• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Smart Media Journal
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• v.9 no.4
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• pp.26-35
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• 2020

Attempts to predict future stock prices have been studied steadily since the past. However, unlike general time-series data, financial time-series data has various obstacles to making predictions such as non-stationarity, long-term dependence, and non-linearity. In addition, variables of a wide range of data have limitations in the selection by humans, and the model should be able to automatically extract variables well. In this paper, we propose a 'sliding time step normalization' method that can normalize non-stationary data and LSTM autoencoder to compress variables from all variables. and 'moving transfer learning', which divides periods and performs transfer learning. In addition, the experiment shows that the performance is superior when using as many variables as possible through the neural network rather than using only 100 major financial variables and by using 'sliding time step normalization' to normalize the non-stationarity of data in all sections, it is shown to be effective in improving performance. 'moving transfer learning' shows that it is effective in improving the performance in long test intervals by evaluating the performance of the model and performing transfer learning in the test interval for each step.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_2
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• pp.687-697
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• 2022

An automated system is needed for the effectiveness of non-destructive testing. In order to utilize the radiographic testing data accumulated in the film, the types of welding defects were classified into 9 and the shape of defects were analyzed. Data was preprocessed to use deep learning with high performance in image classification, and a combination of one-stage/two-stage method and convolutional neural networks/Transformer backbone was compared to confirm a model suitable for welding defect detection. The combination of two-stage, which can learn step-by-step, and deep-layered CNN backbone, showed the best performance with mean average precision 0.868.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.421-431
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• 2021

Purpose: For improving outgoing quality, this study presents a novel sampling framework based on predictive analytics. Methods: The proposed framework is composed of three steps. The first step is the variable selection. The knowledge-based and data-driven approaches are employed to select important variables. The second step is the model learning. In this step, we consider the supervised classification methods, the anomaly detection methods, and the rule-based methods. The applying model is the third step. This step includes the all processes to be enabled on real-time prediction. Each prediction model classifies a product as a target sample or random sample. Thereafter intensive quality inspections are executed on the specified target samples. Results: The inspection data of three Samsung products (mobile, TV, refrigerator) are used to check functional defects in the product by utilizing the proposed method. The results demonstrate that using target sampling is more effective and efficient than random sampling. Conclusion: The results of this paper show that the proposed method can efficiently detect products that have the possibilities of user's defect in the lot. Additionally our study can guide practitioners on how to easily detect defective products using stratified sampling

• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.73-82
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• 2020

The main purpose of this study was to analyze the general informatization situation of North Korean defectors and to study the characteristics and needs of the learners in order to provide the directions of information education for them. The results of the study showed the following characteristics of the North Korean defectors: They are slow learners due to the fear of new devices, have difficulty in learning due to the unfamiliar language of information and English, and indifferent when the situation is not related to themselves. Based on these learner characteristics and needs, this study suggests the strategies of step-by-step repetition, use of North and South Korean dictionary of the information terminology, apply job-centered and communication abilities, and suggested a four-element STEP model. Raising the level of informatization of North Korean defectors will help establish a successful settlement to South Korea. This will be a valuable foundation and a stepping stone for the future unification of Korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.649-651
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• 2021

In Korea, there are around 30,000 cardiac arrest patients annually. Gradually the number is increasing. Against this background, CPR education and publicity programs were expanded nationwide, but the rate of witness CPR by the general public was 4.4%, which is significantly lower than the 20%~70% rate in other countries. Therefore, in this paper, we analyzed the factors affecting the performance of CPR by witnesses who discovered cardiac arrest patients. Based on the results, an application planning and development study was conducted to provide users with correct cardiorespiratory response tips and step-by-step CPR guidelines to help users effectively assist in increasing the rate of CPR by general eyewitnesses.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.251-258
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• 2005

Automatic document classification may differ significantly according to the characteristics of documents that are subject to classification, as well as classifier's performance. This research identifies e-mail document's characteristics to apply a three-step preprocessing algorithm that can minimize e-mail document's atypical characteristics. In the first 5go, uncertain based sampling algorithm that used Mean Absolute Deviation(MAD), is used to address the question of selection learning document for the rule generation at the time of classification. In the subsequent stage, Weighted vlaue assigning method by attribute is applied to increase the discriminating capability of the terms that appear on the title on the e-mail document characteristic level. in the third and last stage, accuracy level during classification by each category is increased by using Naive Bayesian Presumptive Algorithm's Dynamic Threshold. And, we implemented an E-Mail Recommendtion System using a three-step preprocessing algorithm the enable users for direct and optimal classification with the recommendation of the applicable category when a mail arrives.

• Korean Journal of Health Education and Promotion
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• v.20 no.2
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• pp.1-18
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• 2003

The purpose of this study was to develop game style courseware in oral health instruction for elementary school children. The manufacturing equipment and languages which were used to develop the courseware were： Photoshop version 7.0, Illustrator version 10.0, HTML, Dream Weaver MX, Editplus, CSS and Java - script. The data base was built up by using PHP and mySQL over Internet Explorer version 4.0. The contents of courseware for oral health education were based on the list of oral health affaires of the Department of Health and Welfare, 2002. The story of the game ‘Saving Hayani locked down in a castle of a cavity man’ was developed for the learners to learn oral health by inducing learning motivation. A character named ‘Chan i’ was introduced to the learners to be more friendly with the program while they were learning. As the game was made of three step education levels, the learner most pass the prepared test given at each step to advance to a higher level. A database connected to web was constructed to store the scores the learners earned at each step. In conclusion, the courseware will help the elementary school children learn oral health care efficiently through the internet regardless of time and space.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.

• Proceedings of the Korea Association of Crystal Growth Conference
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• 1997.10a
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• pp.1-5
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• 1997

The growth mechanism of AMT(Ammonium Meta-Tungstate) crystal was interpreted as two-step model. The contribution of the diffusion step increased with the increase of temperature, crystal size, and supersaturation. The crystal size distribution from a batch cooling crystallizer was predicted by the numerical solution of a mathematical model which uses the kinetics of nucleation and crystal growth. Temperature control of a batch crystallizer was studied using Learning control algorithm. The purity of AMT crystal producted in this investigation was above 99.99%.