• Journal of Korean Society for Geospatial Information Science
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• v.11 no.4 s.27
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• pp.3-11
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• 2003

Lately, digital photogrammetry based on the principles of photographic suey has been more and more applied to various high-tech industries and becomes one of more interesting focuses of study than ever. Thus, this study aims to develop a roadway information system by means of digital photogrammetry. Data acquired from digital photogrammetry were processed via Delphi, an object-oriented programming language to develop a computer aided program that allows us to build up the information on road horizontal alignment(BC, EC, R. IP), road vertical alignment and road facilities. And the developed program could maximize the visual effects better than traditional programs, because it used many image data.

• Health Policy and Management
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• v.32 no.3
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• pp.272-281
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• 2022

With the outbreak of coronavirus disease 2019 (COVID-19) pandemic, health policymakers are adopting new policies regarding the issue of immunization disparities, especially for children in low-income communities of color who lack awareness and thereby access to vaccines. The purpose of this paper is to propose an evaluation framework using program theory-based evaluation approach and logic model to analyze and evaluate the immunization disparities in children aged 19-35 months. Data is collected from New York City department of Health and the U.S. Census Bureau for Northern Manhattan Start Right Coalition program which consists of 19,800 children, and the community-provider partnership includes 26 practices and 20 groups. Program theory is used to evaluate this community-based initiative with the logic model which is a visual depiction that illustrations the program theory to all stakeholders. The logic model highlights the resources, activities, outputs, outcomes, and impacts of the program to guide to planners and evaluators and to call attention to the inadequacies or flaws in the operational, implementation and service delivery process of the program in offering a new perspective on the program. This framework adds to the literature on evaluations of immunization disparities in determining whether evaluators can definitively attribute positive immunization outcomes in the community to the program and conclude whether it has potential in expanding or duplicating it to other similar settings, especially in other rural areas of the United States, and abroad, where routine immunization equity gaps are wide due to income, racial and ethnic diversity, and language barrier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.231-244
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• 2022

With the improvement of people's living standards and the development of tourism, tourists have greater freedom in choosing destinations. Therefore, as an indicator of satisfaction with scenic spots, tourist comments are becoming increasingly prominent. This paper aims to compare and analyze the landscape image of the Five Great Mountains in China and provide specific strategies for its development. The online reviews of tourists on the Online Travel Agency (OTA) website about the Five Great Mountains from 2015 to 2018 are collected as research samples. The text analysis method and R language are used to analyze the content of the tourist reviews, while the high-frequency words in the word cloud are used for visual display. In addition, the entropy weight method is used to determine the index weight and tourist satisfaction is evaluated to understand the weaknesses of those scenic spots. The results of the study show that firstly, the tourist satisfaction with the Five Great Mountains is basically consistent with its popularity. Secondly, through weight analysis, tourists pay special attention to the landscape features and environmental health of the scenic area, so that relevant departments should focus on building the landscape characteristics and improving the environmental health of the scenic area. At the same time, the accommodation and service management of the scenic spot cannot be ignored. Finally, according to the analysis results, suggestions are made on how to improve the tourist satisfaction with the Five Great Mountains.

• Annals of Clinical Neurophysiology
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• v.23 no.2
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• pp.69-81
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• 2021

Cortico-cortical evoked potential (CCEP) mapping is a rapidly developing method for visualizing the brain network and estimating cortical excitability. The CCEP comprises the early N1 component the occurs at 10-30 ms poststimulation, indicating anatomic connectivity, and the late N2 component that appears at < 200 ms poststimulation, suggesting long-lasting effective connectivity. A later component at 200-1,000 ms poststimulation can also appear as a delayed response in some studied areas. Such delayed responses occur in areas with changed excitability, such as an epileptogenic zone. CCEP mapping has been used to examine the brain connections causally in functional systems such as the language, auditory, and visual systems as well as in anatomic regions including the frontoparietal neocortices and hippocampal limbic areas. Task-based CCEPs can be used to measure behavior. In addition to evaluations of the brain connectome, single-pulse electrical stimulation (SPES) can reflect cortical excitability, and so it could be used to predict a seizure onset zone. CCEP brain mapping and SPES investigations could be applied both extraoperatively and intraoperatively. These underused electrophysiologic tools in basic and clinical neuroscience might be powerful methods for providing insight into measures of brain connectivity and dynamics. Analyses of CCEPs might enable us to identify causal relationships between brain areas during cortical processing, and to develop a new paradigm of effective therapeutic neuromodulation in the future.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.119-124
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• 2010

In this paper planed mobile shopping system using MVLS. MVLS is based on braille system, and list this to a square color box. This is can accomplish convenience of use and encipherment of fundamental personal information. Also, this system has advantage to fast communication speed because that has little size packets. Planed system does and fit this in shopping to graft together mobile-phone, smart-phone and tablet-pc that the such MVLS and most of internal adult are possessing. User can more fast finish shopping just photographing and press the button that show on TV or PC monitor and printed materials etc. by camera of mobile phone.

• Journal of Fashion Business
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• v.27 no.5
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• pp.78-92
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• 2023

Fashion trends are actively disseminated through social media, which influences both their propagation and consumption. This study explored how users perceive subversive basic fashion in social media videos, by examining the associated concepts and characteristics. In addition, the factors contributing to the style's social media dissemination were identified and its distinctive features were analyzed. Through text mining analysis, 80 keywords were selected for semantic network and CONCOR analysis. TF-IDF and N-gram results indicate that subversive basic fashion involves transformative design techniques such as cutting or layering garments, emphasizing the body with thin fabrics, and creating bold visual effects. Topic modeling suggests that this fashion forms a subculture that resists mainstream norms, seeking individuality by creatively transforming the existing garments. CONCOR analysis categorized the style into six groups: forward-thinking unconventional fashion, bold and unique style, creative reworking, item utilization and combination, pursuit of easy and convenient fashion, and contemporary sensibility. Consumer actions, linked to social media, were shown to involve easily transforming and pursuing personalized styles. Furthermore, creating new styles through the existing clothing is seen as an economic and creative activity that fosters network formation and interaction. This study is significant as it addresses language expression limitations and subjectivity issues in fashion image analysis, revealing factors contributing to content reproduction through user-perceived design concepts and social media-conveyed fashion characteristics.

• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.81-93
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• 2022

As deep learning technology in natural language and visual processing has rapidly developed, collaborative filtering-based recommendation systems using deep learning technology are being actively introduced in the recommendation field. In this study, OCF-GAN, a hybrid collaborative filtering model using GAN, was proposed to solve the one-class and cold-start problems, and its usefulness was verified through performance evaluation. OCF-GAN based on conditional GAN consists of a generator that generates a pattern similar to the actual user preference pattern and a discriminator that tries to distinguish the actual preference pattern from the generated preference pattern. When the training is completed, user preference vectors are generated based on the actual distribution of preferred items. In addition, the cold-start problem was solved by using a hybrid collaborative filtering recommendation method that additionally utilizes user and item profiles. As a result of the performance evaluation, it was found that the performance of the OCF-GAN with additional information was superior in all indicators of the Top 5 and Top 20 recommendations compared to the existing GAN-based recommender. This phenomenon was more clearly revealed in experiments with cold-start users and items.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.645-652
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• 2024

Building Information Modeling (BIM) involves the integration of equipment information and component parameters across various engineering disciplines. The complex processes during model construction can lead to human errors. Furthermore, design changes often occur at various stages of the building's lifecycle, requiring designers and modelers to make timely modifications, resulting in significant costs and time consumption. Mechanical, electrical, and plumbing (MEP) design is considerably more complex than architectural design. Therefore, this study focuses on the automatic generation of a heating, ventilation, and air conditioning (HVAC) ductwork model with MEP design through BIM. Dynamo, a visual programming language (VPL), offers features such as arrangement, connectivity, and scalability. Thus, this research applied Dynamo to develop the Automatic Ductwork BIM Model Generation System. The BIM model generated by the system facilitates collaborative efforts and enables the analysis of HVAC System Conflicts. The system extracts coordinates for air handling units, supply air, and exhaust air outlets. The equipment is automatically positioned based on these coordinates, and the corresponding duct paths are generated by reading CAD files. At each duct connection point, appropriate fittings are fabricated according to specifications and dimensions. The duct system is configured with distinct colors, and the results are visualized in Revit, facilitating HVAC system clash detection in the future. This study undertakes a real project to validate the proposed system and processes and assesses its impact on modeling efficiency, real-time responsiveness, and accuracy, realizing automated ductwork generation.