• 대한공업교육학회지
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• 제30권1호
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• pp.1-18
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• 2005

The purpose of this study was to development a model of problem solving instruction for improving practical skill-competence in technical high school. For the study, various literature researches were reviewed intensively about problem solving process, laboratory instruction's approaches and learning principals. The problem solving instruction process was composed with identifying problems, generating alternative solutions, investigation and research, choosing a solution, acting on a plan, modeling of problem solving, testing and evaluating, redesigning and improving. The skills schema combines a four domain of skilled activity, that is, cognitive skills, psychomotor skills, reactive skills and interactive skills. The problem solving instruction was composed with five major learning systems-emotional, social, cognitive, physical, and reflective-that can be used extensively as generic lesson plashing. The teacher serves as a coach or guide for student learning. As a facilitator, the teacher challenges, questions, and stimulates the students in their thinking, problem solving and self-directed study. In this process, students represent problem with think aloud, assume responsibility for their learning and move from teacher-centered to student-centered education.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2006년도 춘계 학술발표회 논문집
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• pp.402-413
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• 2006

Strength evaluation of soft soils is a formidable task because of difficulties in sampling, specimen preparation and setting in triaxial cells. In undrained triaxial testing, sampling disturbance, verticality of specimen and bedding effect give a great influence on shear strength measurements. In the other hand, shear wave measurements of specimens are less influenced by these factors. In this research, the bender elements were attached top cap and base pedestal of triaxial cell and shear wave velocities were measured. To initiate a methodology to evaluate shear strength indirectly by measuring shear wave velocity, a relationship between shear strength and shear wave velocity was developed with kaolinite specimens consolidated in the laboratory. Undrained shear strength turns out to increase linearly with shear wave velocity. Stress-strain curves can also be predicted with a hyperbolic model and shear wave measurements.

• 한국지반공학회논문집
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• 제16권2호
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• pp.71-77
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• 2000

In this paper, consolidation settlements with time at vertical drain sites were predicted by artificial neural networks. Laboratory test results and field measurements of two vertical drain sites were used for training and testing neural networks. Predicted consolidation settlements by trained artificial neural networks were compared with measured settlements by field instrumentation. To improve the prediction accuracy, modular artificial neural networks were studied. From the results of applying artificial neural networks to the same situation, it was shown that modular artificial neural network model was more accurate for the prediction of the consolidation settlements than the general model.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2004년도 제22회 춘계학술대회논문집
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• pp.205-210
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• 2004

The pulse detonation engine (PDE) has recently expected as a new aerospace propulsion system. The PDE system has high thermal efficiency because of its constant-volume combustion and its simple tube structure. We measured thrust of single-tube pulse detonation rocket (PDR) by two methods using the PDR-Engineering Model (full scale model) for ground testing. The first involved measuring the displacement of the PDR-EM by laser displacement meter, and the second involved measuring the time-averaged thrust by combining a load cell and a spring-damper system. From these two measurements, we obtained 130.1 N of time-averaged thrust, which corresponds to 321.2 sec of effective specific impulse (ISP). As well, we measured the heat flux in the wall of PDE tubes. The heat flux was approximately 400 ㎾/$m^2$. We constructed the PDR-Flight Mode] (PDR-FM). In the vertical flight test in a laboratory, the PDR-FM was flying and keeping its altitude almost constant during 0.3 sec.

• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.932-941
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• 2014

In this paper, a simulation model for 7-phase BLDC motor drives for an Autonomous Underwater Vehicles (AUV) is proposed. A 7-phase BLDC motor is designed and the electrical characteristics are analyzed using FEA program and the power electronics drives for the 7-phase BLDC motor are theoretically analyzed and the actual implementation has been accomplished using Matlab Simulink. PI controller and fuzzy controller are compared for verifying the validity of the proposed model and the informative results are described in detail. Especially A fuzzy controller is used to characterize 7-phase BLDC motor, drive systems under normal and fault operating conditions.

• 대한수학회지
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• 제59권2호
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• pp.217-233
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• 2022

Although machine learning shows state-of-the-art performance in a variety of fields, it is short a theoretical understanding of how machine learning works. Recently, theoretical approaches are actively being studied, and there are results for one of them, margin and its distribution. In this paper, especially we focused on the role of margin in the perturbations of inputs and parameters. We show a generalization bound for two cases, a linear model for binary classification and neural networks for multi-classification, when the inputs have normal distributed random noises. The additional generalization term caused by random noises is related to margin and exponentially inversely proportional to the noise level for binary classification. And in neural networks, the additional generalization term depends on (input dimension) × (norms of input and weights). For these results, we used the PAC-Bayesian framework. This paper is considering random noises and margin together, and it will be helpful to a better understanding of model sensitivity and the construction of robust generalization.

• International Journal of Fluid Machinery and Systems
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• 제5권3호
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• pp.134-142
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• 2012

The competition to deliver ultra-low emitting vehicles at a reasonable cost is driving the automotive industry to invest significant manpower and test laboratory resources in the design optimization of increasingly complex exhaust after-treatment systems. Optimization can no longer be based on traditional approaches, which are intensive in hardware use and laboratory testing. The CFD is in high demand for the analysis and design in order to reduce developing cost and time consuming in experiments. This paper describes the development of a comprehensive practical model based on experiments for simulating the performance of automotive three-way catalytic converters, which are employed to reduce engine exhaust emissions. An experiment is conducted to measure species concentrations before and after catalytic converter for different loads on engine. The model simulates the emission system behavior by using an exhaust system heat conservation and catalyst chemical kinetic sub-model. CFD simulation is used to study the performance of automotive catalytic converter. The substrate is modeled as a porous media in FLUENT and the standard k-e model is used for turbulence. The flow pattern is changed from axial to radial by changing the substrate model inside the catalytic converter and the flow distribution and the conversion efficiency of CO, HC and NOx are achieved first, and the predictions are in good agreement with the experimental measurements. It is found that the conversion from axial to radial flow makes the catalytic converter more efficient. These studies help to understand better the performance of the catalytic converter in order to optimize the converter design.

• 대한의용생체공학회:의공학회지
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• 제41권5호
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• pp.195-202
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• 2020

Needle detection in ultrasound images is sometimes difficult due to obstruction of fat tissues. Accurate needle detection using continuous ultrasound (CUS) images is a vital stage of treatment planning for tissue biopsy and brachytherapy. The main goal of the study is classified into two categories. First, new detection model, i.e. D-Attention Unet, is developed by combining the context information of 3D medical data and CUS images. Second, the D-Attention Unet model was compared with other models to verify its usefulness for needle detection in continuous ultrasound images. The continuous needle images taken with ultrasonic waves were converted into still images for dataset to evaluate the performance of the D-Attention Unet. The dataset was used for training and testing. Based on the results, the proposed D-Attention Unet model showed the better performance than other 3 models (Unet, D-Unet and Attention Unet), with Dice Similarity Coefficient (DSC), Recall and Precision at 71.9%, 70.6% and 73.7%, respectively. In conclusion, the D-Attention Unet model provides accurate needle detection for US-guided biopsy or brachytherapy, facilitating the clinical workflow. Especially, this kind of research is enthusiastically being performed on how to add image processing techniques to learning techniques. Thus, the proposed method is applied in this manner, it will be more effective technique than before.

• Asian Pacific Journal of Cancer Prevention
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• 제14권8호
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• pp.4699-4704
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• 2013

Background: MicroRNAs have been demonstrated to play important roles in the development and progression of colorectal cancer. Several studies utilizing microRNAs as diagnostic biomarkers for colorectal cancer (CRC) have been reported. The aim of this meta-analysis was to comprehensively and quantitatively summarize the diagnostic value of microRNAs for detecting colorectal cancer. Methods: We searched PubMed, Embase and Cochrane Library for published studies that used microRNAs as biomarkers for the diagnosis of colorectal cancer. Summary estimates for sensitivity, specificity and other measures of accuracy of microRNAs in the diagnosis of colorectal cancer were calculated using the bivariate random effects model. A summary receiver operating characteristic (SROC) curve was also generated to summarize the overall effectiveness of the test. Result: Thirteen studies from twelve published articles met the inclusion criteria and were included. The overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odd ratio of microRNAs for the diagnosis of colorectal cancer were 0.81 (95%CI: 0.79-0.84), 0.78 (95%CI: 0.75-0.82), 4.14 (95%CI: 2.90-5.92), 0.24 (95%CI: 0.19-0.30), and 19.2 (95%CI: 11.7-31.5), respectively. The area under the SROC curve was 0.89. Conclusions: The current evidence suggests that the microRNAs test might not be used alone as a screening tool for CRC. Combining microRNAs testing with other conventional tests such as FOBT may improve the diagnostic accuracy for detecting CRC.

• Computers and Concrete
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• 제21권1호
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.