• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.11-18
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• 2022

As the need for software development manpower increases, various educational programs appear and the popularity of bootcamp style education program for software development increases. However, despite the operations and forms of bootcamp education programs are completely different from the existing software development education programs, there is a lack of research in understanding bootcamp as a software education program. Therefore, this study tried to derive the core elements of the education program through a case study on bootcamp software developer education program. After conducting interviews of 7 members who have completed a series of bootcamp software developer education program X, seven characteristics of bootcamp-type software development education program were derived: intensive theory education, sense of growth and achievement, team project-based learning, community characteristics, peer pressure, stress and fatigue due to short-term learning, and contact-free specialty. Based on the derived characteristics, the advantages and improvements of bootcamp-type education were described, and the direction of the bootcamp-type education program for software developer was discussed.

• Smart Structures and Systems
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• v.31 no.4
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• pp.393-407
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• 2023

To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BI-SMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.49-55
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• 2023

The evolution of new digital technologies is progressing rapidly. In particular, many changes in software and artificial intelligence are progressing rapidly in the field of education. The Ministry of Education is planning an educational program by linking software and artificial intelligence regular curriculum. Before applying it to regular subjects, various software and artificial intelligence related experience camps are being promoted. This study aims to construct an educational model for software and artificial intelligence education programs for high school students based on new digital technology. By expanding and distributing software and artificial intelligence education, we aim to enhance the basic capabilities of software and artificial intelligence for high school students. I would like to define the concept of software and artificial intelligence in high school and propose a model that links software and artificial intelligence learning factors to the regular curriculum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1759-1772
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• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.

• The Journal of Bigdata
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• v.7 no.1
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• pp.37-47
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• 2022

Autonomous delivery operation data is the key to driving a paradigm shift for last-mile delivery in the Corona era. To bridge the technological gap between domestic autonomous delivery robots and overseas technology-leading countries, large-scale data collection and verification that can be used for artificial intelligence training is required as the top priority. Therefore, overseas technology-leading countries are contributing to verification and technological development by opening AI training data in public data that anyone can use. In this paper, 326 objects were collected to trainn autonomous delivery robots, and artificial intelligence models such as Mask r-CNN and Yolo v3 were trained and verified. In addition, the two models were compared based on comparison and the elements required for future autonomous delivery robot research were considered.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Human Ecology Research
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• v.61 no.3
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• pp.349-360
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• 2023

This study aimed to develop, and evaluate the effectiveness of, a home economics dietary education program that could be used to improve high school students' sustainable dietary competencies. To achieve this goal, learning objectives and elements were selected for the education program to improve the students' sustainable dietary competency. The content validity of this program was verified with numerous experts. After implementing the new educational program, its effect on high school students' sustainable dietary competencies was assessed through pre- and post-tests. The results of this study are summarized as follows. First, through the textbook analysis, the necessity of developing an educational program that can cultivate the three areas that constitute a sustainable diet (environment, health, and consideration) was confirmed. Second, a total of nine teaching and learning plans for sustainable dietary education programs were developed. From there, content validity verification was conducted by experts, indicating that the developed educational program was highly applicable in the field. Third, after the implementation, a survey was conducted based on a questionnaire consisting of 20 items related to three areas of sustainable diet, while the pre- and post-test assessment indicated statistically significant differences in all three areas: environment (pre: 3.40, post: 4.46, p<.001), health (pre: 3.15, post: 4.32, p<.001), and consideration (pre: 3.46, post: 4.48, p<.001). It is expected that the educational program developed in this study will be used as a basis for dietary education that fosters food citizenship in high school home economics courses.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.431-441
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• 2023

In a good class, the elements that make up the class are organically related as a system. The goal of the class is to foster the ability of students to fully understand the educational content of the subject and then apply it to their professional areas. Therefore, for ideal classes, it is necessary to design students to acquire the necessary theories and apply them practically. The question We always ask ourselves during lectures is how to effectively give large-scale lectures for students. This is also the concern of all professors in charge of large-scale lectures opened across various major fields. Now is the time to find ways to effectively give lectures on a large scale. We studied how it is most effective to design and implement various factors such as lectures, presentation and group organization, assignment, group presentation, professor's group presentation guidance, lecture materials posting, questions and answers, group presentation feedback, final report writing, and grade calculation.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.167-186
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• 2008

This study is to investigate, from the perspective of implicit theory, what elements influence children's motivation to learn and how their configurations are different according to different sexes. One analysis was based on answers to a motivation questionnaire by fourth to sixth graders from four different cities in South Korea. The subjects children were most highly motivated to learn were math and science for boys, and math and English for girls, respectively. Factors influencing the motivation were near 30 in number, including later happier life, joy of learning, parental rewards, pleasure of being informed, and meeting parental expectations, among others. Another analysis was an exploratory and confirmative factor analysis on motivation to learn among 856 fourth to sixth graders randomly sampled from 7 different cities all over South Korea. Factors revealed to contribute to the motivated learning here were five factors of utility, interest, recognition, knowledge acquisition(being informed), and expectancy sufficiency. There were some differences in the structure of factors between sexes; importance was given to five factors of utility, interest, recognition, knowledge acquisition, and expectancy sufficiency in descending order for boys, and six factors of interest, utility, rewards, recognition, expectancy sufficiency, and competition for girls.

• Journal of KIBIM
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• v.13 no.4
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.