• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.152-162
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• 2022

This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r² = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.435-444
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• 2023

Suitable environmental conditions in Saemangeum frequently favor phytoplankton growth. There have been occurrences of sudden phytoplankton blooms, surpassing the algae management standards. A model was designed to prevent such blooms using scientific predictive techniques to forecast and regulate the possibility of phytoplankton blooms. We propose effective and efficient algae control measures concerning every phytoplankton species optimized through the policy control of nutrients (DIN, PO4-P) from rivers and controlling lake salinity using gate operations. The probability of phytoplankton blooms was initially forecast using an artificial neural network algorithm based on observations. The model's Kappa number fluctuated from 0.7889 to 1.0000, indicating good to excellent predictive power. The Garson algorithm was then utilized to assess the significance of explanatory variables for every species. Meanwhile, the probability of phytoplankton blooms was anticipated depending on the DIN and salinity value changes. Therefore, the model predicted the precise DIN and salinity concentrations to inhibit phytoplankton blooms for each species. Hence, the green algae model can create effective proactive measures to avoid future phytoplankton blooms in enormous artificial lakes.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.62-67
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• 2023

Due to the prolonged impact of COVID-19, the demand for Information Technology (IT) products is increasing, and their production facilities are expanded. Consequently, the use of harmful and dangerous chemicals are increased, the risk of fire(s) and explosion(s) is also elevated. In order to mitigate these risks, the government sets standards, such as KS C IEC 60079-10-1, and manages explosion-prone hazardous facilities where flammable substances are manufactured, used, and handled. However, using the standards of KS, it is difficult to predict the actual possibility of an explosion in a facility, because ventilation (an important factor) is not considered when setting up a hazardous work environment. In this study, the SEMI S6, Tracer Gas Test was applied to the chemical vapor deposition (CVD) facility, a major part of the display industry, to evaluate ventilation performance and to confirm the possibility of creating a less explosive environment. Based on the results, it was confirmed that the ventilation performance in the assumed scenarios met the standards stipulated in SEMI S6, along with supporting the possibility of creating a less explosive working condition. Therefore, it is recommended to use the prediction tool using engineering techniques, as well as KS standards, in such hazardous environments to prevent accidents and/or reduce economic burden following accidents.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.1
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• pp.33-47
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• 2022

In this study, distribution of submarine sand ridges in the coastal waters of Korea was surveyed using multibeam echo sounder data, and the topographic characteristics of each region were identified. For this purpose, the DEM (Digital Elevation Model) data was generated using depth data obtained from the Yellow Sea and the South Sea by Korea Hydrographic and Oceanographic Agency, and then applied the TPI (Topographic Position Index) technique to precisely extract the boundary of the sand ridges. As a result, a total of 200 sand ridges distributed in the coastal waters were identified, and the characteristics of each region of the sedimentary sediments were analyzed by performing statistical analysis on the scale (width, length, perimeter, area, height) and shape (width/length ratio, height/width ratio, linear·branch type, exposure·non-exposure type). The results of this study are expected to be used not only for coastal navigational safety, but also for marine naming support, marine aggregate resource identification, and fisheries resource management.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.867-873
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• 2023

In this study, for the purpose of standardized quality control of a cold medicine, we simultaneous analyzed four main chemical components of a cold medicine: acetaminophen, caffeine, methyl paraben, and propyl paraben. The sample was subjected to quantitative analysis using high performance liquid chromatography (HPLC), after pretreatment of four components. The experiment was carried out by using Isocratic elution at wavelength of 270nm. Acetonitrile and water (H₂O) were used as a mobile phase at a flow rate of 1.0mL/min in a commercial C18 reversed-phase column. A volume of 10uL cold medicine were injected into the column with column oven temperature at 35℃. As a result of the experiment, the values of Resolution were 4.983, 1.596, 5.519, and 1.678 respectively-well over Rs >1.5, which indicates that the separation of four components were efficient. In addition, value of symmetry factor of the components was 1.056, 1.069, 1.032, and 1.133 respectively, to show its symmetrical stability. The calibration curve of all four components exhibits good linearity with R² >0.9995 to 0.9999. Furthermore, the limit of detection(LOD) were between 0.0118 to 1.5973 mg/mL, while the limit of quantification (LOQ) were between 0.0353 to 4.7919 ㎍/mL with the recovery rate of 79.6% ~ 120.5%. The results of this study showed an efficient quality evaluation of a simultaneous analysis method for cold medicine components.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• Korean Journal of Environment and Ecology
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• v.38 no.2
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• pp.178-188
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• 2024

The dynamics of suspended sediment (SS) in forested catchments vary depending upon human or natural disturbances, including land use change, forestry activity, forest fires, and landslides. Understanding the dynamics of SS originating from the potential sources within a forested catchment is crucial for establishing an effective water quality management strategy. Therefore, to suggest a systematic method for interpreting SS dynamics, we evaluated the performance and applicability of ten methods for calculating the hysteresis index based on observed hydrological data and two calculation models (Lawler's method and Lloyd's method) with five sampling intervals (50th, 25th, 10th, 5th, and 1st percentiles). Our results showed that Lloyd's method, which used a sampling interval at the 1st percentile, had the largest number of analyzable runoff events and exhibited the best performance. The results of this study can contribute to quantifying the hysteresis in the relationship between discharge and SS and provide useful information for interpreting SS dynamics.