• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.231-241
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• 2021

Although the deterioration of bridges may occur due to various causes, one of the representative causes is that the chloride used for deicing in the winter penetrates bridge members and results in corrosion. This study aims to quantify the ageing degree resulting from the corrosion of a bridge, apply it to the inelastic dynamic analysis model of the bridge, perform a seismic fragility analysis, and evaluate the relationship between the ageing degree and the seismic fragility curve. It is important to appropriately define the threshold values for each damage state in seismic fragility analyses considering the ageing degree. The damage state was defined using the results of existing experimental studies on the characteristics of the deterioration in the displacement ductility capacity of the pier, according to the ageing degree. Based on the seismic fragility analyses of six types of bridges divided by three types of bearing devices and two pier heights, it was found that the seismic vulnerability tends to increase with the ageing degree. The difference in seismic vulnerability with respect to the ageing degree exhibits a tendency to increase as the damage state progresses from slight to moderate, severe, and collapse.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1329-1341
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• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.308-318
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• 2022

The purpose of this study is to evaluate the biomechanical properties of fractured adjacent soft tissue during closed reduction after forearm fracture using the finite element method. To accomplish this, a finite element (FE) model of the forearm including soft tissue was constructed, and the material properties reported in previous studies were implemented. Based on this, nine finite element models with different fracture types and fracture positions, which are the main parameters, were subjected to finite element analysis under the same load and boundary conditions. The load condition simulated the traction of increasing the fracture site spacing from 0.4 mm to 1.6 mm at intervals of 0.4 mm at the distal end of the radioulnar bone. Through the finite element analysis, the fracture type, fracture location, and displacement were compared and analyzed for the fracture site spacing of the fractured portion and the maximum equivalent stress of the soft tissues adjacent to the fracture(interosseous membrane, muscle, fat, and skin). The results of this study are as follows. The effect of the major parameters on the fracture site spacing of the fractured part is negligible. Also, from the displacement of 1.2 mm, the maximum equivalent stress of the interosseous membrane and muscle adjacent to the fractured bone exceeds the ultimate tensile strength of the material. In addition, it was confirmed that the maximum equivalent stresses of soft tissues(fat, skin) were different in size but similar in trend. As a result, this study was able to numerically confirm the damage to the adjacent soft tissue due to the fracture site spacing during closed reduction of forearm fracture.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.394-401
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• 2021

The construction of new renewable energy facilities is steadily increasing every year. In particular, the offshore wind farm market, which has abundant development scalability and a high production coefficient, is growing rapidly. The southwest sea has the highest possible offshore wind power potential, and related projects are to be promoted. This study presents a basic design procedure by the EUROCODE and considers structural safety in the development of an effective of shore wind foundation in the clay layer. In a previous study, the wind power generator of 5MW class was the main target, but the 8MW of wind turbine generator, which meets the technical trend of the wind turbine market in the Southwest sea, was selected as the standard model. Furthermore, a foundation that fulfills the geological conditions of the Southwest sea was developed. The structural safety of this foundation was verified using finite element method. Moreover, structural safety was secured by proper reinforcement from the initial design. Based on the results of this study, structural safety check for various types of foundations is possible in the future. Additionally, specialized structural design and evaluation guidance were also established.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.642-657
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• 2022

This study aimed to investigate how elementary school students' science reading ability is influenced by collaborative problem-solving for competency instruction strategy using science reading text. This study recruited two groups of elementary students in fifth grade. The experimental group underwent an instruction strategy using science reading text, while the comparative group experienced a science class using a textbook. Afterward, data from the science reading ability tests, voice recordings of the discussion process involving each group, and class videos were collected and analyzed. The results showed that science classes that used collaborative problem-solving for their competency instruction strategy via science reading text were effective in enhancing elementary school students' science reading ability. Meanwhile, the science reading ability test results indicated that the experimental group had statistically higher total scores than the comparative group in the three subelements, especially "introspection and evaluation" and "integration and interpretation" owing to their significant improvement in high-level cognitive processes. In these classes, the students read the materials that the teacher provided, participated in the discussion based on what they have read, and had the chance to reflect on their reading processes. Overall, students' science reading ability was enhanced through this process.

• Journal of Industrial Convergence
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• v.20 no.5
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• pp.39-59
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• 2022

We analyze the perceptions and requirements of early childhood teachers for artificial intelligence(AI) education to develop an AI education program for 5-year-olds. As for the research methodology, we conducted a survey and an in-depth interview to extract the AI educational elements centering on the analysis stage, the first stage of the ADDIE model. The research result is that first, it is necessary to design a curriculum that combines the contents of early childhood education and AI education to be naturally accepted as AI education for 5-year-olds. Second, an evaluation tool for AI education that can showcase the teacher's reflection should be developed systematically. Third, it is necessary to support a play-centered AI education support and environment for early childhood teachers. Lastly, it is essential to establish a system that can be continuously operated in the field of early childhood education in consideration of AI education in the non-curricular curriculum. It is expected that in the future, a play-oriented AI education program for 5-year-olds will be developed to spread awareness of AI education for infants and present an AI education approach for each age and stage of learners.

• Journal of Science Education
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• v.46 no.1
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• pp.1-16
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• 2022

The purpose of this study is to explore learning progressions for global warming at middle school level. For this purpose, we conducted a construct modeling approach that specifies constructs, item designs, outcome spaces, and measurement model steps from April to October, 2021. In order to develop student assessment items, we analyzed the 2015 revised curriculum and textbooks of middle school and categorized a concept hierarchy for each construct to create a construct map. The assessment items were developed into multiple-choice, short answer, and essay questions according to the selected constructs to strengthen the linkage between the constructs and the items. Based on the three-step grading criteria for each item, an online assessment of 21 minor items developed for middle school students show that many students met 'high' level, but none met 'low' level. In this manner, the initial set lower anchor was reset to level 0, the original set upper anchor was lowered from level 4 to level 3, and the hypothetical learning progression for global warming was presented in the following order: phenomenal, conceptual, and mechanical understandings. The results of the research have raised implications for reorganizing the next science curriculum and improving the assessment system.

• Korean Association of Arts Management
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• no.54
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• pp.59-93
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• 2020

The development of digital technology has made consumers more knowledgeable about products than ever before. In this regard, experts have defined consumers in the digital age as knowledge seekers and pointed out that they are proficient in acquiring and sharing product knowledge prior to purchase. For service goods such as musicals, product prior knowledge executes strong effect due to such characteristics as intangibility, inseparabilit y,and heterogeneity. Prior studies in the field of performing arts, including musicals, have revealed that the prior knowledge of the audience has a positive effect on the satisfaction of the audience and WOM(word-of-mouth) intention. However, studies in the marketing field argue that consumers' high prior knowledge may have a negative impact on customer satisfaction and product evaluation depending on conditions, as they are more likely to compare, review, and expect products more closely. Therefore, in this study, we tried to identify under what conditions the musical prior knowledge enhances audience satisfaction and WOM intention. According to the results of previous studies, a mediating effect model was established in which the musical prior knowledge enhances the WOM intention through the mediation of the audience satisfaction. Then, Transportation was introduced as a mediation variable and it was verified whether the level changed the audience satisfaction and WOM intention. The reason for the introduction of the transportation construct reflects the results of previous research that the story is important component of musical. The results showed that the musical prior knowledge has a significant effect on WOM intention through the mediation of audience satisfaction. The moderating effect of transport was also significant on the relationship between musical prior knowledge and audience satisfaction. Lastly, through moderated-mediation analysis, it was confirmed that transportation moderates the mediating effect that prior knowledge affects on the WOM intention through audience satisfaction. Based on the results, we demonstrated that a musical story is fairly important to satisfy audiences with high prior knowledge. This study could contribute to the related filed in that it introduced the transportation construct for the first time, thereby broadening the understanding of the musical audiences in the era of smart consumers.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of Korean Society of Disaster and Security
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• v.15 no.3
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• pp.89-99
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• 2022

Bridges are a key infrastructure that underpins economic and social activities. In Korea, bridges began to be built with economic development in the 1970s and were built intensively in the 1980s and 1990s. In recent years, as the number of bridges with a service life of more than 30 years is increasing, continuous maintenance is required to ensure the safety of the bridges. In particular, in order to cope with the aging of bridges, research on technology development such as maintenance using ICT technology, preventive maintenance, life cycle cost reduction, and long life bridge is being actively promoted. This paper presents the results of correlation analysis based on the safety evaluation data of bridges as part of the research on the development of a model for estimating load-carrying capacity of bridges. As a analysis result, indicators highly correlated with the load-carrying capacity of the bridge was derived.