• Journal of Information Processing Systems
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• v.14 no.4
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• pp.904-915
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• 2018

We demonstrate how social media content can be used to predict the unemployment rate, a real-world indicator. We present a novel method for predicting the unemployment rate using social media analysis based on natural language processing and statistical modeling. The system collects social media contents including news articles, blogs, and tweets written in Korean, and then extracts data for modeling using part-of-speech tagging and sentiment analysis techniques. The autoregressive integrated moving average with exogenous variables (ARIMAX) and autoregressive with exogenous variables (ARX) models for unemployment rate prediction are fit using the analyzed data. The proposed method quantifies the social moods expressed in social media contents, whereas the existing methods simply present social tendencies. Our model derived a 27.9% improvement in error reduction compared to a Google Index-based model in the mean absolute percentage error metric.

• Proceedings of the Korean Society of Laser Processing Conference
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• 1998.11a
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• pp.14-15
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• 1998

• Proceedings of the Korean Society of Laser Processing Conference
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• 2005.06a
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• pp.48-52
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• 2005

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Journal of the Korean Ceramic Society
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• v.32 no.5
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• pp.535-542
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• 1995

The effect of sintering temperature, sintering time and forming pressure on microwave dielectric properties of (Zr1-xSnx)TiO4 ceramics containing 1.0wt% B2O3, 0.3 wt% La2O3 and 1.0wt% NiTa2O6 was investigated using the response surface methodology. The optimum values of processing variables were determined based on the reproducibility. The optimum values of the dielectric constant of >35. Q.f0 of >55000 and $\tau$f=$\pm$5 ppm/$^{\circ}C$ could be obtained when the sample was pressed at 500~600kg/$\textrm{cm}^2$ and sintered at 1500~155$0^{\circ}C$ for 2~3 hrs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4241-4254
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• 2019

The efficiency and accuracy of handwriting measurement could be improved by adopting digital image processing. This study developed a computer-based Korean Handwriting Assessment tool. Second graders participated in this study by performing writing tasks of consonants, vowels, words, and sentences. We extracted boundary parameters for each letter using digital image processing and calculated the variables of size, size coefficient of variation (CV), misalignment, inter-letter space, inter-word space, and ratio of inter-letter space to inter-word space. Children were also administered traditional handwriting and visuomotor tests. Digital variables from image processing were correlated with these previous tests. Using these correlations, we established a three-point scoring system that computed test scores for each variable. We analyzed inter-rater reliability between the computer rater and human rater and test-retest reliability between the first and second performances. The validity was examined by analyzing the relationship between the Korean Handwriting Assessment and previous handwriting and visuomotor tests. We suggested the Korean Handwriting Assessment to measure size, size consistency, misalignment, inter-letter space, inter-word space, and space ratio using digital image processing. This Korean Handwriting Assessment tool proved to have reliability and validity. It is expected to be useful for assessing children's handwriting.

• Journal of the Semiconductor & Display Technology
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• v.6 no.1 s.18
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• pp.65-69
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• 2007

A design of experiments was applied in order to investigate the effect of processing variables in UV irradiation on the electrical/optical properties in indium-zinc oxide thin films, The processing variables, equivalently input variables are listed as UV irradiation time, oxygen flow rate, and chamber pressure. The statistical significance of Ultra Violet (UV) treatment was confirmed using a paired-t test. The full factorial $2^3$ design was employed to determine significant main and interaction effects in UV irradiation process. The chamber pressure and the interaction between UV irradiation time and $O_2$ flow rate were found to be statistically significant at the significance level of 0.1. Furthermore, the optimized approach was proposed in achieving the improved conductivity after UV irradiation.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2693-2706
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• 1999

Reengineering to object-oriented system is needed to maintain the system and satisfy requirements of structure change. Target systems which should be reengineered to object-oriented system are difficult to change because these systems have no design document or their design document is inconsistent of source code. Using design document to identifying objects for these systems is improper. There are several researches which identify objects through procedural source code analysis. In this paper, we propose automatic object identification method based on clustering of VTFG(Variable-Type-Function Graph) which represents relations among variables, types, and functions. VTFG includes relations among variables, types, and functions that may be basis of objects, and weights of these relations. By clustering related variables, types, and functions using their weights, our method overcomes limit of existing researches which identify too big objects or objects excluding many functions. The method proposed in this paper minimizes user's interaction through automatic object identification and make it easy to reenginner procedural system to object-oriented system.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.443-446
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• 2007

A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.

• Korean Journal of Materials Research
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• v.5 no.2
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• pp.191-196
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• 1995

Self-bonding strength developed at the interface of amorphous PEEK films is highly sensitive to the processing variables(time, temperature, and pressure) during the bonding process. In order to examine the effects of these processing variables, amorphous PEEK films were bonded at various bonding conditions and the resultant interfacial bond strengths were measured using a modified single lap-shear test. Experimental results showed that the developed self-bonding strength increases with increase in bonding temperature and is directly proportional to the bonding time raised to the 1/4 power. The applied pressure seems only to produce better wetting at the beginning stage of the bonding process. Conclusively, the self-bonding of amorphous PEEK films provides a great potential for developing excellent bond strength approaching the strength of the parent material without any adhesives in structural applications.