• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.8
• /
• pp.339-346
• /
• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.1
• /
• pp.24-35
• /
• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.3
• /
• pp.499-510
• /
• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.

• The korean journal of orthodontics
• /
• v.27 no.5 s.64
• /
• pp.771-779
• /
• 1997

The purpose of this in vitro study was to evaluate the effects of different acid etching times on the enamel surface morphology, shear bond strength and debonding failure mode of orthodontic attachment. Ninety six extracted human mandibular premolars were divided into eight groups of twelve teeth. The buccal surfaces were etched with $37\%$, phosphoric acid for 5, 10, 15, 30, 45, 60, 90 and 120 seconds, respectively. Two teeth from each group were used for scanning electron microscope examination. On the etched buccal surfaces of remaining teeth, orthodontic attachments(lingual buttons) were bonded with light cured orthodontic adhesive. Twenty foot hours after bonding, a Instron universal testing machine was used to determine shear bond strength of orthodontic attachment to enamel. After debonding, bases of orthodontic attachments and enamel surfaces were examined under stereoscopic microscope to determine failure mode. Statistical analysis of the data was carried out with one nay ANOVA and Duncan's multiple range test The results were as follows; 1. There was no statistically significant difference in shear bond strengths between the various etching times(p<0.05). 2. The failure modes of orthodontic attachments had some differences. In 5, 10 and 15 seconds etching groups, the percentage of adhesive/enamel interface failure was higher than that of adhesive/attachment interface failure. On the contrary, in 30, 45, 60, 90 and 120 seconds etching groups, the results were reversed. 3. The etching patterns of enamel surfaces had a great variation. So, we could not find any correlation between etching pattern and bond strength. 4. The findings in this study indicate that in vitro reduction of the etching me to 5 seconds maintains clinically acceptable bond strength. However, further study is required to determine the cause of failure mode in 5, 10 and 15 seconds groups.

• Proceedings of the KACD Conference
• /
• 2008.05a
• /
• pp.177-183
• /
• 2008

It was reported that esthetic composite resin restoration reinforces the strength of remaining tooth structure with preserving the natural tooth structure. However, it is unknown how much the strength would be recovered. The purpose of this study was to compare the fracture resistance of three types of undermined cavity filled with composite resin with that of non-cavitated natural tooth. Forty sound upper molars were allocated randomly into four groups of 10 teeth. After flattening occlusal enamel. undermined cavities were prepared in thirty teeth to make three types of specimens with various thickness of occlusal structure (Group $1{\sim}3$). All the cavity have the 5 mm width mesio-distally and 7 mm depth bucco-lingually. Another natural 10 teeth (Group 4) were used as a control group. Teeth in group 1 have remaining occlusal structure about 1 mm thickness, which was composed of mainly enamel and small amount of dentin. In Group 2, remained thickness was about 1.5 mm, including 0.5 mm thickness dentin. In Group 3, thickness was about 2.0 mm, including 1 mm thickness dentin. Every effort was made to keep the remaining dentin thickness about 0.5 mm from the pulp space in cavitated groups. All the thickness was evaluated with radiographic Length Analyzer program. After acid etching with 37% phosphoric acid, one-bottle adhesive (Single $Bond^{TM}$, 3M/ESPE, USA) was applied following the manufacturer's recommendation and cavities were incrementally filled with hybrid composite resin (Filtek $Z-250^{TM}$, 3M/ESPE, USA). Teeth were stored in distilled water for one day at room temperature, after then, they were finished and polished with Sof-Lex system. All specimens were embedded in acrylic resin and static load was applied to the specimens with a 3 mm diameter stainless steel rod in an Universal testing machine and cross-head speed was 1 mm/min. Maximum load in case of fracture was recorded for each specimen. The data were statistically analyzed using one-way analysis of variance (ANOVA) and a Tukey test at the 95% confidence level. The results were as follows: 1. Fracture resistance of the undermined cavity filled with composite resin was about 75% of the natural tooth. 2. No significant difference on fracture loads of composite resin restoration was found among the three types of cavitated groups. Within the limits of this study, it can be concluded the fracture resistance of the undermined cavity filled with composite resin was lower than that of natural teeth, however remaining tooth structure may be supported and saved by the reinforcement with adhesive restoration, even of that portion consists of mainly enamel and a little dentin structure.

• Restorative Dentistry and Endodontics
• /
• v.33 no.3
• /
• pp.177-183
• /
• 2008

It was reported that esthetic composite resin restoration reinforces the strength of remaining tooth structure with preserving the natural tooth structure. However, it is unknown how much the strength would be recovered. The purpose of this study was to compare the fracture resistance of three types of undermined cavity filled with composite resin with that of non-cavitated natural tooth. Forty sound upper molars were allocated randomly into four groups of 10 teeth. After flattening occlusal enamel, undermined cavities were prepared in thirty teeth to make three types of specimens with various thickness of occlusal structure (Group $1{\sim}3$). All the cavity have the 5 mm width mesiodistally and 7 mm depth bucco-lingually. Another natural 10 teeth (Group 4) were used as a control group. Teeth in group 1 have remaining occlusal structure about 1 mm thickness, which was composed of mainly enamel and small amount of dentin. In Group 2, remained thickness was about 1.5 mm, including 0.5 mm thickness dentin. In Group 3, thickness was about 2.0 mm, including 1 mm thickness dentin. Every effort was made to keep the remaining dentin thickness about 0.5 mm from the pulp space in cavitated groups. All the thickness was evaluated with radiographic Length Analyzer program. After acid etching with 37% phosphoric acid, one-bottle adhesive (Single $Bond^{TM}$, 3M/ESPE, USA) was applied following the manufacturer's recommendation and cavities were incrementally filled with hybrid composite resin (Filtek $Z-250^{TM}$, 3M/ESPE, USA). Teeth were stored in distilled water for one day at room temperature, after then, they were finished and polished with Sof-Lex system. All specimens were embedded in acrylic resin and static load was applied to the specimens with a 3 mm diameter stainless steel rod in an Universal testing machine and cross-head speed was 1 mm/min. Maximum load in case of fracture was recorded for each specimen. The data were statistically analyzed using one-way analysis of variance (ANOVA) and a Tukey test at the 95% confidence level. The results were as follows: 1. Fracture resistance of the undermined cavity filled with composite resin was about 75% of the natural tooth. 2. No significant difference in fracture loads of composite resin restoration was found among the three types of cavitated groups. Within the limits of this study, it can be concluded the fracture resistance of the undermined cavity filled with composite resin was lower than that of natural teeth, however remaining tooth structure may be supported and saved by the reinforcement with adhesive restoration, even if that portion consists of mainly enamel and a little dentin structure.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.127-137
• /
• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.71-89
• /
• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• The Journal of Small Business Innovation
• /
• v.19 no.2
• /
• pp.53-68
• /
• 2016

Motivation and activities for technological learning, entrepreneurship, innovation, and creativity are driving forces of economic development in Asian countries. In the early stages of technological development, technological learning and entrepreneurship are efficient ways in which to catch up with advanced countries because firms can accumulate skills and knowledge quickly at relatively low risk. In the later stages of technological development, however, innovation and creativity become more important. This study aims to identify a) the factors (learning capabilities) that influence technological learning performance and b) barriers to enhancing innovation capabilities for the creative economy and organizations. The major part of this study is related to learning capabilities in the post-catch-up era. Based on a literature review and observations from Korean experiences, this study proposes a technological learning model composed of various influencing factors on technological learning. Three hypotheses are derived, and data are collected from Korean machine tool manufacturers. Intense interviews with CEOs and R&D directors are conducted using structured questionnaires. Statistical analysis, such as correlation and ANOVA are then carried out. Furthermore, this study addresses how to enhance innovation capabilities to move forward. Innovation enablers and barriers are identified by case studies and policy analysis. The results of the empirical study identify several levels of firms' learning capabilities and activities such as a) stock of technology, b) potential of technical labor, c) explicit technological efforts, d) readiness to learn, e) top management support, f) a formal technological learning system, g) high learning motivation, h) appropriate technology choice, and i) specific goal setting. These learning capabilities determine firms' learning performance, especially in the early stages of development. Furthermore, it is found that the critical factors for successful technological learning vary along the stages of technology development. Throughout the statistical and policy analyses, this study confirms that technological learning can be understood as an intrinsic principle of the technology development process. Firms perform proactive and creative learning in the late stages, while reactive and imitative learning prevails in the early stages. In addition, this study identifies the driving forces or facilitating factors enhancing innovation performance in the post catch-up era. The results of the preliminary case studies and policy analysis show some facilitating factors such as a) the strategic intent of the CEO and corporate culture, b) leadership and change agents, c) design principles and routines, d) ecosystem and collaboration with partners, and e) intensive R&D investment.

• Journal of Korea Society of Industrial Information Systems
• /
• v.24 no.1
• /
• pp.45-63
• /
• 2019

The number of domestic construction company is expanding every year while the construction workers' exposure to disaster risk is increasing due to technological advancements and popularity of high-rise buildings. In particular, the industry faces greater fatalities and severe large scale accidents because of construction industry characteristics including influx of foreign workers with different language and culture, large number of aged workers, outsourcing, high place work, heavy machine construction. The construction industry is labor-intensive, which is to be completed under given timeline and consists of unique working environment with a lot of night shifts. In addition, when a fixed construction budget is not secured, there is less investment in safety management resulting in poor risk management at the construction site. Taking account that the construction industry has higher accident risk rate and fatality rate, risky and unique working environment, and various labor pool from foreign to aged workers, preemptive safety management through risk factor identification is a mandatory requirement for the construction industry and site. The study analyzes about 8,500 cases of construction accidents that occurred over the past 10 years and identified risk factor by construction industry sector to secure a systematic insight for risk management. Based on interrelation analysis between accident types, work types, original cause materials and assailing materials, there is correlation between each analysis factor and work industry. Especially for work types, there is great correlation between work tasks and industry type. For reinforced concrete and earthwork are among the most frequent types of accidents, and they are not only high in frequency of accidents, but also have a high risk in categories of occurrence.