• The KIPS Transactions:PartA
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• v.14A no.1 s.105
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• pp.47-54
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• 2007

In this paper we present a translation lookaside buffer (TLB) system with low power consumption for imbedded processors. The proposed TLB is constructed as multiple banks, each with an associated block buffer and a corresponding comparator. Either the block buffer or the main bank is selectively accessed on the basis of two bits in the block buffer (tag buffer). Dynamic power savings are achieved by reducing the number of entries accessed in parallel, as a result of using the tag buffer as a filtering mechanism. The performance overhead of the proposed TLB is negligible compared with other hierarchical TLB structures. For example, the two-cycle overhead of the proposed TLB is only about 1%, as compared with 5% overhead for a filter (micro)-TLB and 14% overhead for a same structure without continuos accessing distinction algorithm. We show that the average hit ratios of the block buffers and the main banks of the proposed TLB are 95% and 5% respectively. Dynamic power is reduced by about 95% with respect to with a fully associative TLB, 90% with respect to a filter-TLB, and 40% relative to a same structure without continuos accessing distinction algorithm.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Journal of Food Hygiene and Safety
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• v.34 no.4
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• pp.380-387
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• 2019

Of great importance in food safety culture are the values of an organization regarding food safety that combine the human and material requirements needed to produce safe and hygienic foods. In recent years, efforts have been made to improve the level of implementation of food safety management systems by improving certain cultural elements of food safety. This study investigated the current state of food safety culture in the HMR manufacturing sector. An anonymous survey of 46 HMR manufacturers of various sizes was conducted to evaluate the implementation status of HACCP prerequisite program and food safety culture. The perceived importance of food safety culture factors and their performance were also surveyed. Employees of HMR manufacturers who participated in this survey recognized that the participation of employees and leadership was the most important factor in ensuring food safety. Smaller enterprises are less aware of the importance of such organizational culture. The survey shows that food safety culture indicators in large companies are generally higher than those of small and medium enterprises. Especially, the manager's level of commitment to food safety, resources input, and education and training was significantly higher than that found at small companies (p=0.005). Among the food safety culture evaluation factors, it was found that education and training had significant influence on HACCP prerequisite program performance. Continued employee education and training on food safety and hygiene are important for HMR manufacturers to achieve HACCP certification standards.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.1
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• pp.82-91
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• 2019

In this study, in order to evaluate Hydration Heat Characteristics of mass concrete using ternary blended cement for large underground structures, the analysis considering the temperature history and the thermal characteristics inside the actual structure was performed. The results of the analysis are compared with the measured values to verify the reliability of the analysis and to evaluate the crack resistance performance. As a result of the measured the actual structure temperature, The adiabatic temperature rise coefficients K and ${\alpha}$ of the slab were $35.1^{\circ}C$ and 0.72, respectively, and the wall was analyzed as $29.3^{\circ}C$ and 0.67. The analytical results and the correlation coefficients(r) were 0.95 and 0.98, respectively. As a result of evaluating the crack resistance of slab and wall, the minimum crack index of slab and wall was 1.22 and 1.20, respectively. These results were found to satisfy the site management standards.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.3
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• pp.257-265
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• 2021

The COVID-19 virus is a β-genus virus that causes infection by mediating the angiotensin convertible enzyme 2 (ACE2) receptor, which is distributed in large numbers in the human respiratory tract. The disease requires effective post-management of antibody production by complete healers and vaccinators because there is no perfect remedy for the virus infection. This study aimed to develop recombinant proteins specifically responsive to neutralizing antibodies in clinical specimens and use them to develop a rapid diagnostic kit to diagnose neutralizing antibodies quickly and conveniently against the COVID-19 virus and confirm the possibility of commercialization through a performance evaluation. Rapid diagnostic kits using COVID-19 S1 RBD recombinant proteins can be applied to rapid diagnostic kits, with positive percentage agreement (PPA) and negative percentage agreement (NPA) of 100% and 98.3%, respectively, compared to the U.S. FDA-approved ELISA kits. If the performance of the rapid diagnostic kit is improved and neutralizing antibodies can be analyzed quantitatively using quantitative analysis equipment, it can be used as important data to predict immunity to the COVID-19 virus and determine additional vaccinations.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.62-69
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• 2011

Six commonly used non-linear growth functions were fitted to individual tree height-dbh data of eight major tree species measured by the $5^{th}$ National Forest Inventory in Chungcheong province. A total of 2,681 trees were collected from permanent sample plots across Chungcheong province. The available data for each species were randomly splitted into two sets: the majority (90%) was used to estimate model parameters and the remaining data (10%) were reserved to validate the models. The performance of the models was compared and evaluated by $R^2$, RMSE, mean difference (MD), absolute mean difference (AMD) and mean difference(MD) for diameter classes. The combined data (100%) were used for final model fitting. The results showed that these six sigmoidal models were able to capture the height-diameter relationships and fit the data equally well, but produced different asymptote estimates. Sigmoidal growth models such as Chapman-Richards, Weibull functions provided the most satisfactory height predictions. The effect of model performance on stem volume estimation was also investigated. Tree volumes of different species were computed by the Forest Resources Evaluation and Prediction Program using observed range of diameter and the predicted tree total height from the six models. For trees with diameter less than 30 cm, the six height-dbh models produced very similar results for all species, while more differentiation among the models was observed for large-sized trees.

• Journal of Environmental Impact Assessment
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• v.31 no.4
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• pp.214-225
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• 2022

This study emphasized the soil of environmental impact assessment to devise measures to minimize the negative impact of project implementation on the environment. As a series of efforts for impact assessment procedures, a national inventory-based database was established for urban development projects, and three machine learning model performance evaluation as well as soil pollutant concentration distribution mapping were conducted. Here, nine soil pollutants were mapped to the metropolitan area of South Korea using the Random Forest model, which showed the best performance. The results of this study found that concentrations of Zn, F, and Cd were relatively concerned in Seoul, where urbanization is the most active. In addition, in the case of Hg and Cr6+, concentrations were detected below the standard, which was derived from a lack of pollutants such as industrial and industrial complexes that affect contents of heavy metals. A significant correlation between land cover and pollutants was inferred through the spatial distribution mapping of soil pollutants. Through this, it is expected that efficient soil management measures for minimizing soil pollution and planning decisions regarding the location of the project site can be established.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.91-101
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• 2023

In this study, we estimated the concentration of chlorophyll-a (Chl-a) using hyperspectral water surface reflectance in an inland weir (Baekjae weir) and estuarine reservoir (Namyang Reservoir) for monitoring the occurrence of algae in freshwater in South Korea. The hyperspectral reflectance was measured by aircraft in Baekjae Weir (BJW) from 2016 to 2017, and a drone in Namyang Reservoir (NYR) from 2020 to 2021. The 30 reflectance bands (BJW: 400-530, 620-680, 710-730, 760-790 nm, NYR: 400-430, 655-680, 740-800 nm) that were highly related to Chl-a concentration were selected using permutation importance. Artificial neural network based Chl-a estimation model was developed using the selected reflectance in both water bodies. And the performance of the model was evaluated with the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The performance evaluation results of the Chl-a estimation model for each watershed was R²: 0.63, 0.82, RMSE: 9.67, 6.99, and MAE: 11.25, 8.48, respectively. The developed Chl-a model of this study may be used as foundation tool for the optimal management of freshwater algal blooms in the future.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.1-12
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• 2024

In this paper, we fine-tuned KoAlpaca, a large-scale Korean language model, and implemented a blog text generation system utilizing it. Blogs on social media platforms are widely used as a marketing tool for businesses. We constructed training data of positive reviews through emotion analysis and refinement of collected sponsored blog texts and applied QLoRA for the lightweight training of KoAlpaca. QLoRA is a fine-tuning approach that significantly reduces the memory usage required for training, with experiments in an environment with a parameter size of 12.8B showing up to a 58.8% decrease in memory usage compared to LoRA. To evaluate the generative performance of the fine-tuned model, texts generated from 100 inputs not included in the training data produced on average more than twice the number of words compared to the pre-trained model, with texts of positive sentiment also appearing more than twice as often. In a survey conducted for qualitative evaluation of generative performance, responses indicated that the fine-tuned model's generated outputs were more relevant to the given topics on average 77.5% of the time. This demonstrates that the positive review generation language model for sponsored content in this paper can enhance the efficiency of time management for content creation and ensure consistent marketing effects. However, to reduce the generation of content that deviates from the category of positive reviews due to elements of the pre-trained model, we plan to proceed with fine-tuning using the augmentation of training data.