• Smart Structures and Systems
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• v.4 no.4
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• pp.465-492
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• 2008

Proper pretest planning is a vital component of any successful vibration test on engineering structures. The most important issue in dynamic testing of many engineering structures is arriving at the number and optimal placement of sensors. The sensors must be placed on the structure in such a way that all the important dynamic behaviour of a structural system is captured during the course of the test with sufficient accuracy so that the information can be effectively utilised for structural parameter identification or health monitoring. Several optimal sensor placement (OSP) techniques are proposed in the literature and each of these methods have been evaluated with respect to a specific problem encountered in various engineering disciplines like aerospace, civil, mechanical engineering, etc. In the present work, we propose to perform a detailed characteristic evaluation of some selective popular OSP techniques with respect to their application to practical civil engineering problems. Numerical experiments carried out in the paper on various practical civil engineering structures indicate that effective independence (EFI) method is more consistent when compared to all other sensor placement techniques.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.95-102
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• 2003

To define an analytical model for a structural system or to assess damage in the system, system identification(SI) methods have been developed and widely applied. The paper presents a method of determining optimal sensor location(OSL) based on the maximum likelihood approach, which is applicable to modal SI methods. To estimate unknown parameters reliably, it is necessary that the information provided by the experiment should be maximized. By applying the Cramer-Rao inequality, a Fisher information matrix in terms of the probability density function of measurements is obtained from a lower bound of the estimation error. The paper also proposes a scheme of determining of OSL on damaged structures by using maximum strain energy factor. Simulation studies have carried out to investigate the proposed OSL algorithm for both undamaged and damaged structures.

• Journal of Computing Science and Engineering
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• v.10 no.2
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• pp.51-57
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• 2016

In this paper, we research the influence of data preprocessing. We conclude that using different preprocessing methods leads to different classification performances. Moreover, not all data preprocessing methods are necessary, and a criterion is given to make sure which data preprocessing is necessary and which one is effective. Experiments on some real-world data sets validate that different data preprocessing methods result in different effects. Furthermore, experiments about some algorithms with different preprocessing methods also confirm that preprocessing has a great influence on the performance of a classifier.

• Journal of the Korean Statistical Society
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• v.33 no.3
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• pp.323-337
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• 2004

This article coined a general class of estimators for various measures in normal population when some' a priori' or guessed value of standard deviation a is available in addition to sample information. The class of estimators is primarily defined for a function of standard deviation. An unbiased estimator and the minimum mean squared error estimator are worked out and the suggested class of estimators is compared with these classical estimators. Numerical computations in terms of percent relative efficiency and absolute relative bias established the merits of the proposed class of estimators especially for small samples. Simulation study confirms the excellence of the proposed class of estimators. The beauty of this article lies in estimation of various measures like standard deviation, variance, Fisher information, precision of sample mean, process capability index $C_{p}$, fourth moment about mean, mean deviation about mean etc. as particular cases of the proposed class of estimators.

• Asia pacific journal of information systems
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• v.11 no.3
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• pp.127-147
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• 2001

This paper presents an analysis of Korean middle managers' decision-making processes. The sample included the observations of decisions made by 17 groups with 5 to 7 members each. The 5 hour-long, in average, decision processes were analysed by a modified system of Fisher's(1970) Interaction Process Analysis. The results showed that Korean managers followed alternative-focused decision processes, as opposite to value-focused ones. That is, the decision-making groups showed a strong trend to elaborate on alternative generation and evaluation right after the situation analysis. They tended to discuss the objectives of decision and relevant criteria only to resolve conflicts arisen during the evaluation process of alternatives. The analysis also showed that a decision proposition was more frequently followed by negative responses than positive ones and by interpretative evaluations than substantiative ones. The lessons from this study suggest a direction for the development of group decision support systems tailored for Korean cultural characteristics. This study is also meaningful as the first observation and empirical analysis of Korean middle managers' decision-making processes.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.59-74
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• 2004

The paper considers the Bayesian interval estimation for the common mean of several normal populations. A Bayesian procedure is proposed based on the idea of matching asymptotically the coverage probabilities of Bayesian credible intervals with their frequentist counterparts. Several frequentist procedures based on pivots and P-values are introduced and compared with Bayesian procedure through simulation study. Both simulation results demonstrate that the Bayesian procedure performs as well or better than any available frequentist procedure even from a frequentist perspective.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.81-88
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• 2017

The purpose of this study was to investigate the effects of mindfulness meditation programs based on stress management on the perceived stress, self-control, self-esteem and satisfaction with life scale in nursing college students. A quasi-experimental study with a non-equivalent control group pretest-posttest design was used. Participants consisted of an experimental group(n=18) and a control group(n=22). The experimental group received the mindfulness meditation program for 60 minutes a week for 13 sessions. Data were analyzed using descriptive statistics, ${\chi}^2-test$, Fisher's exact test, t-test, Mann-Whitney U test. The results showed there were significant decrease for perceived stress(t=-9.43, p=.025) and improved self-esteem(t=1.98, p=.038) in the experimental compared to group control group. These findings indicate that mindfulness meditation programs is effective to psychological well-being in nursing college students. Therefore this program needs to be considered as a nursing curriculum for students.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1554-1574
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• 2017

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.9-13
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• 2021

Type 2 diabetes mellitus (T2D) is a complex diabetes disease that is caused by high blood sugar, insulin resistance, and a relative lack of insulin. Many studies are trying to predict variant genes that causes this disease by using a sample disease model. In this paper we predict diabetic and normal persons by using fisher score feature selection, chi-2 feature selection and Logistic Regression supervised learning algorithm with best accuracy of 90.23%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.