• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.9-16
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• 2023

Technological advancement is being advanced in sports such as electronic protection of taekwondo competition and VAR of soccer. However, a person judges and guides the posture by looking at the posture, so sometimes a judgment dispute occurs at the site of the competition in Taekwondo Poomsae. This study proposes an artificial intelligence model that can more accurately judge and evaluate Taekwondo movements using artificial intelligence. In this study, after pre-processing the photographed and collected data, it is separated into train, test, and validation sets. The separated data is trained by applying each model and conditions, and then compared to present the best-performing model. The models under each condition compared the values of loss, accuracy, learning time, and top-n error, and as a result, the performance of the model trained under the conditions using ResNet50 and Adam was found to be the best. It is expected that the model presented in this study can be utilized in various fields such as education sites and competitions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.17-25
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• 2023

In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.

• Smart Structures and Systems
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• v.33 no.1
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• pp.1-16
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• 2024

The investigation on vehicle-bridge coupling system (VBCS) is crucial in bridge design, bridge condition evaluation, and vehicle overload control. A real-time hybrid testing (RTHT) method for VBCS (RTHT-VBCS) is proposed in this paper for accurately and economically disclosing the dynamic performance of VBCSs. In the proposed method, one of the carriages is chosen as the experimental substructure loaded by servo-hydraulic actuator loading system in the laboratory, and the remaining carriages as well as the bridge structure are chosen as the numerical substructure numerically simulated in one computer. The numerical substructure and the experimental substructure are synchronized at their coupling points in terms of force equilibrium and deformation compatibility. Compared to the traditional iteration experimental method and the numerical simulation method, the proposed RTHT-VBCS method could not only obtain the dynamic response of VBCS, but also economically analyze various working conditions. Firstly, the theory of RTHT-VBCS is proposed. Secondly, numerical models of VBCS for RTHT method are presented. Finally, the feasibility and accuracy of the RTHT-VBCS are preliminarily validated by real-time hybrid simulations (RTHSs). It is shown that, the proposed RTHT-VBCS is feasible and shows great advantages over the traditional methods, and the proposed models can effectively represent the VBCS for RTHT method in terms of the force equilibrium and deformation compatibility at the coupling point. It is shown that the results of the single-degree-of-freedom model and the train vehicle model are match well with the referenced results. The RTHS results preliminarily prove the effectiveness and accuracy of the proposed RTHT-VBCS.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.83-88
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• 2024

In this research, we propose a Semi-Supervised learning based railroad surface defect detection method. The Resnet50 model, pretrained on ImageNet, was employed for the training. Data without labels are randomly selected, and then labeled to train the ResNet50 model. The trained model is used to predict the results of the remaining unlabeled training data. The predicted values exceeding a certain threshold are selected, sorted in descending order, and added to the training data. Pseudo-labeling is performed based on the class with the highest probability during this process. An experiment was conducted to assess the overall class classification performance based on the initial number of labeled data. The results showed an accuracy of 98% at best with less than 10% labeled training data compared to the overall training data.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.845-849
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• 2024

Currently, rail surface defects are increasing due to the aging of urban railway rails, but in the detailed guidelines for track performance evaluation established by the country, rail surface damage is inspected with the naked eye of an engineer and with simple measuring tools. It is very important to discover defects in the rail surface through periodic track tours and visual inspection. However, evaluating the severity of defects on the rail surface based on the subjective judgment of the inspector has significant limitations in predicting damage inside the rail. In this study, the characteristics of cracks inside the rail due to rail surface damage were studied. In field measurements, rail surface damage was selected, old rail samples were collected in the acceleration and braking sections, and a scanning electron microscope (SEM) was used to evaluate the rail surface damage was used to analyze the crack characteristics. As a result of the analysis, the crack mechanism caused by the running train and the crack characteristics of the acceleration section where cracks occur at an angle rising toward the rail surface were experimentally proven.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Horticultural Science & Technology
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• v.32 no.6
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• pp.887-900
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• 2014

This study was conducted to provide a job analysis for, and assess the job performance of horticultural therapists, as well as examine future educational needs. To this end, a chart developed using the DACUM method was chosen as the appropriate tool for the job analysis of horticultural therapists (Study 1). Based on the chart, a survey using an evaluation form was produced to investigate the current level of job performance and future required level of horticultural therapists (Study 2). A total of 8 duties and 45 tasks were classified to examine job performance, based on analysis of the DACUM Council (Study 1). These duties include A. Decide execution organization for horticultural therapy (HT) program, B. Diagnose and assess clients before starting the HT program, C. Plan HT program, D. Develop HT program, E. Prepare to implement HT program for each session, F. Implement HT program for each session, G. Implement overall assessment for HT program, and H. Develop oneself as a horticultural therapist. Their duties were broken down further into five to eight tasks per duty, totaling 45 tasks. Based on the horticultural therapist job performance sheet developed through this process, an assessment of the current job level of horticultural therapists was performed and future required level were examined (Study 2). The evaluation forms were sent to 779 horticultural therapists with level 1 or 2 certification via email or mail delivery. The analysis of 242 questionnaires (31.1%) revealed that horticultural therapists with level 1 certificates have a significantly higher job performance level for 34 of the 45 tasks. Regarding future required level, 20 out of 45 tasks were assessed as higher for level 1 horticultural therapists than level 2. In addition, a Borich formula was utilized to identify the priority of educational needs for the 45 horticultural therapist tasks. The results revealed the following top three tasks: H1. Receive feedback from the supervisor for the horticultural therapy program; A1. Distribute promotional materials about the horticultural therapy program; and H2. Submit a grant proposal for horticultural therapy program to organizations such as welfare foundations. The results of this study are anticipated to facilitate understanding and improve work conditions for current horticultural therapists or horticultural therapists-in-training. In addition, institutions that train horticultural therapists will be able to use this as basic research to develop a practical training curriculum.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1701-1708
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• 2011

A typical day in the subway transportation is used by hundreds of thousands are also concerned about the safety of the various workrooms with high underground fire or other less than in the subway users could be damaging even to be raised and there. In 2010, in fact, room air through vents in the fire because smoke and toxic gas accident victims, and train service suspended until such cases are often reported. In response to these incidents in subway stations, even if the latest IT technology, wireless sensor network technology and intelligent video surveillance technology by integrating fire and structural integrity, such as a comprehensive integrated surveillance system to monitor the development of intelligent urban transit system and are under study. In this study, prior to the application of the monitoring system into the field stations, authors carried out the ZigBee-based wireless sensor networks performance analyzation in the Chungmuro station. The test results at a communications room and ventilation room of the station are summarized and analyzed.