• The monthly packaging world
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• s.295
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• pp.77-81
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• 2017

• The monthly packaging world
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• s.294
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• pp.90-95
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• 2017

• Korean Journal of Construction Engineering and Management
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• v.7 no.6
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• pp.151-164
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• 2006

Numerous efforts are currently underway to automate pavementcrack and joint sealing activities. Productivity improvements, improved safety and quality, and reduced road user costs motivate these developments. Recently, an automated pavement crack sealing machine has been developed to automate the process of sealing pavement cracks and joints in Korea. This paper mainly describes the results of the economic feasibility analysis revealed through its overall performance evaluation and field tests. Finally, it is concluded that the automated machine exceeds the performance in terms of productivity, safety, and quality required in conventional method, thus making the machine economically feasible.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.2
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• pp.51-58
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• 2015

In this study, we developed an automated non-contact thickness measurement machine that continuously and precisely measures the thickness and warp of a PCB product using a laser sensor. The system contains a measurement part to measure the thickness in real time automatically according to the set conditions with an alignment supply unit and unloading unit to separate OK and NG products. The measurement machine was utilized to evaluate the performance at each step to minimize measurement error. At the zero setting for the initial setup, the standard deviation of the 216 samples was determined to be $5.52{\mu}m$. A measurement error of 0.5mm and 1.0mm as a standard sample in the measurement accuracy assessment was found to be 2.48% and 2.28%, respectively. In the factory acceptance test, the standard deviation of 1.461mm PCB was measured as $28.99{\mu}m$, with a $C_{pk}$ of 1.2. The automatic thickness measurement machine developed in this study can contribute to productivity and quality improvement in the mass production process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.2
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• pp.121-126
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• 2013

Precision machine tools for high dignity cutting are needed for efforts to improve machining accuracy. However, there are many factors to improve machining accuracy. This study investigated how machining accuracy changes when variation and unbalance amount in rotational speed domain is decreased. Machining accuracy of initial machine tools depends on manufacturing and assembly of parts such as bearing. And then, vibration and noise vary with volume of unbalance amount when it is rotation, so it effects unbalance amount. Also vibration and noise increased by unbalance shorten spindle's life and it especially makes worse boring accuracy. Therefore, this study studied the change of roundness and cylindricity of workpiece when it decreases variation and unbalance in rotational speed domain.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• Journal of The Korean Association For Science Education
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• v.36 no.3
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• pp.389-397
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• 2016

This study aims to test the efficacy of English-based automated computer scoring models and machine translation to score Korean college students' written responses on natural selection concept items. To this end, I collected 128 pre-service biology teachers' written responses on four-item instrument (total 512 written responses). The machine translation software (i.e., Google Translate) translated both original responses and spell-corrected responses. The presence/absence of five scientific ideas and three $na{\ddot{i}}ve$ ideas in both translated responses were judged by the automated computer scoring models (i.e., EvoGrader). The computer-scored results (4096 predictions) were compared with expert-scored results. The results illustrated that no significant differences in both average scores and statistical results using average scores was found between the computer-scored result and experts-scored result. The Pearson correlation coefficients of composite scores for each student between computer scoring and experts scoring were 0.848 for scientific ideas and 0.776 for $na{\ddot{i}}ve$ ideas. The inter-rater reliability indices (Cohen kappa) between computer scoring and experts scoring for linguistically simple concepts (e.g., variation, competition, and limited resources) were over 0.8. These findings reveal that the English-based automated computer scoring models and machine translation can be a promising method in scoring Korean college students' written responses on natural selection concept items.

• Korean Medical Education Review
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• v.19 no.3
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• pp.158-168
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• 2017

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword 'asbestos' were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included 'asbestos' in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.162-165
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• 2007

TFT-LCD process has two advantages as compared with the semiconductor-process. It is that cycle time is short and number of the final products are small. But it needs complicated inspection / assembly line to be treated manually and much higher labor costs in the TFT-LCD process. Also, It is necessary to build PICS(Production Information and Control System) which is automated and intelligent. In this paper, an automated process of LCD module assembly line that can increase productivity and reduce the cost of production to strengthen the competitiveness corresponding with global market is planned in comparison with its manual/semi-auto. It is noted that The automated line for COG$\sim$FOG process replacing with the existing facilities had the following effects; the productivity is increased to about 1.5 times and labor cost reduced 85%. In addition, whole assembly line can be short and simple.