• Journal of Korean Elementary Science Education
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• v.31 no.3
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• pp.334-346
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• 2012

For teachers' conceptions and understandings are critical to their decision making and classroom practice, this study attempts to understand pre-service elementary teachers' views and practices of science inquiry during peer teaching practice. Fifteen 4th year university students in teacher education program participated in peer teaching practice. Their teaching and reflective discussion were video and audio recorded and written lesson plans were collected for data analysis. Five science teacher educators individually looked into the data and shared their comments and interpretations on pre-service teachers' views and practice. The study findings suggest that pre-service teachers emphasized the importance of providing students with motivating resources in the beginning of lesson, employing certain inquiry teaching models, the process of predicting and dis/proving via experiment, and teachers' minimal intervention as the important features of inquiry teaching. Science teacher educators emphasized that it is critical to help children understand inquiry questions in the beginning of inquiry process, to be mindful of children's problem solving and critical thinking rather than following instruction models or simply going through prediction and test process. They also commented that teachers' guidance could lead a good inquiry process in classroom practice, not always interfering students' inquiry. Based on the findings, the study suggests science teacher educators need to understand what and how pre-service teachers view and practice science inquiry teaching and consider these as useful resources where they can start effective teaching for pre-service teachers at the university level.

• Journal of KIISE:Information Networking
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• v.34 no.4
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• pp.287-295
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• 2007

IETF's FMIPv6 protocol enables a mobile node to switch to the reactive mode of handover operation when the prediction of the movement is incorrect. In this case, the mobile node may experience severe service disruption due to large handover latency and packet loss. In order to solve this problem, we propose a fast mobile IP handover with multiple pre-registrations. In the proposed approach, the new temporary IP addresses are prepared in advance at multiple locations where the mobile node may probably move into. In this case, even though the prediction is wrong, the mobile node can move into the alternative locations without causing service disruption. We have designed and implemented a prototype system, and measured the performance of the proposed system. The experimental results show that the proposed approach can reduce the handover latency drastically.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.102-110
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• 2010

Alternative splicing is an important process in gene expression. Alternative Splicing can lead to mutations and diseases. Most studies detect alternatively spliced genes with ESTs (Expressed Sequence Tags). However, reliance on ESTs might have some weaknesses in predicting alternative splicing. ESTs have been stored in the libraries. The EST libraries are often not clearly organized and annotated. We can pick erroneous ESTs. It is also difficult to predict whether or not alternative splicing exists for those genes where ESTs are not available. To address these issues and to improve the quality of detection and prediction for alternative splicing, we propose the One-leaf One-node Tree Algorithm that uses pre-mRNAs. It is achieved by codons, three nucleotides, as attributes for each chromosome in Arabidopsis thaliana. The proposed decision tree shows that alternative and normal splicing have different splicing patterns according to triplet nucleotides in each chromosome. Based on the patterns, alternative splicing of unlabeled genes can also be predicted.

• Journal of the Korean GEO-environmental Society
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• v.13 no.12
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• pp.35-42
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• 2012

There are many large-scale coastal region landfill and land development by loading to use territory efficiently, this regions are mostly soft clay ground. Constructing structures and road on the soft ground bring about engineering problems like ground shear fracture and a big amount of consolidation by bearing capacity. Improvement of soft soil is required to secure soil strength and settlement control. In improvement of soft soil, predict for the amount of settlement based on field surveyed reports are important element for estimating pre-loading banking height and the final point of consolidation. In this study, there is calculating theoretical settlement by analyzing field surveyed report and ground investigation to improvement of soft soil with pre-loading and vertical drain method. And present settlement prediction method reflect soil characteristics in Gimpo Hangang site by analysing prediction settlement and observational settlement during compaction using hyperbolic, ${\sqrt{s}}$, Asaoka method.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.347-355
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• 2017

The purpose of this study is to develop a formula for predicting weight gain after six months of smoking cessation in men. The subjects are 412 men who succeeded in quitting smoking for 6 months at public health center smoking cessation clinic. They have been undergone nicotine patch therapy and weekly counseling for 8 weeks. The final success of smoking cessation has been confirmed by urinalysis. I have measured body composition and vascular compliance before and after the program. Weight(0.98) and BMI(0.85) have shown high positive correlation. The prediction is as follows. Post Weight(kg) = 1.04636 * Pre-Weight - 0.19535 * Pre-BMI + 4.43528. The explanatory power of this estimation equation is 82.46%(<.0001). Based on these results, it is necessary to develop education and programs for effective counseling of the smoking cessation clinic. In addition, research on women is needed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.367-373
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• 2021

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.160-160
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• 2008

In practical etching process, etch ant is sprayed on the metal-deposited panel through nozzles collectively connected to the manifold and that panel is usually composed of many PCB(printed circuit board)'s. The etching uniformity, the difference between individual PCB's on the same panel, has become one of most important features of etching process. In this paper, the prediction of nozzle trajectory has been performed by the combination of algebraic formula and numerical simulation. With the pre-determined geometrical factors of nozzle distribution, the trajectories of individual nozzles were predicted with the change of process operational factors such as panel speed, nozzle swing frequency and so on. As results, two dimensional distribution of impulsive force of etchant spray which could be considered as a key factor determining the etching performance have been successfully obtained. Though only qualitative prediction of etching uniformity have been predicted by the process developed in this study, the expansion to the quantitative prediction of etching uniformity is expected to be apparent by this study.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.383-396
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• 2017

The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (${\beta}^{\prime}s$) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (${\theta}^{\prime}s$) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

• The Journal of Information Systems
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• v.30 no.2
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• pp.105-126
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• 2021

Purpose The purpose of this study is to implement a optimal machine learning model about the cancellation prediction performance in car sales business. It is to apply the data set of accumulated contract, cancellation, and sales information in sales support system(SFA) which is commonly used for sales, customers and inventory management by imported car dealers, to several machine learning models and predict performance of cancellation. Design/methodology/approach This study extracts 29,073 contracts, cancellations, and sales data from 2015 to 2020 accumulated in the sales support system(SFA) for imported car dealers and uses the analysis program Python Jupiter notebook in order to perform data pre-processing, verification, and modeling that is applying and learning to Machine learning model after then the final result was predicted using new data. Findings This study confirmed that cancellation prediction is possible by applying car purchase contract information to machine learning models. It proved the possibility of developing and utilizing a generalized predictive model by using data of imported car sales system with machine learning technology. It can reduce and prevent the sales failure as caring the potential lost customer intensively and it lead to increase sales revenue by predicting the cancellation possibility of individual customers.