• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.629-639
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• 2010

This paper shows that mathematics and statistics are not only a necessity but a strategic element in contributing for the competitiveness of field of S/W and H/W. First, a survey was conducted to investigate the perceived importance of mathematics and statistics to the specialists of the field. Second, computer programming was implemented in university mathematics courses to understand the educational effect related to problem solving ability. Third, a educational case in which mathematics and statistics in S/W and H/W subject were combined was analyzed. Lastly, an effective plan for mathematics and statistics education in the field of S/W and H/W will be presented.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.388-390
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• 2005

In this paper, a new rehabilitation training system was developed to improve equilibrium sense by combining virtual reality technology with a fixed exercise bicycle. The subjects consisted of two groups. A group of young people, was compared against a group of elderly. We measured three different running modes of virtual bicycle system with two successive sets. The parameters measured were running time, velocity, the weight movement, the degree of the deviation from the road, and the variables about the center of pressure. The repeated training, our results showed that the running capability of the elderly improve compared, In addition, it was found out that the ability of postural control and the equilibrium sense was improved with the presentation of the visual feedback information of the distribution of weight. From the results of this experiment, we showed that our newly developed system might be useful in the diagnosis of equilibrium sense or in the improvement of the sense of sight and, somatic, and vestibular sense of the elderly in the field of rehabilitation training.

• Smart Structures and Systems
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• v.26 no.1
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• pp.63-75
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• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• International Journal of Industrial Entomology and Biomaterials
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• v.4 no.2
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• pp.121-126
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• 2002

Breeding programme was initiated during November, 1995 with the main objective to breed productive multivoltine breeds/ hybrids with suitable genetic constitution to suit the fluctuating tropical climate prevailed in India. Two multivoltine breeds viz., BL-24 and BL-27 selected were as breeding resource materials from the silkworm germplasm maintained at Central Sericul-tural Research and Training Institute, Mysore. By adopting hybridization, backcrossing inbreeding and selection, a new multivoltine breed namely BL-67. This breed spins light greenish yellow cocoons and cocoon shape is oval with medium to coarse grains. The evolved breed was crossed with five tropical bivoltine breeds viz., NB4D2, CSR2, CSR5, CSR18 and CSR101 to study the combining ability, and identified a superior hybrid, BL67 ${\times}$ CSR101, named as CAUVERY, The hybrid is characterized by high pupation rate (>95%), high shell weight (> 35 cg), high cocoon shell ratio (> 20%), longer filament length (> 900 m) and high neatness (93) with a renditta of 6.5 producing 2A-3A grade silk. The hybrid is selected for Race Autho-rization test of Central Silk Board.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• Smart Structures and Systems
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• v.3 no.4
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• pp.423-437
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• 2007

The empirical mode decomposition (EMD) method is well-known for its ability to decompose a multi-component signal into a set of intrinsic mode functions (IMFs). The method uses a sifting process in which local extrema of a signal are identified and followed by a spline fitting approximation for decomposition. This method provides an effective and robust approach for decomposing nonlinear and non-stationary signals. On the other hand, the IMF components do not automatically guarantee a well-defined physical meaning hence it is necessary to validate the IMF components carefully prior to any further processing and interpretation. In this paper, an attempt to use the EMD method to identify properties of nonlinear elastic multi-degree-of-freedom structures is explored. It is first shown that the IMF components of the displacement and velocity responses of a nonlinear elastic structure are numerically close to the nonlinear normal mode (NNM) responses obtained from two-dimensional invariant manifolds. The IMF components can then be used in the context of the NNM method to estimate the properties of the nonlinear elastic structure. A two-degree-of-freedom shear-beam building model is used as an example to illustrate the proposed technique. Numerical results show that combining the EMD and the NNM method provides a possible means for obtaining nonlinear properties in a structure.

• Transactions of Materials Processing
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• v.21 no.8
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• pp.499-505
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• 2012

The fuel cell is a very promising power generation system combining the benefits of extremely low emissions, high efficiency, ease of maintenance and durability. In order to promote the commercialization of fuel cells, a flexible forming process, in which a hyper-elastic rubber is adopted as a medium to transmit forming pressure, is suggested as an efficient and cost effective manufacturing method for fuel cell bipolar plates. In this study, the ability of this flexible forming process to produce the micro channel arrays on metallic bipolar plates was first demonstrated experimentally. Then, a finite element (FE) model was built and validated through comparisons between simulated and experimental results. The effects of key process parameters on the forming performance such as applied load and punch velocity were investigated. As a result, appropriate process parameter values allowing high dimensional accuracy without failure were suggested.

• International Journal of Industrial Entomology and Biomaterials
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• v.5 no.1
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• pp.23-32
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• 2002

The introduction of hybrid and exploitation of heterosis has played a vital role in Indian sericulture industry, which clearly depicts a quantum jump in silk production during the last four decades. Since, the introduction of heterosis, progress in silkworm breeding has depended on success or failure in identifying better combiners. Systematic procedures developed have enabled the breeders to identify the best combiners by combining ability test, line ${\TIMES}$ tester analysis or $D^2$ analysis for maximum expression of heterosis. The level of heterosis expressed in the crossbreed population is determined by the interaction between genotype and prevailing environmental factors. Except some of the pre and post cocoon parameters, heterosis is invariably higher in single crosses compared to three-way and double crosses. However, during hot and humid season, when rearing of F1 bivoltine hybrid is unsuccessful at field level and indigenous races results in very low and poor quality yield, three-way and double crosses can play an important role as an intermediary technology. The objective of this article is to review briefly the concept and causes of heterosis, utilization of different forms of heterosis in silk production and its significance in silkworm, Bombyx mori breeding.

• Applied Microscopy
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• v.41 no.2
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• pp.89-98
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• 2011

Atom Probe Tomography (APT) can provide 3-dimensional information such as position and chemical composition with atomic resolution. Despite the ability of this technique, APT could not be applied for poor conductive materials such as semiconductor. Recently APT has dramatically developed by applying the laser pulsing and combining with Focused Ion Beam (FIB). The invention and combination of these techniques make possible site-specific sample preparation and permit the investigation of various materials including insulators. In this paper, we introduced the recently achieved state of the art applications of APT focusing on Si based FET devices, LED devices, low dimensional materials.