• Journal of Korean language education
• /
• v.29 no.3
• /
• pp.325-344
• /
• 2018

The purpose of this study is to summarize PWIM (Picture Words Inductive Model) which is one of learner-centered vocabulary teaching-learning models, and suggest ways to implement them in Korean language education. The pictures that are used in the Korean language education field help visualize the specific shape, color, and texture of the vocabulary that is the learning target; thus, helping beginner learners to recognize the meaning of the sound. Visual material stimulates the intrinsic schema of the learner and not only becomes a 'bridge' connecting the mother tongue and the Korean language, but also reduces difficulty in learning a foreign language because of the ambiguity between meaning and sound in Korean and all languages. PWIM shows commonality with existing learning methods in that it uses visual materials. However, in the past, the teacher-centered learning method has only imitated the teacher because the teacher showed a piece-wise, out-of-life photograph and taught the word. PWIM is a learner-centered learning method that stimulates learners to find vocabulary on their own by presenting visual information reflecting the context. In this paper, PWIM is more suitable for beginner learners who are learning specific concrete vocabulary such as personal identity (mainly objects), residence and environment, daily life, shopping, health, climate, and traffic. The purpose of this study was to develop a method of using PWIM suitable for Korean language learners and teaching procedures. The researchers rearranged the previous research into three steps: brainstorming and word organization, generalization of semantic and morphological rules of extracted words, and application of words. In the case of PWIM, you can go through all three steps at once. Otherwise, it is possible to divide the three steps of PWIM and teach at different times. It is expected that teachers and learners using the PWIM teaching-learning method, which uses realistic visual materials, will enable making an effective class together.

• Journal of the Semiconductor & Display Technology
• /
• v.22 no.1
• /
• pp.11-17
• /
• 2023

This paper proposes a lightweight deep learning network for detecting an image splicing forgery. The research on image forgery detection using CNN, a deep learning network, and research on detecting and localizing forgery in pixel units are in progress. Among them, CAT-Net, which learns the discrete cosine transform coefficients of images together with images, was released in 2022. The DCT coefficients presented by CAT-Net are combined with the JPEG artifact learning module and the backbone model as pre-learning, and the weights are fixed. The dataset used for pre-training is not included in the public dataset, and the backbone model has a relatively large number of network parameters, which causes overfitting in a small dataset, hindering generalization performance. In this paper, this learning module is designed to learn the characterization depending on the DCT domain in real-time during network training without pre-training. The DCT RRU-Net proposed in this paper is a network that combines RRU-Net which detects forgery by learning only images and JPEG artifact learning module. It is confirmed that the network parameters are less than those of CAT-Net, the detection performance of forgery is better than that of RRU-Net, and the generalization performance for various datasets improves through the network architecture and training method of DCT RRU-Net.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.1220-1220
• /
• 2022

Constructionarium Ltd is a not-for-profit organisation which delivers a residential, experiential, immersive learning opportunity to university students from across the built environment education sector. Since 2002, the Constructionarium education model has been available to students in engineering, construction management and architecture at a purpose built, 19-acre multi-disciplinary training facility in Bircham Newton, England simulating real site life and reflecting site processes, practices and health and safety requirements. The unique approach of Constructionarium puts experiential learning and sustainability at the heart of everything. In a week, students develop a practical understanding of the construction process, develop transferable skills, build a team and are exposed to the latest in sustainable technologies. Experiential learning is what differentiates a Constructionarium project from regular field trips or site visits. At Constructionarium the focus is on learning by participation rather than learning through theory or watching a demonstration. The projects cannot be replicated in a classroom or on campus. Using the hands-on construction of scaled down versions of iconic structures from around the world, students learn that it requires the involvement of the whole construction team to successfully complete their project. Skills such as communication, planning, budgeting, time management and decision making are woven into a week-long interrelationship with industry professionals, academic mentors and trades workers. Working together to enhance transferable skills brings the educational environment into the reality of completing an actual construction project handled by the students. Constructionarium has used this transformational learning model to educate thousands of students from all over the United Kingdom, Europe and Asia. Texas A&M University in the United States has sent multiple teams of students from its Department of Construction Science every operational year since 2016.

• Human Ecology Research
• /
• v.61 no.4
• /
• pp.543-563
• /
• 2023

The purpose of this study is to design and develop a Metaverse ZEP platform-based teaching and learning process plan by selecting learning topics that are commonly dealt with among the core concepts of the "family" area of practical (technical and home) subjects. To this end, a teaching and learning process plan was developed through planning, Metaverse platform design, expert review, and revision stages. The Metaverse ZEP "Open Class Day" platform, a virtual learning space, was created and developed to further utilize EduTech programs, such as Padlet, Mentimeter, Jamboard, Miricanvas, and Spatial. The teaching and learning process plan developed in this study consists of a total of seven sessions, including approaching EduTech, Changing Families, Exploring Our Family, and Counseling Centers 1, 2, and 3. Among them, Geumji Counseling Center 1, 2, and 3 was designed as a class in which parents and children participate together in open classes using the ZEP platform. This platform can be used as part of parent classes as well as to encourage online participation in the open classes held periodically at each individual school. In terms of the content validity ratio (CVR) of the developed teaching and learning process verified through five experts, 12 out of 15 questions had a CVR of 1, while the remaining three questions had a CVR of 0.6. The three questions with lower validity were revised and supplemented.

• ETRI Journal
• /
• v.28 no.1
• /
• pp.59-66
• /
• 2006

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF-SVD-SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine-tuning of centers and widths still shows ill-behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center-gradient variance of the RBFN-SVD-SG algorithm. We found analytically that the steadystate weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center-gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady-state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.

• Smart Structures and Systems
• /
• v.30 no.3
• /
• pp.287-301
• /
• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.4
• /
• pp.270-278
• /
• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.403-411
• /
• 2017

In recent years, various activities have been carried out in terms of talent donation for community together. This study suggests curriculum model about talent donation program centered on beauty majors. The key elements revealed in previous literature studies were used as educational contents of this study. The program presented in this study can be used to expand talent donations by beauty talent. The curriculum of the program for community together consisted of talent donation goals, teaching and learning methods, and evaluation items. This training program is divided into understanding area of talent donation and technique area of skin beauty.

• World Technopolis Review
• /
• v.5 no.1
• /
• pp.61-70
• /
• 2016

Entrepreneurship development has increasingly become a global solution to address the problem of rising unemployment. Science, Technology and Innovation (STI) have become important tools in improving the economic performance and social well-being of nations. When universities and industry work together to push the boundaries of knowledge, they become a powerful engine for innovation and economic growth. This paper is based on focus group interviews and discussions conducted with key players involved in the HIT-Sandown-UNDP Business Incubation Programme in Harare Zimbabwe. The business incubation project sought to support young Zimbabweans to transform their technical prototypes into commercially and socially viable ventures. As a result, a total of 10 prototypes were refined and investor ready business plans were developed for capital sourcing purposes. It was only through the coming together of the partners that real transformation of the lives of the participants was achieved through learning valuable business skills, coaching and mentoring. University-industry partnerships are a useful vehicle of setting up sustainable business incubation centres.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.3
• /
• pp.364-372
• /
• 2016

This study examined the multi-phrased effects and outcomes of adult female learners who participated in lifelong learning activities, as well as the proposed structural relationships among the five latent variables. Questions established to achieve the purpose of the study are as follow: What effects do the learner's characteristics, lifelong education institutions, learning flow, and learning satisfaction have on the learning come? The participants of the survey numbered 632, but 54 respondents who were unreliable or did not complete their survey were excluded. A total of 578 cases were analyzed for this research. The structural relationships among the five latent variables-learner's characteristics, lifelong education institutions, learning flow and learning satisfaction, and learning outcome of the adult female learners-AMOS 18.0 program were also used for structural analysis. The major findings of this research are as follows. First, the model fitness showed that the hypothetical model provided a reasonable fit to the data ${\chi}^2=224.267$ (df=69, p<.001), RMSEA=.062, TLI=.943, RFI=.920, CFI=.957, IFI=.957, NFI=.939. Second, the learner's characteristics ( =.218, p<.001) and lifelong education institutions ( =.301, p<.001) have a direct effect on the learning outcomes. The learning flow ( =-.149 p=.541) does not have a direct effect on the learning outcome. Learning satisfaction ( =.405 p<.001) have a direct effect on the learning outcome. To put findings above together, in respect to adult female learners' performances, the learning outcomes are influenced directly by the learner characteristics, conditions of the lifelong education institutions, and learning satisfaction, whereas satisfaction indirectly affects the learners' learning outcome.