• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1807-1822
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• 2023

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution autoencoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.

• The Journal of Bigdata
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• v.7 no.1
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• pp.89-97
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• 2022

Various efforts are needed to prevent accidents because ship collisions can cause various negative situations such as economic losses and casualties. Therefore, research to prevent accidents is being actively conducted, and in this study, new leading indicators for preventing ship collision accidents is proposed. In previous studies, the risk of collision was expressed in consideration of the distance between ships in a specific sea area, but there is a disadvantage that a new model needs to be developed to apply this to other sea areas. In this study, the density-based ship domain DESD (Density-based Empirical Ship Domain) including the environment and operating characteristics of the sea area was defined using AIS (Automatic Identification System) data, which is ship operation information. Deep clustering is applied to two-dimensional DESDs created for each sea area to cluster the seas with similar operating environments. Through the analysis of the relationship between clustered sea areas and ship collision accidents, it was statistically tested that the occurrence of accidents varies by characteristic of each sea area, and it was proved that DESD can be used as a leading indicator of accidents.

• The Journal of Sustainable Design and Educational Environment Research
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• v.22 no.4
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• pp.1-8
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• 2023

The downsizing of schools is accelerating due to a rapid decline in the school-age population, and as the crisis over regional and school disappearance increases, the need for smaller schools to respond to future educational needs is increasing. Through flexible curricula and digital/artificial intelligence-based classroom teaching improvements, students' satisfaction with school life, student creativity and character development, improved academic achievement, and strengthened cooperative communication capabilities will be observed, and teachers' teaching and learning methods will change. Educational effects such as these are important, and transforming school facilities into future-oriented spaces, including school space innovation, is required to accomplish them. This study examined the future of education systems in small schools, focusing on analyzing the educational effects and awareness of the sustainability of spatial innovation, in terms of school space changes, school education correlation, and smart environment, to develop innovation projects in small schools. A desirable direction for implementation is presented.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• Wind and Structures
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• v.37 no.4
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• pp.303-314
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• 2023

In this study, two steady RANS turbulence models (SST k-ω and Realizable k-ε) and four unsteady turbulence models (URANS SST k-ω and Realizable k-ε, SST-SAS, and SST-IDDES) are evaluated with respect to their capacity to predict crosswind characteristics on high-speed trains (HSTs). All of the numerical simulations are compared with the wind tunnel values and LES results to ensure the accuracy of each turbulence model. Specifically, the surface pressure distributions, time-averaged aerodynamic coefficients, flow fields, and computational cost are studied to determine the suitability of different models. Results suggest that the predictions of the pressure distributions and aerodynamic forces obtained from the steady and transient RANS models are almost the same. In particular, both SAS and IDDES exhibits similar predictions with wind tunnel test and LES, therefore, the SAS model is considered an attractive alternative for IDDES or LES in the crosswind study of trains. In addition, if the computational cost needs to be significantly reduced, the RANS SST k-ω model is shown to provide relatively reasonable results for the surface pressures and aerodynamic forces. As a result, the RANS SST k-ω model might be the most appropriate option for the expensive aerodynamic optimizations of trains using machine learning (ML) techniques because it balances solution accuracy and resource consumption.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.33-39
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• 2023

Although COVID-19 has transitioned to a level 4 infectious disease in 2023 and has entered a stable trend, in special education settings, the importance of supporting the academic and social development gaps of students with disabilities caused by non-face-to-face learning situations such as remote classes during the COVID-19 period is emerging. there is. Accordingly, in this study, in order to identify and support the educational status and academic deficits of students with disabilities after COVID-19, we conducted a survey targeting 2,214 special education teachers in 17 cities and analyzed the results. As a result of the study, due to COVID-19, the developmental delay and educational gap in students with disabilities in terms of academics, emotions, and behavior deepened, and there was a high demand for manpower support, psychological counseling, and medical support for emotional behavior as a way to support this. Based on the results of this study, follow-up results were proposed.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.29-38
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• 2024

After Transcatheter Aortic Valve Replacement (TAVR), comprehensive management of complications, including the need for Permanent Pacemaker Implantation (PPI), is crucial, increasing the demand for accurate prediction models. Departing from traditional image-based methods, this study developed an optimal PPI prediction model based on ECG data using the XGBoost algorithm. Focusing on ECG signals like DeltaPR and DeltaQRS as key indicators, the model effectively identifies the correlation between conduction disorders and PPI needs, achieving superior performance with an AUC of 0.91. Validated using data from two hospitals, it demonstrated a high similarity rate of 95.28% in predicting PPI from ECG characteristics. This confirms the model's effective applicability across diverse hospital data, establishing a significant advancement in the development of reliable and practical PPI prediction models with reduced dependence on human intervention and costly medical imaging.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.177-188
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• 2024

In this research, a cohort of two children, aged 7-8 years, was selected to participate in a specialized three-week training program aimed at enhancing their working memory. The program consisted of three sessions, each lasting approximately 30 minutes. The primary goal was to investigate the impact and developmental trajectory of working memory in school-aged children. Working memory plays a significant role in young children's learning and daily activities. To address the needs of this demographic, products should offer both educational and enjoyable activities that engage working memory. Digital educational tools, known for their flexibility, are suitable for both older individuals and young children. By updating software or modifying content, these tools can be effectively repurposed for young learners without extensive hardware changes, making them both cost-effective and practical. For example, memory training games initially designed for older adults can be adapted for young children by altering images, music, or storylines. Furthermore, incorporating elements familiar to children, like animals, toys, or fairy tales, can increase their engagement in these activities. Historically, working memory capabilities have been assessed predominantly through traditional intelligence tests. However, recent research questions the adequacy of these behavioral measures in accurately detecting changes in working memory. To bridge this gap, the current study utilized electroencephalography (EEG) as a more sophisticated and precise tool for monitoring potential changes in working memory after the training. The research findings were revealing. Participants showed marked improvement in their performance on n-back tasks, a standard measure for evaluating working memory. This improvement post-training strongly supports the effectiveness of the training program. The results indicate that such targeted and structured training programs can significantly enhance the working memory abilities of children in this age group, providing promising implications for educational strategies and cognitive development interventions.

• Journal of the Korean Society of Earth Science Education
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• v.17 no.1
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• pp.1-19
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• 2024

This study is a design and development study that developed instructional strategies based on distributed cognitive theory for science classes using technology according to procedures that ensured reliability and validity. To develop instructional strategies, development study and validation study were conducted according to design and development research methodology procedures. In the development study, an initial instructional strategy was developed through prior literature review and prior expert review. In the validation study, the instructional strategy was validated using internal validation (expert validation, usability evaluation) and external validation (field application evaluation) methods, and the final instructional strategy was developed. The final instructional strategy consisted of 3 instructional principles, 9 instructional strategies, and 38 detailed guidelines. Through this study, the researcher suggested the suitability of instructional strategies for science classes using technology, the usefulness of blocks and teaching and learning processes, the possibility of using technology as a cognitive tool, the need for teachers' efforts to cultivate teaching capabilities using technology, and the needs lesson plan that takes into account conditions affecting the application of instructional strategies.

• Journal of Korean Academy of Nursing
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• v.22 no.2
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• pp.207-226
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• 1992

This study was designed to develop a conceptual framework for the curriculum and develop the details of the learning content for the education of Community Health Practitioners (CHPs). Since education programs for CHPs started 10 years ago, concepts related to CHP services have changed because of changes in society. The objectives of the study were as follows : 1) to analyse the usefulness of the present education program for CHPs, 2) to analyse the Job performance and self -confidence of the CHPs, 3) to identify the health needs of the clients served by the CHPs and the community problems related to health. 4) to develop a conceptual framework for the curriculum, for the education of CHPs, 5) to develops details for the learning content of the education program for CHPs. Phase I of the study was conducted by questionnaires to 150 CHPs who have worked in remote rural areas for more than 2 years. Among them, 147 responded. Data was collected from August 16, to August 25, 1990. In order to identify the health needs of the community people, research within the last five years was reviewed and analyzed. The data on 1, 842 communities gathered by the WHO Nursing Collaborations Center of the College of Nursing, Yonsei University was utilized to identify community problems related to health and the self - confidence in job performance of the CHPs. Psase II of the study consisted of a workshop with 13 professionals including Community Health Practitioners to evaluate the existing education program and a conceptual framework of the curriculum for the job education of CHPs. The results of the study are Summariged below : 1. The only 26 among 45 content items of the education program related to job skills was used by 80% of the responding CHPs. The knowledge of $\ulcorner$Networking community organization$\lrcorner$ was used by only 53.7% of the respondents. Educational content about $\ulcorner$Mental disease$\lrcorner$ was used by less than 50% of CHPs because of a knowledge deficit. 2. The CHPs reported that their activities concentrated on clinical services during the last six months. The survey showed that they seemed to neglect the activities for health promotion and disease prevention. Thus, $\ulcorner$Education for community loaders$\lrcorner$(15.9%), $\ulcorner$Activity for eavironmental health$\lrcorner$(16.3%) and $\ulcorner$Social work for needey people$\lrcorner$(23.3%) were done by less than 30% of CHPs. 3. More than 90% of CHPs reported being self - confident for the activities of $\ulcorner$Health education and counselling$\lrcorner$, $\ulcorner$Medicine prescription$\lrcorner$ and $\ulcorner$Immunization$\lrcorner$. But 50% of CHPs reported that they were not have self - confident in $\ulcorner$Management of water and environmental health$\lrcorner$ and only 25.6% of CHPs could insert an IUD independently. 4. It was identified that respiratory diseases and the gastrointestinal diseases were most common problems for the community people, followed by musculoskeletal and skin problems. 5. The community problems were classified into eight categories : physical environmental problems, environmental hygiene, health problems, health behavior, social problem, lack of resources, financial problem and the problems of the cultural and value system. 6. The conceptual framework consisted of the target population and their health status, nursing process working site and primary health care services such as health promotion, disease prevention, treatment and rehabilitation. 7. The contents of curriculum of education program for CHPs were formulated from the results of this study.