• Journal of the Korean Geotechnical Society
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• v.38 no.4
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• pp.21-32
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• 2022

Steel pile foundations are widely used for offshore constructions due to their high bearing capacity and efficiency. Typically, offshore structures that have reached the end of their design life are required to be demolished. However, pile foundations are often left on site due to technical and economic limitations. The pile left on the site not only pollutes the environment, but can also cause obstacles for the construction of new structures. Therefore, research is required to completely eliminate these foundations at the site. In this study, a new type of double-wall pile foundation that can drastically reduce the pull-out load was conceptually proposed, and a series of model tests were performed to validate the performance of the double-wall pile foundation. The installation and extraction of the double-wall pile were simulated in dry sand in the model test, and the measured up-lift load was compared to that of the conventional pile. According to the result, the maximum up-lift load induced by the decommissioning of the double-wall pile was reduced by 45% when compared to the traditional pile in dense sand. This study verified the mechanism for reducing the up-lift load of the double-wall foundation and confirmed the possibility of completely decommissioning a pile that has reached the end of its nominal service life.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.405-412
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• 2022

This paper studies the method of estimating power loss and classifying the factors for improving the power generation efficiency through O&M. It is installed under various climatic conditions worldwide, operational and maintenance technologies suitable for the characteristics of the installation location are required. Existing studies related to solar power generation efficiency have been actively quantifying the impact on short-term losses by environmental factors such as high temperature, dust accumulation, precipitation, humidity, and wind speed, but analysis of the overall impact from a long-term operation perspective is limited. In this study, the potential for efficiency improvement was analyzed by re-establishing a loss classification system according to the power flow of solar power to derive a comprehensive efficiency model for long-term operation and estimating power loss through a case study for each region where climate conditions are classified. As a result of the analysis, the average annual potential for improving soiling loss was 26.9%, Death Valley 7.2%, and Seoul 3.8%. Aging losses was 6.6% in the 20th year as a cumulative. The average annual potential due to temperature loss was 2.9 % for Doha, 1.9% for Death Valley, and 0.2% for Seoul.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.38-49
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• 2021

This study is single-group pretest-posttest design study to examine the effects of web-based cognitive training program using tablet on cognition and depression in the elderly with high risk of dementia or mild dementia living in a rural area, enrolled in dementia center. Intervention was provided to the 18 participants once a week for 10 weeks within 1 hour. Data was analyzed with SPSS 24.0 and interview data was categorized. The study result proves that after intervention, the participants' cognitive score increased significantly(Z=-3.35, p=.001) and the depression scores were significantly decreased(Z=-3.13, p=.002). Also, interview shows positive effect of the intervention on cognition and depression. It is necessary to improve access environment for smart devices so as not to be restricted by time and place, and to develop and apply various types of web-based programs for each cognitive level. Then, the intervention could be used as a cognitive training program incorporating information and communication technology for the prevention and management of dementia in rural areas.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.171-177
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• 2022

In this paper, we propose a service method that can provide insight into multi-source agricultural data, way to cluster environmental factor which supports data analysis according to time flow, and curate crop environmental factors. The proposed curation service consists of four steps: collection, preprocessing, storage, and analysis. First, in the collection step, the service system collects and organizes multi-source agricultural data by using an OpenAPI-based web crawler. Second, in the preprocessing step, the system performs data smoothing to reduce the data measurement errors. Here, we adopt the smoothing method for each type of facility in consideration of the error rate according to facility characteristics such as greenhouses and open fields. Third, in the storage step, an agricultural data integration schema and Hadoop HDFS-based storage structure are proposed for large-scale agricultural data. Finally, in the analysis step, the service system performs DTW-based time series classification in consideration of the characteristics of agricultural digital data. Through the DTW-based classification, the accuracy of prediction results is improved by reflecting the characteristics of time series data without any loss. As a future work, we plan to implement the proposed service method and apply it to the smart farm greenhouse for testing and verification.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.82-92
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• 2022

This study aims to investigate the effect of total electrochemical cell resistance (TECR) on electrochemical impedance (EI) measurements of reinforced concrete (RC) by electrochemical impedance spectroscopy (EIS) using a three-electrode system. A series of experimental study is performed to measure electrochemical behavior of a steel bar embedded in a concrete cube specimen, with a side length of 200 mm, in various experimental conditions. Main variables include concrete dry conditions, coupling resistance between sensing electrodes and concrete surface, and area of the counter electrode. It is demonstrated that EI values remains stable when the compliant voltage of a measuring device is sufficiently great compared to the potential drop caused by TECR of concrete specimens. It is confirmed that the effect of the coupling resistance of TECR is far more influential than other two factors (concrete dry conditions and area of the counter electrode). The results in this study can be used as a fundamental basis for development of a surface-mount sensor for corrosion monitoring of reinforced concrete structures exposed to wet-and-dry cycles under marine environment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.1-13
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• 2022

This study aims to investigate the feasibility of the half-cell potential (HCP) measurements on the concrete surface for evaluation of corrosion rate (or corrosion levels) of reinforcing steel in concrete. A series of experimental study is performed to measure HCP (or corrosion potential, E_corr) and corrosion current density (i_corr) of reinforcing steel in concrete cube specimens, with a side length of 200 mm. Various corrosion levels in a range of 0% to 20% of the test specimens are accelerated by impressing current to the reinforcing steel in concrete immersed in 3.0 % NaCl solution. HCP is measured in accordance with ASTM C876-15, and corrosion current density is determined by using the Stern-Geary equation and measured polarization resistance measured by electrochemical impedance spectroscopy (EIS). As a result, a numerical formula that relates HCP and i_corr in the test specimen is established by a regression analysis of the measured data in this study. It is observed that HCP is linearly correlated with log(i_corr) with a R² greater than 0.87, which is less affected by the experimental variables such as concrete mixture proportion, diameter of reinforcing steel and the amount of applied current in this study. These results exhibit that HCP measurements could be effective for evaluation of corrosion rate (or corrosion levels) of reinforcing steel in concrete in the case of exposed to a certain consistent environment.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.123-136
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• 2021

Teddy bear is a typical rag doll which has been loved by people all over the world for more than 100 years based on its cute and friendly image. In addition, as it has been together for a long time with us, it is considered as a friend of people with memories of all ages and sexes, not just animal doll. Teddy bear has been developing its appearance and character continually playing a role as the symbol of society and issues of an era beyond toys, however it still remains in the image of stuffed toys. Therefore, more advanced teddy bear characters should be created in line with the current environment and market conditions that are undergoing major changes based on the Internet and smart phones. Thus, the concept of the character and the recent development of the market were reflected and the meaning and current image of teddy bears were analyzed to develop new teddy bear stories, worldviews, and characters through design process. And it was created 3D characters, videos, and SNS channels through the developed 3D character design and motion design. Furthermore, we want to take a look at the direction in which Korea's character business can develop in accordance with global changes and suggest the possibility of entering as a character powerhouse.

• Journal of Korean Elementary Science Education
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• v.40 no.4
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• pp.443-459
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• 2021

In this study, a TPACK-based STEAM program was developed and applied under the theme of excretory organs in the 'Structure and Function of Our Body' of the elementary science curriculum. The program was produced and conducted through curriculum analysis and learning goal detailing, learning environment analysis, teaching·learning method and technology selection, TPACK elements arrangement and teaching·learning material development, application and effectiveness verification. Teacher's TPACK considered in STEAM program design process is content knowledge (appearance and work of excretory organs), pedagogical knowledge (STEAM, problem-based learning, research learning, discussion learning, cooperative learning, scientific writing) and technology knowledge (3D printer and smart device application technology). The program consisted of a total of 8 hours of project learning activities and was applied to 29 students in the fifth grade as an experimental group. A program of the same theme developed mainly from textbooks was applied to 27 students in the fifth grade of a comparison group. As a result of the application of the program, the experimental group showed significant improvement in creative problem-solving ability and scientific attitude compared to the comparison group, and the class satisfaction with the STEAM program was also high. However, there was no significant difference in academic achievement ability.