• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.421-424
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• 2003

This paper proposes the generalized predictive control(GPC) method of chaotic systems using a self-recurrent wavelet neural network(SRWNN). The reposed SRWNN, a modified model of a wavelet neural network(WNN), has the attractive ability such as dynamic attractor, information storage for later use. Unlike a WNN, since the SRWNN has the mother wavelet layer which is composed of self-feedback neurons, mother wavelet nodes of the SRWNN can store the past information of the network. Thus the SRWNN can be used as a good tool for predicting the dynamic property of nonlinear dynamic systems. In our method, the gradient-descent(GD) method is used to train the SRWNN structure. Finally, the effectiveness and feasibility of the SRWNN based GPC is demonstrated with applications to a chaotic system.

• Journal of Gastric Cancer
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• v.21 no.3
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• pp.246-257
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• 2021

Purpose: It is unclear whether high-definition (HD) imaging improves visibility and diagnostic ability in early gastric cancer (EGC) compared with standard-definition (SD) imaging. We aimed to compare the diagnostic performance and visibility scores of HD and SD ultraslim endoscopes in EGC. Materials and Methods: We used HD and SD ultraslim endoscopes to obtain 60 images with similar compositions of gastric environments. Of the 60 images, 30 showed EGC (15 images for each modality) and 30 showed no EGC (15 images for each modality). Seventeen endoscopists evaluated the presence and location of the lesions in each image. Diagnostic ability was compared between modalities. The color difference between a lesion and the surrounding mucosa (ΔE) was measured and compared between the modalities. Results: The ability of HD to detect EGC was significantly higher than that of SD (accuracy: 80.8% vs. 71.6%, P=0.017; sensitivity: 94.9% vs. 76.5%, P<0.001; positive predictive value, 76.2% vs. 55.3%, P<0.001; and negative predictive value (NPV), 94.1% vs. 73.5%, P<0.001). The ability of HD to determine the horizontal extent of EGC was significantly higher than that of SD (accuracy: 71.0% vs. 57.8%, P=0.004; sensitivity: 75.3% vs. 49.0%, P<0.001; NPV, 72.9% vs. 55.9%, P<0.001; and area under the curve: 0.891 vs. 0.631, P=0.038). The mean ΔE was significantly higher for HD than for SD (10.3 vs. 5.9, P=0.011). Conclusions: The HD ultraslim endoscope showed a higher diagnostic performance in EGC than the SD endoscope because it provided good color contrast.

• Clinical and Experimental Reproductive Medicine
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• v.20 no.1
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• pp.1-7
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• 1993

The present study was designed to test the validity of the semen analysis(S/A) and the sperm penetration assay(SPA) as a prognostic indicator of male fertility in 123 patients undergoing in vitro fertilization(IVF). We attempted to correlate the traditional semen parameters or the extent of sperm penetration in SPA with the results of human IVF rate or cleavage rate. Poor correlation was found between the results of S/A and human IVF rate(sensitivity, 80.6% ;specificity, 46.7%; positive predictive value, 91.6%;negative predictive value, 25%). Conversely, good correlation was found between the results of SPA and human IVF rate(sensitivity, 100% ; specificity, 80% ;positive predictive value, 97.3% ;negative predictive value, 100%). Our results corroborate the conclusion that SPA can be a valuable tool as a prognostic indicator of male fertilizing ability.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.203-208
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• 2020

This study examined whether Mild Cognitive Impairment (MCI) is related to the reduction of specific memory among linguistic memory and visuospatial memory, and to identify the most predictive index for discriminating MCI from normal elderly. The subjects were analyzed for 189 elderly (103 healthy elderly, 86 MCI). The verbal memory was used by the Seoul Verbal Learning Test. visuospatial memory was measured using the Rey Complex Figure Test. As a result of multiple logistic regression, verbal memory and visuospatial memory showed significant predictive performance in discriminating MCI from normal elderly. On the other hand, when all the confounding variables were corrected, including the results of each memory test, the predictive power was significant in distinguishing MCI from normal aging only in the immediate recall of verbal memory, and the predictive power was not significant in the immediate recall of visuospatial memory. This result suggests that delayed recall of visuospatial memory and immediate recall of verbal memory are the best combinations to discriminate memory ability of MCI.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.12-18
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• 2001

In this paper, we propose the Recurrent Neural Predictive HMM (RNPHMM). The RNPHMM is the hybrid network of the recurrent neural network and HMM. The predictive recurrent neural network trained to predict the future vector based on several last feature vectors, and defined every state of HMM. This method uses the prediction value from the predictive recurrent neural network, which is dynamically changing due to the effects of the previous feature vectors instead of the stable average vectors. The models of the RNPHMM are Elman network prediction HMM and Jordan network prediction HMM. In the experiment, we compared the recognition abilities of the RNPHMM as we increased the state number, prediction order, and number of hidden nodes for the isolated digits. As a result of the experiments, Elman network prediction HMM and Jordan network prediction HMM have good recognition ability as 98.5% for test data, respectively.

• Journal of The Korean Association For Science Education
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• v.21 no.3
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• pp.497-505
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• 2001

In this study, the relationships among students' conceptual understanding of molecular structure and cognitive variables were investigated for 165 high school students. After they had learned 'High School Chemistry II' for two semesters, the tests of conception concerning molecular structure, spatial visualization ability, logical thinking ability, mental capacity, and learning approach were administered. The results indicated that students' conceptual understanding of molecular structure was not sound, and several misconceptions were found. The scores of the conception test were significantly correlated with all the cognitive variables studied. Multiple regression analyses were conducted to examine the predictive influences of students' cognitive variables on their conceptual understanding. Meaningful learning approach was the most significant predictor and were followed by logical thinking ability, rote learning approach, and mental capacity. However, spatial visualization ability did not have the predictive power.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.597-606
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• 2020

Recently, as students' programming classes are being conducted online, interest in factors that can lead to the success of online programming classes is also increasing. Therefore, in this study, online programming classes were conducted for specialized high school students using a web-based simulation programming tool through TinkerCad. In these online programming classes, students' self-regulation ability and learning flow were set as variables that influence both learning engagement and teaching presence, and the predictive power of each was analyzed. As a result, it was found that both self-regulation ability and learning flow were predictive variables for learning engagement and teaching presence, and that learning flow played a mediating role between self-regulation ability, learning engagement, and teaching presence. This study is meaningful in that it suggested that self-regulation ability and learning flow should be considered more meaningfully in online programming classes, and a practical strategy for this is presented.

• Journal of The Korean Association For Science Education
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• v.19 no.3
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• pp.471-478
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• 1999

In this study, the relationships among learners' cognitive variables, motivational variables, and conceptual understandings in learning with analogy were investigated. The instruments regarding analogical reasoning ability, field dependence-independence, mental capacity, and logical thinking ability were administered. Some subtests (self-efficacy, expectancy, self-concept of ability, and value) of the Patterns of Adaptive Learning Survey were administered. After students learned with a worksheet that included analogy, a conception test regarding 'stoichiometry that included limiting reagent' was also administered. It was found that learners' conceptual understandings were significantly correlated with the logical thinking ability and the field dependence-independence among the cognitive variables, and the self-efficacy and the self-concept of ability among the motivational variables. The multiple regression analysis of the cognitive variables on conceptual understandings revealed that the logical thinking ability was the most significant predictor. The field dependence-independence also had predictive power. In the analysis of the motivational variables, the self concept of ability was the only significant predictor.

• Journal of Integrative Natural Science
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• v.9 no.2
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• pp.128-135
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• 2016

5-hydroxytryptamine (serotonin) receptor ($5-HT_7R$) 7 is one of G-Protein coupled receptors, which is activated by the neurotransmitter Serotonin. After activation by serotonin, $5-HT_7$ activates the production of the intracellular signaling molecule cyclic AMP. $5-HT_7$ receptor has been found to be involved in the pathophysiology of various disorders. It is reported that $5-HT_7$ receptor antagonists can be used as antidepressant agents. In this study, we report the important structural and chemical parameters for 2-methoxyphenylpiperazinylakanamides as $5-HT_7R$ inhibitors. A 3D QSAR study based on comparative molecular field analysis (CoMFA) was performed. The best predictions were obtained for the best CoMFA model with $q^2$ of 0.594 with 6 components, $r^2$ of 0.986, Fisher value as 60.607, and an estimated standard error of 0.043. The predictive ability of the test set was 0.602. Results obtained the CoMFA models suggest that the data are well fitted and have high predictive ability. The contour maps are generated and studied. The contour analyses may serve as tool in the future for designing of novel and more potent $5-HT_7R$ derivatives.