• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Clinical and Experimental Reproductive Medicine
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• v.46 no.1
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• pp.14-21
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• 2019

Objective: The usual seminal profile has been customarily used for diagnosing male infertility based on an examination of semen samples. However, sperm DNA fragmentation has also been causally linked to reproductive failure, suggesting that it should be evaluated as part of male infertility assessments. To compare the ability of the five most widely utilized methodologies of measuring DNA fragmentation to predict male infertility and reactive oxygen species by Oxisperm kit assay. Methods: In this case-control study, which received ethical committee approval, the participants were divided into fertile and infertile groups (50 patients in each group). Results: The alkaline comet test showed the best ability to predict male infertility, followed by the terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) assay, the sperm chromatin dispersion (SCD) test, and the sperm chromatin structure assay (SCSA), while the neutral comet test had no predictive power. For our patient population, the projected cut-off point for the DNA fragmentation index was 22.08% using the TUNEL assay, 19.90% using SCSA, 24.74% using the SCD test, 48.47% using the alkaline comet test, and 36.37% using the neutral comet test. Significant correlations were found between the results of the SCD test and those obtained using SCSA and TUNEL (r = 0.70 and r = 0.68, respectively; p< 0.001), and a statistically significant correlation was also found between the results of SCSA and the TUNEL assay (r = 0.77, p< 0.001). Likewise, the results of the alkaline comet test showed significant correlations with those of the SCD, SCSA, and TUNEL tests (r = 0.59, r = 0.57, and r = 0.72, respectively; p< 0.001). Conclusion: The TUNEL assay, SCSA, SCD, and the alkaline comet test were effective for distinguishing between fertile and infertile patients, and the alkaline comet test was the best predictor of male infertility.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.205-219
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• 2021

The present study identified determinants to user acceptance of artificial intelligence(AI)-based news anchor. Our conceptual model included three constructs of ability, benevolence, and integrity to determine whether these three constructs are predictive of trust perceived from AI news anchor. This work further examined the influences of social presence, anthropomorphism, perceived usefulness, understanding as well as trust as immediate determinants to user acceptance. The conceptual model was validated on survey data collected from 513 respondents. A series of scale refinement process was conducted by the examination of data normality, common method bias, structure of latent variables as well as internal consistency. In addition, a confirmatory factor analysis was performed to assess the extent to which the sample data collected from survey study measures the constructs adequately. The results from the analysis of structural equation model indicated that, (1) two constructs of ability and integrity were found to be significantly predictive of perceived trust, and (2) anthropomorphism, perceived usefulness, and trust emerged as significant and positive predictors of user acceptance of AI-based news anchor.

• Journal of Microbiology and Biotechnology
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• v.34 no.5
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• pp.1164-1177
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• 2024

Esophageal squamous cell carcinoma (ESCC) is among the most common malignant tumors of the digestive tract, with the sixth highest fatality rate worldwide. The ESCC-related dataset, GSE20347, was downloaded from the Gene Expression Omnibus (GEO) database, and weighted gene co-expression network analysis was performed to identify genes that are highly correlated with ESCC. A total of 91 transcriptome expression profiles and their corresponding clinical information were obtained from The Cancer Genome Atlas database. A mitochondria-associated risk (MAR) model was constructed using the least absolute shrinkage and selection operator Cox regression analysis and validated using GSE161533. The tumor microenvironment and drug sensitivity were explored using the MAR model. Finally, in vitro experiments were performed to analyze the effects of hub genes on the proliferation and invasion abilities of ESCC cells. To confirm the predictive ability of the MAR model, we constructed a prognostic model and assessed its predictive accuracy. The MAR model revealed substantial differences in immune infiltration and tumor microenvironment characteristics between high- and low-risk populations and a substantial correlation between the risk scores and some common immunological checkpoints. AZD1332 and AZD7762 were more effective for patients in the low-risk group, whereas Entinostat, Nilotinib, Ruxolutinib, and Wnt.c59 were more effective for patients in the high-risk group. Knockdown of TYMS significantly inhibited the proliferation and invasive ability of ESCC cells in vitro. Overall, our MAR model provides stable and reliable results and may be used as a prognostic biomarker for personalized treatment of patients with ESCC.

• Journal of Korean Library and Information Science Society
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• v.35 no.1
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• pp.303-317
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• 2004

The purpose of this study was to develop a framework for predicting searching performance through an understanding of how cognitive ability relates to searching process and outcome. Specifically, this study examined the relationship between spatial visualization, logical reasoning, integrative reasoning, and information searching process and outcome. Information searching process was assessed by seven search process indicators: (1) search command selection: (2) combination of search commands; (3) application of Boolean logic: (4) application of truncation; (5) use of limit search function; (6) number of search statements; and (7) number of search errors made. Searching outcome was assessed by the number of correct answers to search questions. Subjects first took three standardized cognitive tests that measured cognitive abilities, and performed online catalog searching in response to seven information search questions. The searches were logged using Lotus ScreenCam, and reviewed for the analysis. Factor analysis was used to find underlying structures of the seven search process variables. Multiple regression analysis was applied to examine the predictive power of three cognitive variables on three extracted factors, and search outcome. Results of the data analysis showed that individual differences in logical reasoning could predict information searching process and outcome.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2241-2258
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• 2023

This study developed digital literacy instruments and tested their effectiveness on college students' perceptions of AI technologies. In creating a new digital literacy test tool, we reviewed the concept and scale of digital literacy based on previous studies that identified the characteristics and measurement of AI literacy. We developed 23 preliminary questions for our research instrument and used a quantitative approach to survey 318 undergraduates. After conducting exploratory and confirmatory factor analysis, we found that digital literacy in the age of AI had four ability sub-factors: critical understanding, artificial intelligence social impact recognition, artificial intelligence technology utilization, and ethical behavior. Then we tested the sub-factors' predictive powers on the perception of AI's usefulness and ease of use. The regression result shows that the most common powerful predictor of the usefulness and ease of use of AI technology was the ability to use AI technology. This finding implies that for college students, the ability to use various tools based on AI technology is an essential competency in the AI era.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.392-395
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• 2003

In the maritime container terminal, LMTT (Linear Motor-based Transfer Technology) is horizontal transfer system for the yard automation, which has been proposed to take the place of AGV (Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc., LMCPS (Linear Motor Conveyance Positioning System) is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the soft-computing method of a multi-step prediction control for LMCPS using DR-FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi-step prediction. Consequently, the system has an ability to adapt for external disturbance, cogging force, force ripple, and sudden changes of itself.

• Transactions on Electrical and Electronic Materials
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• v.18 no.2
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• pp.103-110
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• 2017

The number of applications of solar photovoltaic (PV) systems in power generation grids has increased in the last decade because of their ability to generate efficient and reliable power in a variety of low installation in domestic applications. Various PV converter topologies have therefore emerged, among which the modular multilevel converter (MMC) is very attractive due to its modularity and transformerless features. The modeling and control of the MMC has become an interesting issue due to the extremely large expansion of PV power plants at the residential scale and due to the power quality requirement of this application. This paper proposes a novel control method of MMC which is used to directly integrate the photovoltaic arrays with the power grid. Traditionally, a closed loop control has been used, although circulating current control and capacitors voltage balancing in each individual leg have remained unsolved problem. In this paper, the integration of model predictive control (MPC) and traditional closed loop control is proposed to control the MMC structure in a PV grid tied mode. Simulation results demonstrate the efficiency and effectiveness of the proposed control model.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.99-108
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• 2004

In this paper, a new fast detection and clutter rejection method is proposed for CCD-image-based Automatic Target Detection System. For defence application, fast computation is a critical point, thus we concentrated on the ability to detect various targets with simple computation. In training stage, 1D template set is generated by regional vertical projection and K-means clustering, and binary tree structure is adopted to reduce the number of template matching in test stage. We also use adaptive skip-width by Correlation-based Adaptive Predictive Search(CAPS) to further improve the detecting speed. In clutter rejection stage, we obtain Fourier Descriptor coefficients from boundary information, which are useful to rejected clutters.

• Journal of Integrative Natural Science
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• v.11 no.3
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• pp.161-164
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• 2018

Over the past decade, mass spectrometry-based metabolomics, especially two dimensional gas chromatography mass spectrometry (GCxGC/TOF-MS), has become a key analytical tool for metabolomics data because of its sensitivity and ability to analyze complex biological or biochemical sample. However, the need to reduce variations within/between experiments has been reported and methodological developments to overcome such problem has long been a critical issue. Along with methodological developments, developing reasonable performance measure has also been studied. Following four numerical measures have been typically used for comparison: sensitivity, specificity, receiver operating characteristic (ROC) curves, and positive predictive value (PPV). However, more recently, such measures are replaced with F1 score in many fields including metabolomics area without any carefulness of its validity. Thus, we want to investigate the validity of F1 score on two examples, with the goal of raising the awareness in choosing appropriate performance comparison measure. We noticed that F1 score itself, as a performance measure, was not good enough. Accordingly, we suggest that F1 score be supplemented with other performance measure such as specificity to improve its validity.