• Journal of Fisheries and Marine Sciences Education
• /
• v.11 no.1
• /
• pp.43-58
• /
• 1999

To instruct in accordance with learner's trait & preceding knowledge, letting the learner control the learning activities is the important task of educator & major goal of the Education Department this year. This article intends to provide useful Instructional Model for the teachers in fisheries marine high school, when they design the individualized teaching model using motivation. One of the major reason for the fisheries marine high school students' low learning achievement is due to the neglecting motivation elements in teaching - learning processes. Recently, with assistance of the information communication technology development, various teaching methods such as Individualized Multimedia Mediated Instruction, Internet Instruction, have come to the major method in activating motivation and computer-mediated instruction considering the learner's individual difference is the useful tools for the instructional efficiency. Because current navigation text book of fisheries marine high school have special characteristic considering the spacial context & time series from departing port to entering port, Teachers can maximize learner's learning accomplishment by using individualized multimedia & providing similar situation like a real navigation(simulating), representing this text characteristics. Thus this paper searches for the specifications of Keller's Motivation Model & Sweeter's Tutorial Model to solve instructional efficiency problems in fisheries marine high school & developed an efficient instructional design by integrating two models.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.11
• /
• pp.1-10
• /
• 2021

This paper compares the performance of Fama-French three-factor and five-factor models using a dataset of 20 Pakistani commercial banks for the period 2011 to 2020. We focus on an emerging economy as the findings from earlier studies on developed countries cannot be generalized in emerging markets. For empirical analysis, twelve portfolios were developed based on size, market capitalization, investment strategy, and growth. Subsequently, we constructed five Fama-French factors namely, R_M, SMB, HML, RMW, and CMA. The OLS regression technique with robust standard errors was applied to compare the predictive power of both the Fama-French models. Further, we also compared the mean-variance efficiency of the Fama-French models through the GRS test. Our empirical analysis provides three unique and interesting findings. First, both asset pricing models have similar predictive power to explain the expected portfolio returns in most cases. Second, our results from the GRS test suggest that there is no noticeable difference in the mean-variance efficiency of one asset pricing model over the other. Third, we find that all factors of both Fama-French models are statistically significant and are important for explaining the volatility of expected commercial bank returns in the context of Pakistan.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.3
• /
• pp.527-538
• /
• 2021

The purpose of the study is to determine the efficiency, position, and partner selection of securities companies via the negative super-SBM model used in data envelopment analysis (DEA). This model utilizes a variety of inputs, including current assets, non-current assets, fixed assets, liabilities, owner's equity and charter capital, and outputs including net revenue, gross profit, operating profit, and net profit after tax collected from the financial reports (Vietstock, 2020) of 32 securities companies, operating during the period from 2016 to 2019, negative data are collected as well. Empirical results determined both efficient and inefficient terms, and then further determined the position of each securities firm under consideration of every term. The overall score arrived at discovered a large performance change realizing a maximum score able to reach 20.791. In the next stage, alliancing inefficient companies was carried out based on the 2019 scores to seek out optimal partners for the inefficient companies. The tested result indicated that AAS was the best partner selection when its partners received a good result after alliancing, as with FTS (11.04469). The partner selection is deemed as a solution helpful to inefficient securities companies in order to improve their future efficiency scores.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.143-148
• /
• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.122-122
• /
• 2020

Urban flood simulation plays a vital role in national flood early warning, prevention and mitigation. In recent studies on 2-dimensional flood modeling, the integrated run-off inundation model is gaining grounds due to its ability to perform in greater computational efficiency. The adaptive quadtree shallow water numerical technique used in this model implements the adaptive mesh refinement (AMR) in this simulation, a procedure in which the grid resolution is refined automatically following the flood flow. The method discounts the necessity to create a whole domain mesh over a complex catchment area, which is one of the most time-consuming steps in flood simulation. This research applies the dynamic grid refinement method in simulating the recent extreme flood events in Metro Manila, Philippines. The rainfall events utilized were during Typhoon Ketsana 2009, and Southwest monsoon surges in 2012 and 2013. In order to much more visualize the urban flooding that incorporates the flow within buildings and high-elevation areas, Digital Surface Model (DSM) resolution of 5m was used in representing the ground elevation. Results were calibrated through the flood point validation data and compared to the present flood hazard maps used for policy making by the national government agency. The accuracy and efficiency of the method provides a strong front in making it commendable to use for early warning and flood inundation analysis for future similar flood events.

• Annual Conference of KIPS
• /
• 2022.11a
• /
• pp.625-627
• /
• 2022

In the construction and development of modern industrial production technology, the traditional technology management mode is faced with many problems such as low qualification rates and high application costs. In the research, an improved workpiece defect detection method based on deep learning is proposed, which can control the application cost and improve the detection efficiency of irregular defects. Based on the research of the current situation of deep learning applications, this paper uses the improved Faster R-CNN network structure model as the core detection algorithm to automatically locate and classify the defect areas of the workpiece. Firstly, the robustness of the model was improved by appropriately changing the depth and the number of channels of the backbone network, and the hyperparameters of the improved model were adjusted. Then the deformable convolution is added to improve the detection ability of irregular defects. The final experimental results show that this method's average detection accuracy (mAP) is 4.5% higher than that of other methods. The model with anchor size and aspect ratio (65,129,257,519) and (0.2,0.5,1,1) has the highest defect recognition rate, and the detection accuracy reaches 93.88%.

• The Journal of Society for e-Business Studies
• /
• v.19 no.3
• /
• pp.91-105
• /
• 2014

This study purposed to benchmark the number of patients who visited an oriental medicine hospital from its surrounding regions using data envelopment analysis (DEA) model, and to analyze the relationships between regional characteristics and efficiency scores from DEA. Study data was collected from one oriental medicine hospital operated in a metropolitan city in Korea. Patient locations were identified at the smallest administrative district, Dong, and number of patients was calculated at the Dong level based on the address of patients in hospital information system. Socio-demographic variables of each Dong were identified from the Statistics of Korea web-sites. DEA was used to benchmark the number of patients between Dongs and to compute the efficiency scores. Tobit regression analysis model was applied to analyze the relationship between efficiency scores and regional variables. 6 Dongs were identified as efficient after DEA. In Tobit analysis, number of medical aid recipients and number of total population in each Dong was significant in explaining the differences of efficiency scores. The study model introduced the application of DEA model in benchmarking the patients between regions. It can be applied to identify the number of patients in each region which a hospital needs to improve their performances.

• Korean Chemical Engineering Research
• /
• v.50 no.3
• /
• pp.516-521
• /
• 2012

This study developed a simple model to investigate effects of important operating parameters on performance of a bubbling-bed adsorber and regenerator system collecting $CO_2$ from flue gas. The chemical reaction rate was used with mean particles residence time of a reactor to determine the extent of conversion in both adsorber and regenerator reactors. Effects of process parameters - temperature, gas velocity, solid circulation rate, moisture content of feed gas - on $CO_2$ capture efficiency were investigated in a laboratory scale process. The $CO_2$ capture efficiency decreased with increasing temperature or gas velocity of the adsorber. However, it increased with increasing the moisture content of the flue gas or the regenerator temperature. The calculated $CO_2$ capture efficiency agreed to the measured value reasonably well. However the present model did not agree well to the effect of the solid circulation rate on $CO_2$ capture efficiency. Better understanding on contact efficiency between gas and particles was needed to interpret the effect properly.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.1 s.45
• /
• pp.205-217
• /
• 2007

The purpose of this paper is to measure and analyze the efficiency of management in general bank. Each bank was analyzed to find out the efficiency and classified as efficient and inefficient bank. To the inefficient bank, the inefficient part and the extent are round out, the improvement methods of efficiency are suggested, and the effects of scale of bank on management efficiency by BCC model are analyzed. And, the differences between commercial bank and local bank also are examined. Empirical main results are as follows: Frist, After 2000, both of commercial and local bank shows the continuous improvement of the management efficiency. Second, the relative efficiency of management of commercial bank is higher than local bank except 2004. Third, there are differences in variables except equity ratio and no differences appeared in the management efficiency between commercial bank and local bank. Fourth, there are no influences or negative influences by M&A and reconstruction among banks in the effect of scale to the management efficiency.

• Journal of Soil and Groundwater Environment
• /
• v.18 no.1
• /
• pp.46-56
• /
• 2013

Laboratory scale experiments to remove benzene in solution by using the bio-carrier composed of dead biomass have been performed. The immobilized bio-carrier with dead Bacillus drentensis sp. and polysulfone was manufactured as the biosorbent. Batch sorption experiments were performed with bio-carriers having various quantities of biomass and then, their removal efficiencies and uptake capacities were calculated. From results of batch experiments, 98.0% of the initial benzene (1 mg/L) in 1 liter of solution was removed by using 40 g of immobilized bio-carrier containing 5% biomass within 1 hour and the biosorption reaction reached in equilibrium within 2 hours. Benzene removal efficiency slightly increased (99.0 to $99.4%{\pm}0.05$) as the temperature increased from 15 to $35^{\circ}C$, suggesting that the temperature rarely affects on the removal efficiency of the bio-carrier. The removal efficiency changed under the different initial benzene concentration in solution and benzene removal efficiency of the bio-carrier increased with the increase of the initial benzene concentration (0.001 to 10 mg/L). More than 99.0% of benzene was removed from solution when the initial benzene concentration ranged from 1 to 10 mg/L. From results of fitting process for batch experimental data to Langmuir and Freundlich isotherms, the removal isotherms of benzene were more well fitted to Freundlich model ($r^2$=0.9242) rather than Langmuir model ($r^2$=0.7453). From the column experiment, the benzene removal efficiency maintained over 99.0% until 420 pore volumes of benzene solution (initial benzene concentration: 1 mg/L) were injected in the column packed with bio-carriers, investigating that the immobilized carrier containing Bacillus drentensis sp. and polysulfone is the outstanding biosorbent to remove benzene in solution.