• International Journal of Industrial Entomology and Biomaterials
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• 제5권1호
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• pp.37-43
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• 2002

Eighteen new bivoltine silkworm (Bombyx mori L.) hybrids developed at Andhra Pradesh State Sericul-ture Research and Development Institute, Hindupur are evaluated for 10 economic traits by following two multiple trait index methods, i.e., Subordinate Function and Evaluation Index for their economic merit. The hybrid genotype, APS6${\times}$APS11 with highest Subordinate function value of 8.2432 and highest average Evaluation Index of 61.67 ranked first. This hybrid is adjudicated as most promising hybrid and recommended for commercial use. Further, applicability of Subordinate Function Index Method is tested and recommended for application of multiple trait evaluation similar to Evaluation Index Method as the results obtained are comparable. Further, both these methods can be applied for confirmation of results.

• Communications for Statistical Applications and Methods
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• 제27권4호
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• pp.469-486
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• 2020

Entropy is an important term in statistical mechanics that was originally defined in the second law of thermodynamics. In this paper, we consider the maximum likelihood estimation (MLE), maximum product spacings estimation (MPSE) and Bayesian estimation of the entropy of an inverse Weibull distribution (InW) under a generalized type I progressive hybrid censoring scheme (GePH). The MLE and MPSE of the entropy cannot be obtained in closed form; therefore, we propose using the Newton-Raphson algorithm to solve it. Further, the Bayesian estimators for the entropy of InW based on squared error loss function (SqL), precautionary loss function (PrL), general entropy loss function (GeL) and linex loss function (LiL) are derived. In addition, we derive the Lindley's approximate method (LiA) of the Bayesian estimates. Monte Carlo simulations are conducted to compare the results among MLE, MPSE, and Bayesian estimators. A real data set based on the GePH is also analyzed for illustrative purposes.

• Nuclear Engineering and Technology
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• 제48권3호
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• pp.711-718
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• 2016

Steady-state operation of a fusion power plant requires external current drive to minimize the power requirements, and a high fraction of bootstrap current is required. One of the external sources for current drive is lower hybrid current drive, which has been widely applied in many tokamaks. Here, using lower hybrid simulation code, we calculate electron distribution function, electron currents and phase velocity changes for two options of demonstration reactor at the launched lower hybrid wave frequency 5 GHz. Two plasma scenarios pertaining to two different demonstration reactor options, known as pulsed (Option 1) and steady-state (Option 2) models, have been analyzed. We perceive that electron currents have major peaks near the edge of plasma for both options but with higher efficiency for Option 1, although we have access to wider, more peripheral regions for Option 2. Regarding the electron distribution function, major perturbations are at positive velocities for both options for flux surface 16 and at negative velocities for both options for flux surface 64.

• Smart Structures and Systems
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• 제16권3호
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• pp.401-414
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• 2015

A developed hybrid method for crack identification of beams is presented. Based on the Euler-Bernouli beam theory and concepts of fracture mechanics, governing equation of the cracked beams is reformulated. Finite element (FE) method as a powerful numerical tool is used to discritize the equation in space domain. After transferring the equations from time domain to frequency domain, frequencies and mode shapes of the beam are obtained. Efficiency of the governed equation for free vibration analysis of the beams is shown by comparing the results with those available in literature and via ANSYS software. The used equation yields to move the influence of cracks from the stiffness matrix to the mass matrix. For crack identification measured data are produced by applying random error to the calculated frequencies and mode shapes. An objective function is prepared as root mean square error between measured and calculated data. To minimize the function, hybrid genetic algorithms (GAs) and particle swarm optimization (PSO) technique is introduced. Efficiency, Robustness, applicability and usefulness of the mixed optimization numerical tool in conjunction with the finite element method for identification of cracks locations and depths are shown via solving different examples.

• Journal of applied mathematics & informatics
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• 제12권1_2호
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• pp.199-209
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• 2003

When a sampling theorem holds in wavelet subspaces, sampling expansions can be a good approximation to projection expansions. Even when the sampling theorem does not hold, the scaling function series with the usual coefficients replaced by sampled function values may also be a good approximation to the projection. We refer to such series as hybrid sampling series. For this series, we shall investigate the local convergence and analyze Gibbs phenomenon.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.838-841
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• 1993

This paper describes a fuzzy network circuit of analogue and digital mixed operation. The circuits are suggested for membership function, MIN function and normalization function using either linear voltage-controlled MOSFET resistance or pulse stream operation. The analogue-digital hybrid fuzzy hardware is extensible to the fuzzy-neural network as its basic configurations are already used in URAN-I of 135,424 synaptic connections.

• 한국CDE학회논문집
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• 제11권2호
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• pp.77-87
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• 2006

It is necessary to change the existing design process to cope with a short life-cycle product and various customer's demands. Also a frequent design change may delay the whole design process and it will increase the unit cost of the production. New alternatives or techniques have emerged to solve the existing design problems, such as a knowledge based engineering, an intelligent CAD, a function based design, and so on. In this paper, we propose a hybrid design system with a knowledge based design methodology and a function based design technique. The knowledge based design is good at a frequent design change and the function based design is effective to extract a core design behavior. In an early design process, the system utilizes a core design behavior through the function based design process. On the other hand, the system manages complicated design issues with the knowledge based design technique in the detailed design process. We conclude that the hybrid design system can bring fair effects on implementing an efficient design environment in aspect of time and expense.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• 대한안전경영과학회지
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• 제4권2호
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• pp.11-22
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• 2002

In this study, an asymmetric lifting posture prediction model was developed, which was a three-dimensional model with 12 links and 23 degrees of freedom open kinematic chains. Although previous researchers have proposed biomechanical, psychophysical, or physiological measures as cost functions, for solving redundancy, they lack in accuracy in predicting actual lifting postures and most of them are confined to the two-dimensional model. To develop an asymmetric lifting posture prediction model, we used the resolved motion method for accurately simulating the lifting motion in a reasonable time. Furthermore, in solving the redundant problem of the human posture prediction, a moment weighted Joint Range Availability (JRA) was used as a cost function in order to consider dynamic lifting. However, it is known that the moment weighted JRA as a cost function predicted the lower extremity and L5/S1 joint motions better than the upper extremities, while the constant weighted JRA as a cost function predicted the latter better than the former. To compensate for this, we proposed a hybrid moment weighted JRA as a new cost function with moment weighted for only the lower extremity. In order to validate the proposed cost function, the predicted and real lifting postures for various lifting conditions were compared by using the root mean square(RMS) error. This hybrid JRA reduced RMS more than the previous cost functions. Therefore, it is concluded that the cost function of a hybrid moment weighted JRA can be used to predict three-dimensional lifting postures. To compare with the predicted trajectories and the real lifting movements, graphical validations were performed. The results also showed that the hybrid moment weighted cost function model was found to have generated the postures more similar to the real movements.

• 한국컴퓨터정보학회논문지
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• 제17권12호
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• pp.149-158
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• 2012

본 논문에서는 점근적으로 최적인 두가지의 무작위화 함수인 일라이어스(Elias) 함수와 페레즈(Peres) 함수의 장단점을 고려한 하이브리드 무작위화 함수를 제안한다. 무작위화 함수는 편향성이 있는 무작위수의 공급원으로부터 균등한 무작위수를 생성하는데 쓰이는 알고리즘을 수학적으로 추상화한 것이다. 일라이어스 함수와 페레즈 함수는 입력의 길이가 무한으로 증가함에 따라 그 출력효율성이 정보론적 한계치에 다가간다. 특히, 일라이어스 함수는 주어진 (유한의) 입력길이에 대해 최적인 무작위화 함수이다. 그러나 그 계산은 간단하지 않고, 주어진 입력길이에 의존한다. 반면, 페레즈 함수는 정해진 입력의 길이에 대해 출력효율이 최적이지는 않으나, 점근적으로는 최적이고, 간단한 재귀식에 의해 정의되어서 그 계산이 매우 간단하고 적은 메모리를 필요로 한다. 이러한 계산복잡도와 출력효율에 대한 두가지 무작위화 함수의 장단점에 주목하여, 각각의 장점을 고려한 하이브리드 무작위화 함수를 제안하고 이를 분석한다.