responsiveness of services owing to additional context switching and inefficient request handling. In this paper, we anatomize Android IPC mechanisms and observe that 55% of IPC latency comes from the communication overhead between Binder and Context Manager. Based on this observation, this paper proposes a kernel Binder cache that retains a popular subset of service function mappings, thereby reducing the requests transferred to the user-level daemon. The proposed Binder cache is implemented in Android 5.0 and experimental results with various benchmarks show that the proposed cache architecture improves performance by 52.9% on average.

• Journal of the Korean Mathematical Society
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• v.46 no.2
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• pp.363-447
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• 2009

The author previously defined the spectral invariants, denoted by $\rho(H;\;a)$, of a Hamiltonian function H as the mini-max value of the action functional ${\cal{A}}_H$ over the Novikov Floer cycles in the Floer homology class dual to the quantum cohomology class a. The spectrality axiom of the invariant $\rho(H;\;a)$ states that the mini-max value is a critical value of the action functional ${\cal{A}}_H$. The main purpose of the present paper is to prove this axiom for nondegenerate Hamiltonian functions in irrational symplectic manifolds (M, $\omega$). We also prove that the spectral invariant function ${\rho}_a$ : $H\;{\mapsto}\;\rho(H;\;a)$ can be pushed down to a continuous function defined on the universal (${\acute{e}}tale$) covering space $\widetilde{HAM}$(M, $\omega$) of the group Ham((M, $\omega$) of Hamiltonian diffeomorphisms on general (M, $\omega$). For a certain generic homotopy, which we call a Cerf homotopy ${\cal{H}}\;=\;\{H^s\}_{0{\leq}s{\leq}1}$ of Hamiltonians, the function ${\rho}_a\;{\circ}\;{\cal{H}}$ : $s\;{\mapsto}\;{\rho}(H^s;\;a)$ is piecewise smooth away from a countable subset of [0, 1] for each non-zero quantum cohomology class a. The proof of this nondegenerate spectrality relies on several new ingredients in the chain level Floer theory, which have their own independent interest: a structure theorem on the Cerf bifurcation diagram of the critical values of the action functionals associated to a generic one-parameter family of Hamiltonian functions, a general structure theorem and the handle sliding lemma of Novikov Floer cycles over such a family and a family version of new transversality statements involving the Floer chain map, and many others. We call this chain level Floer theory as a whole the Floer mini-max theory.