• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.281-286
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• 2022

In this study, design of an intuitive internal resistance diagnostic device is to diagnose the residual capacity and aging of the battery regardless of the model and the internal protocol of the waste battery through the method of measuring the internal resistance of a waste battery. In this paper, charging and discharging were continuously performed with 2A charging and 5A discharging in order to secure data on impedance changes that may occur in the charging and discharging process of various methods. As a result of the final experiment, it was confirmed that the impedance change occurred during charging and discharging, and the amount of change increased as the charging/discharging C-rate increased. In addition, it was confirmed that the waste battery aged or abnormal cell had a large change in the impedance value.

• Imaging Science in Dentistry
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• v.43 no.3
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• Toxicological Research
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• v.12 no.2
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• pp.213-221
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• 1996

In order to develop a suitable secondary renal disease model and diagnostic markers of renal disease in the rat, the change of PIIIP (aminoterminal procollagen III peptide) in serum and hydroxyproline levels in the renal tissue that reflect the synthesis of extracellular matrix (ECM) during development of experimental renal diseases were observed. Two types of experimental primary diseases, diabetes mellitus administrated by streptozotocin (STZ, 75 mg/kg, i.p.) and liver cirrhosis produced by bile duct ligation/scission (BDL/s) operation, were induced. The hydroxyproline level increased according to the high PIIIP and NCl(carboxyterminal procollagen IV peptide) in Western blot analysis as early as 1 week in the STZ treated-rat kidney. Increased renal ECM was observed at 15 weeks in STZ and BDL/s model under the microscopic examination. High PAS positive reaction was found in capillary basement membrane in STZ treated-rats and mesangium in BDL/s operated rats at this time, showing the histological characteristics of diabetic nephropathy and cirrhotic glomerulonephritis in human, respectively. Such secondary renal failure were supported by additional tests including urinalysis and renal function test. The serum PIIIP detected by ELISA was a useful parameter to estimate synthesis rate of renal ECM during development of renal disease without extrarenal fibrosis i.e. liver cirrhosis in rats. This study is proposed that STZ treatment or BDL/s operation may be a suitable experimental animal model for the induction and development of chronic secondary renal diseases. Morover, it was found that hydroxyproline level in renal tissues was a good parameter of the change of renal ECM at the early stage of the diseases without apparent histological changes. Especially, serum PIIIP could be a choice as a diagnostic or prognostic marker during the development of renal diseases in rats.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Transactions on Electrical and Electronic Materials
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• v.7 no.5
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• pp.240-246
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• 2006

The mathematical model of the power substation is proposed. The substation esteems as a set of the equipment elements which integrated common connections and electromagnetic field. Some capabilities of model represented by both electric networks and a discrete logical model demonstrated by non-directional graph. The model can be useful for solution of the problem of diagnostic of the high-voltage equipment.

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.97-101
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• 2016

This paper presents a CART(Classification and Regression Tree)-based model to diagnose mental fatigue using speech technology. The parameters used in the model are the significant speech parameters highly correlated to the fatigue and questionnaire responses obtained before and after imposing the fatigue. It is shown from the experiments that the proposed model achieves classification accuracies of 96.67% and 98.33% using the speech parameters and questionnaire responses, respectively. This implies that the proposed model can be used as a tool to diagnose the mental fatigue, and that speech technology is useful to diagnose the fatigue.

• Journal of Environmental Policy
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• v.1 no.1
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• pp.91-110
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• 2002

Ecosystem health assessment is an emerging concept regarded as a useful diagnostic tool for evaluating ecosystems. The stability of ecosystem is the main theme in the assessment. Generally, two components - resilience and resistance - are involved in the mechanism of ecosystem stability. In this study, relative degrees of the resistance and the resilience were quantified for most aquatic Insects Inhabiting running waters in Korea. A total of 34 groups were newly categorized based on previous studies, and a conceptual model has been produced. The model was applied for the aquatic insect communities inhabiting different streams and demonstrated that each stream ecosystem possessed different degrees of stability. This study also indicated that it was possible to compare stabilities of different ecosystems using relative degrees of resilience and resistance. Using the conceptual model, suitable conservation and management strategies could be recommended in ecological assessments. The model can be used as a stepping-stone for developing more comprehensive methodology that objectively diagnoses and evaluates the ecosystem stability.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.3
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• pp.8-13
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• 2008

The paper proposes a model-based measurement of unsteady flow rate by using distributed parameter pipeline model and the measured pressure values at two distant points along the pipeline. The distributed parameter model of hydraulic pipeline is applied with consideration of frequency dependent viscosity friction and unsteady velocity distribution at a cross section of a pipeline. By using the self-diagnostics functions of the measurement method, the validity is investigated by comparison with the measured and estimated pressure and flow rate wave forms at the halfway section on the pipeline. The results show good agreement between the estimated flow rate wave forms and theoretical those under unsteady laminar flow conditions. The method proposed here is useful in estimating unsteady flow rate through an arbitrary cross section in hydraulic pipeline and components without installing an instantaneous flowmeter.

• Journal of dental hygiene science
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• v.5 no.1
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• pp.25-32
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• 2005

The dental hygiene process of care is a model for providing integrated dental hygiene care. It was developed by Mueller-Joseph and Petersen in 1995. The purpose of the dental hygiene process is to provide a framework within which the individualized needs of the client can be met. This model enables the dental hygienist to focus on patient need. The process is composed of five components: assessment, diagnosis, planning, implementation and evaluation. The process of dental hygiene has to move from simple clinical procedure to comprehensive and systemic dental hygiene care. The dental hygiene diagnostic model broadens the biomedical dental model to the behavioral model to include health behavior and health function of individuals. The dental hygiene process will provide a mechanism to develop dental hygienist's role and scope of practice in Korea.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.137-141
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• 2022

To inspect the power facility faults using artificial intelligence, it need that improve the accuracy of the diagnostic model are required. Data augmentation skill using generative adversarial network (GAN) is one of the best ways to improve deep learning performance. GAN model can create realistic-looking fake images using two competitive learning networks such as discriminator and generator. In this study, we intend to verify the effectiveness of virtual data generation technology by including the fake image of power facility generated through GAN in the deep learning training set. The GAN-based fake image was created for damage of LP insulator, and ResNet based normal and defect classification model was developed to verify the effect. Through this, we analyzed the model accuracy according to the ratio of normal and defective training data.