• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.28-34
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• 2022

This study analyzes the impact that accidents and near misses have on clients' and contractors' awareness of safety culture. Due to the unique characteristic of employment structure in Korea, the occurrence of accidents differs by company size, which has relevant implications for the establishment of safety culture. Attention has been drawn to the importance of the management of accidents and near misses, with safety awareness acting as a core factor. A positive effect on the prevention of accidents could be achieved by noting the difference in safety awareness between clients and contractors and suggesting an associated suitable safety management system. In support of this study, a survey was distributed to workers in the automobile manufacturing industry (May-August 2020), and data from a total of 574 workers was collected and analyzed, including 399 clients' worksers and 175 contractors' workers. The questionnaire addressed participants' experiences of accidents and near misses as well as 50 items from the Nordic Occupational Safety Climate Questionnaire. Analysis of the responses was conducted using the methods of frequency analysis, Fisher's exact test, t-test, correlation analysis, and regression analysis. The results demonstrated that clients had more experiences with accidents and near misses compared to contractors. Additional differences between clients and contractors were noted in terms of the safety culture factors of learning, communication, and trust. A correlation was observed between the experience of accidents and safety justice management: for clients and contractors who experienced accidents, safety justice management was 9.4 times higher. Furthermore, clients' and contractors' awareness of employees' commitment to safety was determined to be 28.5 times higher in those who had experienced near misses This study concludes that, in order to improve accident prevention through the management of accidents and near misses, clients must focus on overseeing safety justice management and aspects of safety culture factors, while contractors must focus efforts on managing employees' commitment to safety. In further applications, this study could provide baseline data for health and safety activities in terms of the safety culture of clients and contractors. Further study on the establishment of safety culture as related to employment structure is proposed for future research.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.74-99
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• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• Journal of Korean Elementary Science Education
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• v.42 no.3
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• pp.399-408
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• 2023

This study developed and applied a maker education program for an elementary school's science unit on 'Animal Life'. It examined the program's impact on students' academic achievement and creative problem-solving ability. The theme of the maker education program was 'creating a robot virtual reality (VR) exhibition hall mimicking animal characteristics'. It explored scientific concepts and creatively created a robot VR exhibition hall in accordance with the TMI maker education model. Findings revealed that the program significantly improved students' academic achievement and creative problem-solving ability (p<.05). This study provides evidence for the effectiveness of maker education in elementary school science classes and suggests that using maker education can increase students' interest in and engagement with science learning. To implement maker education more actively in elementary school science classes, stakeholders should develop various topics and programs. Additional research investigating the effectiveness of maker education in different age groups and various other areas of elementary science education is required to generalize the results of this study. Moreover, educational and teacher capacity building is required for educators to utilize maker education effectively.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Korean Journal of Comparative Education
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• v.24 no.6
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• pp.163-185
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• 2014

Along with concerns about deteriorating social and economic status of teachers around the world, Master Teacher System(MTS) has been considered as one of the alternatives to transform teaching profession into a more attractive job. In this study, the conditions and problems associated with the MTS in China is analyzed to draw implications for South Korea, which recently legalized the MTS. Research framework including four research questions is developed based on the controversies surrounding MTS of South Korea. The main findings show that the MTS in China was introduced to improve teachers' social and economic status along with the quality of prospective teachers. A very small number of master teachers are selected through rigorous standards including longer service period. They are given additional monetary and non-monetary compensations in return for their teaching-learning leadership and responsibilities. As highly respected educators, they enjoy the lifelong benefits, although they are annually evaluated. It is evident that the MTS has contributed to improving the attractiveness of teaching profession in China. Nevertheless, there are many problems associated with selection standards and methods of master teachers, their roles, compensation, evaluation and terms of service. Recent criticism due to changing circumstances surrounding education in China makes the MTS more questionable. Based on the findings, major implications for future directions of MTS of South Korea are drawn and suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.123-132
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• 2023

Git's commit messages manage the history of source changes during project progress or operation. By utilizing this historical data, project risks and project status can be identified, thereby reducing costs and improving time efficiency. A lot of research related to this is in progress, and among these research areas, there is research that classifies commit messages as a type of software maintenance. Among published studies, the maximum classification accuracy is reported to be 95%. In this paper, we began research with the purpose of utilizing solutions using the commit classification model, and conducted research to remove the limitation that the model with the highest accuracy among existing studies can only be applied to programs written in the JAVA language. To this end, we designed and implemented an additional step to standardize source change data into natural language using GPT. This text explains the process of extracting commit messages and source change data from Git, standardizing the source change data with GPT, and the learning process using the DistilBERT model. As a result of verification, an accuracy of 91% was measured. The proposed model was implemented and verified to ensure accuracy and to be able to classify without being dependent on a specific program. In the future, we plan to study a classification model using Bard and a management tool model helpful to the project using the proposed classification model.

• Economic and Environmental Geology
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• v.57 no.1
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.19-32
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• 2024

This study examined the feasibility of image-based surveys by detecting objects in facilities and agricultural land using the YOLO algorithm based on drone images and comparing them with the land category by law. As a result of detecting objects through the YOLO algorithm, buildings showed a performance of detecting objects corresponding to 96.3% of the buildings provided in the existing digital map. In addition, the YOLO algorithm developed in this study detected 136 additional buildings that were not located in the digital map. Plastic greenhouses detected a total of 297 objects, but the detection rate was low for some plastic greenhouses for fruit trees. Also, agricultural land had the lowest detection rate. This result is because agricultural land has a larger area and irregular shape than buildings, so the accuracy is lower than buildings due to the inconsistency of training data. Therefore, segmentation detection, rather than box-shaped detection, is likely to be more effective for agricultural fields. Comparing the detected objects with the land category by law, it was analyzed that some buildings exist in agricultural and forest areas where it is difficult to locate buildings. It seems that it is necessary to link with administrative information to understand that these buildings are used illegally. Therefore, at the current level, it is possible to objectively determine the existence of buildings in fields where it is difficult to locate buildings.