• Food Science and Preservation
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• v.30 no.3
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• pp.459-471
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• 2023

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.

• Industry Promotion Research
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• v.9 no.1
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• pp.21-29
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• 2024

In the rapidly advancing information society, electronic devices, including smartphones and tablets, are increasingly digitized and equipped with high-performance features such as flexible displays. This study focused on optimizing the manufacturing process for Transparent Conductive Films (TCF) by using the cost-effective conductive polymer PEDOT and transparent substrate PET as alternatives to expensive materials in flexible display technology. The variables considered are production speed (m/min), coating maximum temperature (℃), and PEDOT supply speed (rpm), with surface resistivity (Ω/□) as the response parameter, using Response Surface Methodology (RSM). Optimization results indicate the ideal conditions for production: a speed of 22.16 m/min, coating temperature of 125.28℃, and PEDOT supply at 522.79 rpm. Statistical analysis validates the reliability of the results (F value: 18.37, P-value: < 0.0001, R²: 0.9430). Under optimal conditions, the predicted surface resistivity is 145.75 Ω/□, closely aligned with the experimental value of 142.97 Ω/□. Applying these findings to mass production processes is expected to enhance production yields and decrease defect rates compared to current practices. This research provides valuable insights for the advancement of flexible display manufacturing.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.585-590
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• 2024

In this study, 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 engineers and simple measuring tools. With the recent enactment of the Track Diagnosis Act, a large budget has been invested and the volume of rail diagnosis is rapidly increasing, but it is difficult to secure the reliability of diagnosis results using labor-intensive visual inspection techniques. 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 rail internal crack characteristics due to rail surface damage were studied. In field measurements, rail surface damage locations were selected, samples of various damage types were collected, and the rail surface damage status was evaluated. In indoor testing, we intend to analyze the correlation between rail surface defects and internal defects using a electron scanning microscope (SEM). To determine the crack growth rate of urban railway rails currently in use, the Gaussian probability density function was applied and analyzed.

• Journal of the Korean Society of Radiology
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• v.84 no.6
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• pp.1324-1336
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• 2023

Purpose This study aimed to compare the diagnostic performance of cardiac CT and transthoracic echocardiogram (TTE) depending on the degree of valvular calcification and bicuspid aortic valve (BAV) subtype. Materials and Methods This retrospective study included 266 consecutive patients (106 with BAV and 160 with tricuspid aortic valve) who underwent cardiac CT and TTE before aortic valve replacement. Cardiac CT was used to evaluate the morphology of the aortic valve, and a calcium scoring scan was used to quantify valve calcium. The aortic valves were classified into fused and two-sinus types. The diagnostic accuracy of cardiac CT and TTE was calculated using a reference standard for intraoperative inspection. Results CT demonstrated significantly higher sensitivity, negative predictive value, and accuracy than TTE in detecting BAV (p < 0.001, p < 0.001, and p = 0.003, respectively). The TTE sensitivity tended to decrease as valvular calcification increased. The error rate of TTE for CT was 10.9% for the twosinus type of BAV and 28.3% for the fused type (p = 0.044). Conclusion Cardiac CT had a higher diagnostic performance in detecting BAV than TTE and may help diagnose BAV, particularly in patients with severe valvular calcification.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.326-334
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• 2024

Extra-oral imaging techniques such as Panoramic X-rays (PXs) and Cone Beam Computed Tomography (CBCT) are the most preferred imaging modalities in dental clinics owing to its patient convenience during imaging as well as their ability to visualize entire teeth information. PXs are preferred for routine clinical treatments and CBCTs for complex surgeries and implant treatments. However, PXs are limited by the lack of third dimensional spatial information whereas CBCTs inflict high radiation exposure to patient. When a PX is already available, it is beneficial to reconstruct the 3D oral structure from the PX to avoid further expenses and radiation dose. In this paper, we propose 3DentAI - an U-Net based deep learning framework for 3D reconstruction of oral structure from a PX image. Our framework consists of three module - a reconstruction module based on attention U-Net for estimating depth from a PX image, a realignment module for aligning the predicted flattened volume to the shape of jaw using a predefined focal trough and ray data, and lastly a refinement module based on 3D U-Net for interpolating the missing information to obtain a smooth representation of oral cavity. Synthetic PXs obtained from CBCT by ray tracing and rendering were used to train the networks without the need of paired PX and CBCT datasets. Our method, trained and tested on a diverse datasets of 600 patients, achieved superior performance to GAN-based models even with low computational complexity.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.

• The Journal of Bigdata
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• v.8 no.2
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• pp.73-82
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• 2023

With 78% of current fisheries workers being elderly, there's a pressing need to address labor shortages. Consequently, active research on smart aquaculture technologies, centered on object detection and tracking algorithms, is underway. These technologies allow for fish size analysis and behavior pattern forecasting, facilitating the development of real-time monitoring and automated systems. Our study utilized video data from cameras outside aquaculture facilities and implemented fish detection and tracking algorithms. We aimed to tackle high maintenance costs due to underwater conditions and camera corrosion from ammonia and pH levels. We evaluated the performance of a real-time system using YOLOv7 for fish detection and the SORT algorithm for movement tracking. YOLOv7 results demonstrated a trade-off between Recall and Precision, minimizing false detections from lighting, water currents, and shadows. Effective tracking was ascertained through re-identification. This research holds promise for enhancing smart aquaculture's operational efficiency and improving fishery facility management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.583-595
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• 2014

Currently, Korean transportation policies are aiming for increase of safety and environment-friendly and efficient operation, by avoiding construction and expansion of roads, and upgrading road alignments and facilities. This is revealed by that there have been 22 road expansion projects (30%) and 50 road improvement projects (70%) under the 3rd Five-Year Plan for National Highways ('11~'15), while there were 53 road expansion projects (71%) and 22 road improvement projects (29%) under the 2nd Five-Year Plan for National Highways. For more effective road improvement projects, there is a need of choosing projects after an objective and scientific safety assessment of each road, and assessing safety improvement depending on projects. This study is intended to develop a model for this road safety analysis and assessment. The major objective of this study is creating a road safety analysis and assessment model appropriate for Korean society, based on the HSM (Highway Safety Manual) of the U.S. In order to build up data for model development, the sections thought to have identical geometrical structure factors in 5 lines, Cheonbuk province, were divided as homogeneous sections, and representative values of geometric structures, facilities, traffic volume, climate conditions and land usage were collected from the 1,452 sections divided. In order to build up data for model development, the sections thought to have identical geometrical structure factors in 5 lines, Cheonbuk province, were divided as homogeneous sections, and representative values of geometric structures, facilities, traffic volume, climate conditions and land usage were collected from the 1,452 sections divided. The collected data was processed correlation analysis of each road element was implemented to see which factor had a big effect on traffic accidents. On the basis of these results, then, an accident model was established as a negative binomial regression model.Using the developed model, an Crash Modification Factor (CMF) which determines accident frequency changes depending on safety performance function (SPF) predicting the number of accident occurrence through traffic volume and road section expansion, road geometric structure and traffic properties, was extracted.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.