• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.1044-1048
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• 2002

The experimental modal testing has been carried out for the stator of a generator to confirm the vibrational mode shapes and the corresponding natural frequencies. The model of the stator for the vibration analysis was developed and a series of vibration analyses was carried out. And the properties of the solid element were updated to reduce the differences of the natural frequencies between the measured and the analysed. In the vibration anlyses, the axial, radial and circumferential properties of the solid element were separately varied to take into account the orthotropic effect of the laminated structure and to match the primary modes of the stator core which were extracted from the modal testing. After several attempts to match the measured natural frequencies and model shapes, the properties of the stator model were determined. Comparison of the vibration analyses results based on the determined properties showed fairly good coincidence with the measured data.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.37-46
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• 2020

The paper examines the dynamic relationship of domestic credit and stock market liquidity on the economic growth of the Philippines from 1995 to 2018 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration, together with Granger causality test based on vector error correction model (VECM). The ARDL model indicated a long-run relationship of domestic credit and stock market liquidity on GDP growth. When the GDP per capita is the dependent variable there is weak cointegration. Also, the Johansen cointegration test confirmed the existence of long-run relationship of domestic credit and stock market liquidity both on GDP growth and GDP per capita. The VECM concludes a long-run causality running from domestic credit and stock market liquidity to GDP growth. At levels, domestic credit has significant short-run causal relationship with GDP growth. As for stock market liquidity at first lag, has significant short-run causal relationship with GDP growth. With regards to VECM for GDP per capita, domestic credit and stock market liquidity indicates no significant dynamic adjustment to a new equilibrium if a disturbance occurs in the whole system. At levels, the results indicated the presence of short-run causality from stock market liquidity and GDP per capita. The CUSUMSQ plot complements the findings of the CUSUM plot that the estimated models for GDP growth and GDP per capita were stable.

• International Commerce and Information Review
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• v.2 no.1
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• pp.25-48
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• 2009

With the rapid growth of e-logistics in the global logistics industry, it is important to gain further insight into this growing segment of Chinese logistics industry. The current situation in China consists of many small and medium-sized logistics firms. Furthermore, e-logistics is still relatively undeveloped in the majority of the Chinese logistics companies and presently there are still many problems unresolved. This paper attempted to review the concepts and theoretical background of e-logistics systems from previous studies. After acknowledging the essential issues related to e-logistics systems, a research model based on the theory acceptance model was designed and tested. The key factors to the e-logistics system (reliability, maintainability, software, facility and transportation) were validated through the modeling and testing process. Included in the modelling and testing process are other related factors of e-logistics process, logistics information system and added value as dependent variables in this model. The results of this study confirm that the e-logistics Process is affected by transportation, while maintainability and software factors influence logistics information system. reliability, maintainability, facility and transportation are significant factors associated with added value. This research aimed to provide theoretical and practical contribution to Chinese logistics companies and to give some insights into e-logistics system as a whole. The paper also provided some useful theoretical implication and practical guidelines for the development of e-logistics system in the chinese logistics industry.

• International Commerce and Information Review
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• v.11 no.2
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• pp.3-26
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• 2009

With the rapid growth of e-logistics in the global logistics industry, it is important to gain further insight into this growing segment of Chinese logistics industry. The current situation in China consists of many small and medium-sized logistics firms. Furthermore, e-logistics is still relatively undeveloped in the majority of the Chinese logistics companies and presently there are still many problems unresolved. This paper attempted to review the concepts and theoretical background of e-logistics systems from previous studies. After acknowledging the essential issues related to e-logistics systems, a research model based on the theory acceptance model was designed and tested. The key factors to the e-logistics system (reliability, maintainability, software, facility and transportation) were validated through the modeling and testing process. Included in the modelling and testing process are other related factors of e-logistics process, logistics information system and added value as dependent variables in this model. The results of this study confirm that the e-logistics Process is affected by transportation, while maintainability and software factors influence logistics information system. reliability, maintainability, facility and transportation are significant factors associated with added value. This research aimed to provide theoretical and practical contribution to Chinese logistics companies and to give some insights into e-logistics system as a whole. The paper also provided some useful theoretical implication and practical guidelines for the development of e-logistics system in the chinese logistics industry.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.445-450
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• 2003

This study was performed 10 develop the accelerated life test method using Weibull-IPL(Inverse Power Law) model for mechanical components. Weibull-IPL model is concerned with determining the assurance life with confidence level and the accelerated life test time From the relation of weibull distribution factors and confidence limit, the testing times on the no number of failure acceptance criteria arc determined. The mechanical components generally represent wear and fatigue characteristics as a failure mode. IPL based on the cumulative damage theory is applied effectively the mechanical components to reduce the testing time and to achieve the accelerating test conditions. As the actual application example, accelerated life test method of agricultural tractor transmission was described. Life distribution of agricultural tractor transmission was supposed to follow Weibull distribution and life test time was calculated under the conditions of average life (MTBF) 3,000 hours and 90% confidence level for one test sample. According to IPL, because test time call be shorten in case increase test load test time could be reduced by 482 hours when we put the load 1.1 times of rated load than 0.73 times of rated load that is equivalent load calculated by load spectrum of the agricultural tractor. This time, acceleration coefficient was 11.7.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.4
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• pp.409-416
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• 2009

Standard procedures for a fracture toughness testing require very severe restrictions for the specimen geometry to eliminate a size effect on the measured properties. Therefore, the used standard fracture toughness data results in the integrity assessment being irrationally conservative. However, a realistic fracture in general structures, such as in nuclear power plants, may develop under the low constraint condition of a large scale yielding with a shallow surface crack. In this paper, cleavage fracture toughness tests have been made on side-grooved PCVN (precracked charpy V-notch) type specimens (10 by 10 by 55 mm) with various crack depths. The constraint effects on the crack depth ratios were evaluated quantitatively by the developed scaling method using the 3-D finite element method. After the fracture toughness correction from scaling model, the statistical size effects were also corrected according to the standard ASTM E 1921 procedure. The results were evaluated through a comparison with the $T_0$ of the standard CT specimen. The corrected $T_0$ for all of the PCVN specimens showed a good agreement to within $5.4^{\circ}C$ regardless of the crack depth, while the averaged PCVN $T_0$ was $13.4^{\circ}C$ higher than the real CT test results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11A
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• pp.987-994
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• 2005

Most of the proposed SRGMs are required to perfect debugging based on removal of defect as soon as the detection of defects in system tests. But the detected defects are corrected after few days as a fixed time or induced new fault in software under the imperfect debugging environments. Solving these problems, we discussed that the formal software reliability model considered testing-effort for the fault detection and correction of software defects, and then using this model we have estimated of the software reliability closed to practical conditions.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Computers and Concrete
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• v.33 no.2
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

• Journal of Korean Academy of Nursing
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• v.30 no.2
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• pp.366-379
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• 2000

The purpose of this study was to identify causal relationships among variables of transtheoretical model for exercise in the elderly A predictivel model explaining the stage of change was constructed based on a transtheoretical model. Empirical data for testing the hypothetical model was collected from 198 old adults over 60 years old in a community setting in Seoul, Korea in April and May,1999. Data were analyzed by descriptive statistics and correlational analysis using pc-SAS program. The Linear Structural Modeling (LISREL) 8.0 program was used to find the best fit model which predicts causal relationship of variables. The fit of the hypothetical model to the data was X2=132.85. (df=22, p=.000). GFI=.88, NNFI=.35, NFI=.77, AGFI=.59 which was not favorable but the fit of modified model to the data was X2=46.90. (df=27, p=.01).GFI= .95, NNFI=.91, NFI=.92, AGFI=.87) which was more than moderate. The predictable variables of stage of change for exercise of the Korean elderly were helping relationship, self cognitive determination, conversion of negative condition in process of change and efficacy for exercise. These variables explained 68% of stage of change for exercise of the Korean elderly.