• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.1
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• pp.17-29
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• 2024

This study was conducted to select the restoration priority of forest damage sites in Gangwon Province. We first identified the status of damaged areas. We then selected restoration evaluation indicators through a literature review. We then set weights for these indicators through expert surveys. We next acquired data that can represent these indicators and spatially mapped them. Finally, we prioritized the restoration target sites by taking the weights. The results of the study showed that disaster sensitivity and ecologicality are important criteria for selecting the restoration priority of damage sites. The analysis showed that damage sites in Doam, Jeongseon, Samcheok and Inje are in urgent need of restoration. The results of this study are significant in that they selected the restoration priority of damage sites in Gangwon Province based on the restoration priority evaluation criteria selected based on expert surveys. However, the priority restoration areas derived from the results of this study are not actually implementing restoration projects at present. Therefore, it is judged that it would be efficient in various aspects to establish the restoration priority area based on scientific analysis techniques and carry out the project for efficient implementation of the restoration project. In this study, it can be pointed out that the priority of restoration of damage sites was derived based on data from the past due to the limitation of data acquisition. However, the fact that the priority restoration area inferred based on past data has been restored over time has improved the reliability of the study by verifying the usefulness of the priority extraction technique. In the future, if the priority of damage sites is extracted by extracting the restoration target area boundary through the latest data based on the methodology applied in this study, it is considered that it will be available as a result that can be applied to the field.

• The Plant Pathology Journal
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• v.40 no.3
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• pp.310-321
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• 2024

Tomato yellow leaf curl virus (TYLCV) and tomato spotted wilt virus (TSWV) are well-known examples of the begomovirus and orthotospovirus genera, respectively. These viruses cause significant economic damage to tomato crops worldwide. Weeds play an important role in the ongoing presence and spread of several plant viruses, such as TYLCV and TSWV, and are recognized as reservoirs for these infections. This work applies a comprehensive approach, encompassing field surveys and molecular techniques, to acquire an in-depth understanding of the interactions between viruses and their weed hosts. A total of 60 tomato samples exhibiting typical symptoms of TYLCV and TSWV were collected from a tomato greenhouse farm in Nonsan, South Korea. In addition, 130 samples of 16 different weed species in the immediate surroundings of the greenhouse were collected for viral detection. PCR and reverse transcription-PCR methodologies and specific primers for TYLCV and TSWV were used, which showed that 15 tomato samples were coinfected by both viruses. Interestingly, both viruses were also detected in perennial weeds, such as Rumex crispus, which highlights their function as viral reservoirs. Our study provides significant insights into the co-occurrence of TYLCV and TSWV in weed reservoirs, and their subsequent transmission under tomato greenhouse conditions. This project builds long-term strategies for integrated pest management to prevent and manage simultaneous virus outbreaks, known as twindemics, in agricultural systems.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.442-447
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• 2020

Iatrogenic foreign bodies are a challenging complication to both the interventional radiologist and patient, resulting in impaired quality of life and substantial financial cost. The case report describes a successful percutaneous transhepatic removal of an intra-abdominal foreign body. A 72-year-old man underwent surgery for placement of a retrievable covered stent for refractory bile leakage after left hemihepatectomy. Three days after placement, stent folding and migration into a chronic biloma cavity occurred via the bile leakage site. By using a balloon catheter technique, the folded stent could be straightened and repositioned into the bile duct to minimize stent-strut injury during retrieval. The interventional approach could be a valid treatment option for intra-abdominal foreign bodies, as well as intravascular foreign bodies. A thorough understanding of devices and techniques can provide the interventional radiologist with valuable information regarding procedural planning and the management of iatrogenic foreign bodies.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.111-119
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• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.303-314
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• 2024

Precipitation data are an integral part of water management planning, especially the design of hydroelectric structures and the study of floods and droughts. However, it is difficult to obtain accurate data due to space-time constraints. The recent increase in hydrological variability due to climate change has further emphasized the importance of precipitation simulation techniques. Therefore, in this study, the Modified Bartlett-Lewis Rectangular Pulse model was utilized to apply the parameters necessary to predict daily precipitation. The effect of this parameter on the daily precipitation prediction was analyzed by applying exponential distribution, Gamma distribution, and Weibull distribution to evaluate the suitability of daily precipitation prediction according to each distribution type. As a result, it is judged that parameters should be selected in consideration of regional and seasonal characteristics when simulating precipitation using the MBLRP model.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.175-181
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• 2015

Purpose : To evaluate Contralateral Breast Doses with Supine and Prone Positions for tangential Irradiation techniques for left-sided breast Cancer Materials and Methods : We performed measurements for contralateral doses using Human Phantom at each other three plans (conventional technique, Field-in-Field, IMRT, with prescription of 50 Gy/25fx). For the measurement of contralateral doses we used Glass dosimeters on the 4 points of Human Phantom surface (0 mm, 10 mm, 30 mm, 50 mm). For the position check at every measurements, we had taken portal images using EPID and denoted the incident points on the human phantom for checking the constancy of incident points. Results : The contralateral doses in supine position showed a little higher doses than those in prone position. In the planning study, contralateral doses in the prone position increased mean doses of 1.2% to 1.8% at each positions while those in the supine positions showed mean dose decreases of 0.8% to 0.9%. The measurements using glass dosimeters resulted in dose increases (mean: 2.7%, maximum: 4% of the prescribed dose) in the prone position. In addition, the delivery techniques of Field-in-field and IMRT showed mean doses of 3% higher than conventional technique. Conclusion : We evaluated contralateral breast doses depending on different positions of supine and prone for tangential irradiations. For the phantom simulation of set-up variation effects on contralateral dose evaluation, although we used humanoid phantom for planning and measurements comparisons, it would be more or less worse set-up constancy in a real patient. Therefore, more careful selection of determination of patient set-up for the breast tangential irradiation, especially in the left-sided breast, should be considered for unwanted dose increases to left lung and heart. In conclusion, intensive patient monitoring and improved patient set-up verification efforts should be necessary for the application of prone position for tangential irradiation of left-sided breast cancer.