• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.643-656
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• 2024

The prediction of the moment rotation behavior of semi-rigid connections has been the subject of extensive research. However, to improve the accuracy of these predictions, there is a growing interest in employing machine learning algorithms. This paper investigates the effectiveness of using Multi-gene genetic programming (MGGP) to predict the moment-rotation behavior of flush-end plate connections compared to that of artificial neural networks (ANN) and previous studies. It aims to automate the process of determining the most suitable equations to accurately describe the behavior of these types of connections. Experimental data was used to train ANN and MGGP. The performance of the models was assessed by comparing the values of coefficient of determination (R²), maximum absolute error (MAE), and root-mean-square error (RMSE). The results showed that MGGP produced more accurate, reliable, and general predictions compared to ANN and previous studies with an R² exceeding 0.99, an RMSE of 6.97, and an MAE of 38.68, highlighting its advantages over other models. The use of MGGP can lead to better modeling and more precise predictions in structural design. Additionally, an experimentally-based regression analysis was conducted to obtain the rotational capacity of FECs. A new equation was proposed and compared to previous ones, showing significant improvement in accuracy with an R² score of 0.738, an RMSE of 0.014, and an MAE of 0.024.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.415-424
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• 2023

The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.903-910
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• 2024

Complex semantic segmentation tasks are primarily performed in server environments equipped with high-performance graphics hardware such as GPUs and TPUs. This cloud-based AI inference method operates by transmitting processed results to the client. However, this approach is dependent on network communication and raises concerns about privacy infringement during the process of transmitting user data to servers. Therefore, this paper proposes a Generalized On-Device Framework for Semantic Segmentation that can operate in mobile environments with high accessibility to people. This framework supports various semantic segmentation models and enables direct inference in mobile environments through model conversion and efficient memory management techniques. It is expected that this research approach will enable effective execution of semantic segmentation algorithms even in resource-constrained situations such as IoT devices, autonomous vehicles, and industrial robots, which are not cloud computing environments. This is expected to contribute to the advancement of real-time image processing, privacy protection, and network-independent AI application fields.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.5
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• pp.324-333
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• 2024

This paper focuses on the cooperation between Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vessel (USV). It aims to develop efficient guidance and control algorithms for USV based on obstacle identification and path planning from aerial images captured by UAV. Various obstacle scenarios were implemented using the Robot Operating System (ROS) and the Gazebo simulation environment. The aerial images transmitted in real-time from UAV to USV are processed using the computer vision-based deep learning model, You Only Look Once (YOLO), to classify and recognize elements such as the water surface, obstacles, and ships. The recognized data is used to create a two-dimensional grid map. Algorithms such as A^* and Rapidly-exploring Random Tree star (RRT^*) were used for path planning. This process enhances the guidance and control strategies within the UAV-USV collaborative system, especially improving the navigational capabilities of the USV in complex and dynamic environments. This research offers significant insights into obstacle avoidance and path planning in maritime environments and proposes new directions for the integrated operation of UAV and USV.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Korean Academy of Nursing
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• v.13 no.2
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• pp.22-43
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• 1983

1. Need for and Purpose of the Study. There is an increasing demand for curriculum improvement of the Korean Army Nursing Academy (KANA), since it was upgraded into 4-year institution of higher learning from 3-year one. In particular, it is strongly advocated that the KANA needs the outside expertise for its curriculum improvement-namely not only from the internal military view of points but also from the viewpoints of professional educational society, In line with such a necessity for the study, this study was aimed at 1) analyzing the current actual practices of KANA'S curriculum, 2) investigating the desired practices of KANA'S curriculum, and 3) identifying the discrepancy between the actual and desired practices of curriculum. 2. Problems for the Study This study had 4 problems to be answeared as follows: 1) What are the actual curriculum practices of KANA? 2) What are the desired curriculum practices of KANA? 3) How are the extents of perception of actual and desired curriculum different in four groups (student, faculty & administrator, nurse, and medical doctor in militay hospital) ? 4) What are the restraining forces that impede the change from actual to desired curriculum practices? 5) What are the relationships of curriculum component,』 in actual and desired curriculum practices? 3. Methods and Procedures This study was conducted by means of document analysis in addition to literature review and by means of needs assessment questionnaire which was developed by the researcher. The questionnaire included 62 statments with 7 questions for demographic data collection. The needs assessment questionnaire was managed to a total of 243 subjects (100 students, 46 faculty & administrators, 55 nurses, and 42 medical doctors), The collected data were treated using SPSS computer system so as to calculate mean scores, standard deviations, and correlation coefficients. The significance test was made through t-test and one-way ANOVA. The statistical significance level was set at both .05 and .01 level. 4. Major findings The major findings in this study are as follows: 1) The score of desired practices was significantly greater than that of actual practices, representing a strong need for curriculum betterment. 2) There were significant differences in the perceptions of actual practices as well as desired practices among four groups (student, faculty & administrater, nurse, and medical doctor). 3) The most frequently selected restraining forces were army's inherent character, economical limitation, and educational expertise limitations. 4) Such variables as sex, position attachment to the KANA and grade made a statistically significant effect on the perception of desired curriculum practice, while the variables like marrige, position, and military class made it on the perception of actual curriculum practice. 5) The coefficients among the curriculum components were lower in perception of the actual curriculum practices than those in the desired practices. 5. Conclusions The conclusions based on the major findings of this study are as follows: 1) The current curriculum development procedure of the KANA is not consistent with the theoretical frame of systematic development sarategy of curriculum. 2) There are wide conflicts among the groups who are supposed to participate in curriculnm development, concerning the actual and desired practices of KANN'S curriculum. 3) A great deal of need for curriculum improvement for the KANA is clearly felt, and in particular, in the process of teaching and learning. 4) Each component of curriculum is not intergrated into a whole development procedure, being segregated each other. 5) For better curriculum improvement, such restraining forces as financial and professional limitations should be eliminated. 6. Recommendations 1) For Further Research a. There is a need to replicate this study after in-depth statistical analysis of each item of need assessment questionnaire, and with more representative subjects. b. A study should be conducted which. has its focus on the analysis of restraining forces for the change from actual to desired curriculum practices of the KANA. 2) For KANA'S Curriculum Improvement a. There is a need to promote the professional expertise of the participants in curriculum development and the communication among them. b. It is desirable to establish an institution or section of administration, which is soley in charge of curriculum development. c. To better develop KANA's curriculum not only faculty and administrators but also students should be encouraged to participate in development process, while the military medical doctors' participation should be carefully considered.

• Archives of design research
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• v.18 no.2 s.60
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• pp.243-254
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• 2005

Great Britain and the United States and Finland are having an interest in long policy subject about child design education through early design education. And they approaches and practices it systematically. The research about the design learning program instance of advanced nation of primary school's design education for various objective is necessary for use with the fundamental reference data for an elementary design education. And so, This research presented the program instance investigation and analysis result of British primary school's design education. U.K is teaching an primary design education from two subjects of Art & Design and Design and Technology which is a legal subject with national curriculum. The analysis result of design relation unit learning program of two subjects is: Design relation unit learning programs of 'Design and Technology' subject's 20 unit which except 4 food relation unit is largely scientific engineering contents that include utility function contents in part. The reason is as behavior styles based on Design process solve problems scientifically & rationally. Design relation 6 units in subject of Art & Design which except the units which relates with the pure fine arts and architecture in 19 units is aesthetic-symbolic and utility-functional contents largely. And so, the result was analyzed about relation of scientific-engineering content of 'Arts & Design' subject is insufficient comparing with 'Design and Technology' subject Specially, I think that the design relation's unit learning program instances of 'Design and Technology' subject of the British primary school which have been presented by this research paper is a possibility becoming one reference model for a program development. And so I expects that this research could be applied in the program development for the primary design education of primary teacher & education agency.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Korea Science and Art Forum
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• v.23
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• pp.149-162
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• 2016

Here are background, method, scope, main contents of this research. As the interests increased in recent about the construction in complex and diverse areas, construction is locally connected to human life like to coexistence of the technology and culture. The local development should not be fragmentary construction to improve local recycling ability. Local society should be inherited by modern cultural perspective through a variety of local culture and coexistence. Effective decision making analysis is necessary to build a livable area with a combination of high-tech industry. For this reason, this paper will study the political analysis for decision making at the planning stage of construction in point of fusion of technology and culture by using unstructured data analysis. Conclusion is as in the following. Local planning stage of construction describes diverse meanings of intangible and intangible factors as political factor. Technology factors have various qualitative and quantitative factors in construction field. Understanding decision making at the planning stage of construction means not only visible 'technology factor' such as structure, method, shape, and so on, but also invisible 'culture factor' such as spirit of age, religion, learning, and life-style reflected in formation process of space, and insight of brain power about art.