• 한국경영과학회지
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• 제22권1호
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• pp.141-157
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• 1997

The structure and dimensionality of the User Information Satisfaction (UIS) construct is an important theoretical issue that received considerable attentions. The acceptance of UIS as a standardized instrument requires confirmation that it explains and measures the user information satisfaction construct and its component. Based on a simple of 670 respondents who participated in dealing with the Public Management Information System (PMIS), this research used a confirmatory factor analysis to test the alternavtive models of underlying factor structure and assessed the reliability and validity of these factors and items in the PMIS. The result provided a support for a revised UIS model with four first-order factors and one PMIS The result provided a support for a revised UIS model with four first-order factors and one second-order (higher-order) factor in PMIS. To cross-validata these results, the author reexamined two prior data sets. The results showed that the revised model provides better model-data fit in all three data sets.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• Environmental Engineering Research
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• 제22권3호
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.579-586
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• 2009

통계모형의 상호작용 효과를 분석하기위해 비모수적인 방법인 다중인자 차원 축소(MDR)방법을 사용해 왔다. MDR 방법은 사례-대조 데이터에만 적용 할 수 있다. 본 논문에서는 Regression tree 알고리즘과 더미 변수를 활용한 회귀분석 알고리즘을 사용하여 다중 범주를 High 범주와 Low범주로 분류함으로써, MDR방법에서 연속형 데이터에 적용 할 수 없는 문제를 해결하는 방법으로 제시된 Expanded MDR방법과 Dummy MDR방법을 한우의 주요 경제형질(longissimus muscle dorsi area: LMA, carcass cold weight: CWT, average daily gain: ADG)데이터에 적용하여 한우의 경제형질에 영향을 주는 주요 SNPs 마커를 규명하고, Permutation test를 통해 그 결과를 비교한다.

• Wind and Structures
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• 제7권1호
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• pp.13-28
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• 2004

A method of numerical analysis without conducting 3D wind tunnel model tests was examined in our previous study for predicting vortex-induced oscillation of bridge girders with span-wise varying geometry. The aerodynamic damping forces measured for plural wind tunnel 2D models were used in the analysis. A further study was conducted to examine the precision of solution obtained by this method. First, the responses of vortex-induced oscillation of two rocking models and a taut-strip bridge girder model with span-wise varying geometry were measured. Next, the responses of these models were numerically analyzed by means of this method, and then a comparison was made between the obtained $Vr-A-{\delta}_a$ contour diagram of each 3D model in the wind tunnel test and the diagram in the numerical analysis. Since close correlations were observed between each two $Vr-A-{\delta}_a$diagrams obtained in the model test and in the analysis in cases where the 3D model did not have strong three-dimensionality, our findings revealed that the predicted solution proved to be reasonably accurate.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1527-1534
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• 2016

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.891-894
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• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• 대한기계학회논문집
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• 제19권9호
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• pp.2283-2296
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• 1995

Experiments of normal shock wave/turbulent boundary layer interaction were conducted in a supersonic diffuser. The flow Mach number just upstream of the normal shock wave was in the range of 1.10 to 1.70 and Reynolds number based upon the turbulent boundary layer thickness was varied in the range of 2.2*10$^{[-994]}$ -4.4*10$^{[-994]}$ . The wall pressures in streamwise and spanwise directions were measured for two test cases, in which the turbulent boundary layer thickness incoming into the supersonic diffuser was changed. The results show that the interactions of normal shock wave with turbulent boundary layer in the supersonic diffuser can be divided into three patterns, i.e., transonic interaction, weak interaction and strong interaction, depending on Mach number. The weak interactions generate the post-shock expansion which its strength is strong as the Mach number increases and the strong interactions form the pseudo-shock waves. From the spanwise measurements of wall pressure, it is known that if the flow Mach number is low, the interacting flow fields essentially appear two-dimensional, but they have an apparent 3-dimensionality for the higher Mach numbers.

• Asian-Australasian Journal of Animal Sciences
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• 제27권6호
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• pp.784-790
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• 2014

In this study we aimed to evaluate the reduction in dimensionality of 20 linear type traits and more final score in 14,943 Holstein cows in Brazil using factor analysis, and indicate their relationship with longevity and 305 d first lactation milk production. Low partial correlations (-0.19 to 0.38), the medium to high Kaiser sampling mean (0.79) and the significance of the Bartlett sphericity test (p<0.001), indicated correlations between type traits and the suitability of these data for a factor analysis, after the elimination of seven traits. Two factors had autovalues greater than one. The first included width and height of posterior udder, udder texture, udder cleft, loin strength, bone quality and final score. The second included stature, top line, chest width, body depth, fore udder attachment, angularity and final score. The linear regression of the factors on several measures of longevity and 305 d milk production showed that selection considering only the first factor should lead to improvements in longevity and 305 milk production.