• Korean Journal of Radiology
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• v.24 no.1
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• pp.51-61
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• 2023

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent ¹⁸F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from ¹⁸F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

• International Journal of Industrial Entomology and Biomaterials
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• v.4 no.1
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• pp.19-22
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• 2002

Rotary mountages are the best mountages among all the mountages that are used in sericultural areas. But to use this mountage more space, separate mounting hall & requires more labour to pickup the matured worms. to over come these problems, self mounting method is adopted to save the time Er labour, But the mounting rate is generally less. To accelerate the mounting rate different repellents viz; saw dust, phytoecdysone, 1% cresol with paddy husk, lime, kaolin, gormalin chaff were used in the present study. The results indicated that highest number of larvae climbed the mountage in 1% cresol with paddy husk, followed by phytoecdysone and lime. It is observed that the use of repellents at wandering stage accilerates mounting rate and did not affect the quality of the cocoons and reeling characters.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1273-1281
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• 1993

Estimation of daily and seasonal evaportranspiration is essential for water resource planning irrigation feasibility study, and real-time irrigation water management . This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration . A neural network was developed to forecast daily evapotranspiration of the rice crop. It is a three-layer network with input, hidden , and output layers. Back-propagation algorithm with delta learning rule was used to train the neural network. Training neural network wasconducted usign daily actural evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity , and pan evaporation . During the training, neural network parameters were calibrated. The trained network was applied to a set of field data not used in the training . The created response of the neural network was in good agreement with desired values. Evaluating the neural networ performance indicates that neural network may be applied to the estimation of evapotranspiration of the rice crop.

• International Journal of Aerospace System Engineering
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• v.5 no.1
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• pp.9-18
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• 2018

The longitudinal flight parameters of a light airplane are estimated from flight test data by use of the output error method. The reliability of the flight test measurement is examined in engineering judgment, scatter and Cramer-Rao bound, which turns out to be satisfactory with minor defects. Estimated parameter values are validated by comparing the simulated responses with the ones from actual flight tests. The FTD(Flight Training Device) of a light airplane turns out to satisfy the qualification of FAA Level 5 FTD in longitudinal motion. All the necessary practices for generation of high-fidelity data in longitudinal motion of a light aircraft are successfully performed in this study.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.59-67
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• 1998

This paper propose a new vector-quantizer design method for coefficients of wavelet transformed images. In conventional wavelet transform, it is quite often to employ wavelet transformed coefficients, not containing images to be encoded, as training sequences for designing a vector-quantizer. This method has a serious drawback ; it is not known how to find a proper set of training images. This paper investigates characteristics of images that should be considered in the design of vector-quantizers for wavelet transformed images. Besides the statistical parameters such as correlation and standard deviation, edge components are shown to characterise wavelet transform images. Training sequences established in accordance with the above knowledge are used in the design of quantizers having guaranteed range of applicable images. Results of computer simulations are shown to demonstrate the effectiveness of the proposed method.

• Speech Sciences
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• v.7 no.4
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• pp.49-57
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• 2000

This paper proposes an effective decision method focused on evaluating the 'stress position'. Conventional methods usually extract the acoustic parameters and compare them to references in absolute scale, adversely producing unstable results as testing conditions change. To cope with environmental dependency, the proposed method is designed to be model-based and determines the stressed interval by making relative comparison over candidates. The stressed/unstressed models are then induced from normal phone models by adaptive training. The experimental results indicate that the proposed method is promising, and that it is useful for automatic detection of stress positions. The results also show that generating the stressed/unstressed model by adaptive training is effective.

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.119-122
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• 2003

In general, triangular shape filters are used in the filter bank when we get the MFCCs from the spectrum of speech signal. In [1], a new feature extraction approach is proposed, which uses specific filter shapes in the filter bank that are obtained from the spectrum of training speech data. In this approach, principal component analysis technique is applied to the spectrum of the training data to get the filter coefficients. In this paper, we carry out speech recognition experiments, using the new approach given in [1], for a large amount of telephone speech data, that is, the telephone speech database of Korean connected digit released by SITEC. Experimental results are discussed with our findings.

• Computers and Concrete
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• v.24 no.4
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• pp.295-302
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• 2019

Evolutionary algorithms based on conventional statistical methods such as regression and classification have been widely used in data mining applications. This work involves application of gene expression programming (GEP) for predicting compressive strength of fly ash geopolymer concrete, which is gaining increasing interest as an environmentally friendly alternative of Portland cement concrete. Based on 56 test results from the existing literature, a model was obtained relating the compressive strength of fly ash geopolymer concrete with the significantly influencing mix design parameters. The predictions of the model in training and validation were evaluated. The coefficient of determination ($R^2$), mean (${\mu}$) and standard deviation (${\sigma}$) were 0.89, 1.0 and 0.12 respectively, for the training set, and 0.89, 0.99 and 0.13 respectively, for the validation set. The error of prediction by the model was also evaluated and found to be very low. This indicates that the predictions of GEP model are in close agreement with the experimental results suggesting this as a promising method for compressive strength prediction of fly ash geopolymer concrete.

• Journal of Korea Port Economic Association
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• v.34 no.2
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• pp.31-50
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• 2018

This study implemented an empirical analysis of education and training for dangerous goods and hazardous materials handlers on the Busan New Port terminals and hinterland logistics centers using a Structural Equation Modeling (SEM) in combination with the formative model and reflective model, from the viewpoint of the supply chain. An effect size analysis was also conducted. The results of the empirical analysis show that Training Environment and the Atmosphere of Education have a positive influence on the Educational Expectation of hazardous material handlers, and the Educational Expectation has a positive influence on the Education and Training Program and Transfer of Education Training. Likewise, the Education and Training Program has a positive influence on the Transfer of Education Training and Result of Education and Training. Furthermore, the Transfer of Education Training has a positive influence on the Result of Education and Training. The Result of Education and Training has a positive influence on the Present State of hazardous material management. According to the results of the effect size analysis, the following parameters represented a great effect: the Atmosphere of Education to the Education Expectation, the Education Expectation to the Education and Training Program, the Transfer of Education Training to the Result of Education and Training, and the Result of Education and Training to the Present State of Dangerous Goods Management. The results of this study provided various suggestions for related practices.