• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.687-692
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• 2023

We analyze the accuracy of emotion analysis of natural language processing deep learning model and propose to use it for emotional content development. After looking at the outline of the GPT-3 model, about 6,000 pieces of dialogue data provided by Aihub were input to 9 emotion categories: 'joy', 'sadness', 'fear', 'anger', 'disgust', and 'surprise'. ', 'interest', 'boredom', and 'pain'. Performance evaluation was conducted using the evaluation indices of accuracy, precision, recall, and F1-score, which are evaluation methods for natural language processing models. As a result of the emotion analysis, the accuracy was over 91%, and in the case of precision, 'fear' and 'pain' showed low values. In the case of reproducibility, a low value was shown in negative emotions, and in the case of 'disgust' in particular, an error appeared due to the lack of data. In the case of previous studies, emotion analysis was mainly used only for polarity analysis divided into positive, negative, and neutral, and there was a limitation in that it was used only in the feedback stage due to its nature. We expand emotion analysis into 9 categories and suggest its use in the development of emotional content considering it from the planning stage. It is expected that more accurate results can be obtained if emotion analysis is performed by additionally collecting more diverse daily conversations through follow-up research.

• Steel and Composite Structures
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• v.48 no.2
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• pp.207-233
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• 2023

Steel-concrete composite box girder bridges are widely used in the construction of highway and railway bridges both domestically and abroad due to their advantages of being light weight and having a large spanning ability and very large torsional rigidity. Composite box girder bridges exhibit the effects of shear lag, restrained torsion, distortion and interface bidirectional slip under various loads during operation. As one of the most commonly used calculation tools in bridge engineering analysis, one-dimensional models offer the advantages of high calculation efficiency and strong stability. Currently, research on the one-dimensional model of composite beams mainly focuses on simulating interface longitudinal slip and the shear lag effect. There are relatively few studies on the one-dimensional model which can consider the effects of restrained torsion, distortion and interface transverse slip. Additionally, there are few studies on vehicle-bridge integrated systems where a one-dimensional model is used as a tool that only considers the calculations of natural frequency, mode and moving load conditions to study the dynamic response of composite beams. Some scholars have established a dynamic analysis model of a coupled composite beam bridge-train system, but where the composite beam is only simulated using a Euler beam or Timoshenko beam. As a result, it is impossible to comprehensively consider multiple complex force effects, such as shear lag, restrained torsion, distortion and interface bidirectional slip of composite beams. In this paper, a 27 DOF vehicle rigid body model is used to simulate train operation. A two-node 26 DOF finite beam element with composed box beams considering the effects of shear lag, restrained torsion, distortion and interface bidirectional slip is proposed. The dynamic analysis model of the coupled composite box girder bridge-train system is constructed based on the wheel-rail contact relationship of vertical close-fitting and lateral linear creeping slip. Furthermore, the accuracy of the dynamic analysis model is verified via the measured dynamic response data of a practical composite box girder bridge. Finally, the dynamic analysis model is applied in order to study the influence of various mechanical effects on the dynamic performance of the vehicle-bridge system.

• The Journal of Bigdata
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• v.8 no.1
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• pp.157-165
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• 2023

Seafood is a major source of protein in many countries and its consumption is increasing. In Korea, consumption of seafood is increasing, but self-sufficiency rate is decreasing, and the importance of safety management is increasing as the amount of imported seafood increases. There are hundreds of species of aquatic products imported into Korea from over 110 countries, and there is a limit to relying only on the experience of inspectors for safety management of imported aquatic products. Based on the data, a model that can predict the customs inspection results of imported aquatic products is developed, and a machine learning classification model that determines the non-conformity of aquatic products when an import declaration is submitted is created. As a result of customs inspection of imported marine products, the nonconformity rate is less than 1%, which is very low imbalanced data. Therefore, a sampling method that can complement these characteristics was comparatively studied, and a preprocessing method that can interpret the classification result was applied. Among various machine learning-based classification models, Random Forest and XGBoost showed good performance. The model that predicts both compliance and non-conformance well as a result of the clearance inspection is the basic random forest model to which ADASYN and one-hot encoding are applied, and has an accuracy of 99.88%, precision of 99.87%, recall of 99.89%, and AUC of 99.88%. XGBoost is the most stable model with all indicators exceeding 90% regardless of oversampling and encoding type.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.383-392
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established. Therefore, uncertainty analysis are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an unexpected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.481-492
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• 2023

This study explores how to build a Korean dataset to extract information from text using generative large language models. In modern society, mixed information circulates rapidly, and effectively categorizing and extracting it is crucial to the decision-making process. However, there is still a lack of Korean datasets for training. To overcome this, this study attempts to extract information using text-based zero-shot learning using a generative large language model to build a purposeful Korean dataset. In this study, the language model is instructed to output the desired result through prompt engineering in the form of "system"-"instruction"-"source input"-"output format", and the dataset is built by utilizing the in-context learning characteristics of the language model through input sentences. We validate our approach by comparing the generated dataset with the existing benchmark dataset, and achieve 25.47% higher performance compared to the KLUE-RoBERTa-large model for the relation information extraction task. The results of this study are expected to contribute to AI research by showing the feasibility of extracting knowledge elements from Korean text. Furthermore, this methodology can be utilized for various fields and purposes, and has potential for building various Korean datasets.

• Smart Media Journal
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• v.12 no.10
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• pp.93-101
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• 2023

Climate change has emerged as a global problem, with frequent temperature increases, droughts, and floods, and it is predicted that it will have a great impact on the characteristics and productivity of crops. Cnidium officinale is used not only as traditionally used herbal medicines, but also as various industrial raw materials such as health functional foods, natural medicines, and living materials, but productivity is decreasing due to threats such as continuous crop damage and climate change. Therefore, this paper proposes a model that can predict the physiologically active ingredient index according to the climate change scenario of Cnidium officinale, a representative medicinal crop vulnerable to climate change. In this paper, data was first augmented using the CTGAN algorithm to solve the problem of data imbalance in the collection of environment information, physiological reactions, and physiological active ingredient information. Column Shape and Column Pair Trends were used to measure augmented data quality, and overall quality of 88% was achieved on average. In addition, five models RF, SVR, XGBoost, AdaBoost, and LightBGM were used to predict phenol and flavonoid content by dividing them into ground and underground using augmented data. As a result of model evaluation, the XGBoost model showed the best performance in predicting the physiological active ingredients of the sacrum, and it was confirmed to be about twice as accurate as the SVR model.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.63-65
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• 2023

BIM and AR technologies have been assessed as a means of enhancing productivity within the construction industry, through the provision of effortless access to critical data on site, achieved via the projection of 3D models and associated information onto actual structures. However, most of the previous researches for applying AR technology in construction quality management has been performed for construction projects in general, resulting in only overall on-site management solutions. Also, a few previous researches for the application of AR in the quality management of specific elements like reinforcements focused only on simple projection, so conducting specific quality inspection was impossible. Hence, this study aimed to develop a practically applicable BIM-based AR quality management system targeted for reinforcements. For the development of this system, the reinforcement inspection items on the quality checklist used at railway construction sites were analyzed, and four types of AR functions that can effectively address these items were developed and installed. The validation result of the system for the actual railway bridge showed a degradation of projection stability. This problem was solved through model simplification and enhancement of the AR device's hardware performance, and then the normal operation of the system was validated. Subsequently, the final developed reinforcement quality inspection system was evaluated for practical applicability by on-site quality experts, and the efficiency of inspection would significantly increase when using the AR system compared to the current inspection method for reinforcements.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Membrane Journal
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• v.33 no.6
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• pp.447-453
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• 2023

The performance of computer systems and the development of various computer simulation programs have made it possible to analyze chemical systems composed of more complex elements, and accordingly, research using molecular dynamics simulation is being actively conducted. Research on calculating the gas permeation characteristics of polymer membranes by molecular dynamics, which was previously conducted mainly through experiments, is receiving attention for gas barrier membranes used in food packaging and pharmaceuticals. Recently, there has been a report that a gas barrier effect appears when a coating film is made using silk fibroin, and in this study, a study was conducted using molecular dynamics simulation to confirm whether an oxygen barrier effect appears when a composite film is made using silk fibroin. We built a single model, calculated the gas permeation characteristics, and compared it with the experimental value to confirm that the model reflects the actual experimental results. Actual composite membrane models were then built and the gas movement path within the polymer was analyzed. As a result, oxygen molecules were found that they could not pass through and was blocked in the fibroin region. Therefore, the composite membrane with silk fibroin has excellent oxygen barrier property and is expected to be useful in food packaging, etc.