• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.63-63
• /
• 2022

기상 예보자료는 발생 가능한 재난의 예방 및 대비 차원에서 매우 중요한 자료로 활용되고 있다. 우리나라 기상청에서는 동네예보를 통해 5km 공간해상도의 1시간 간격 초단기예보와, 6시간 간격 정량강우예보(Quantitative Precipitation Forecast, QPF)의 단기예보 정보를 제공하고 있다. 그러나 이와 같은 예보자료는 강우량의 시·공간변화가 큰 집중호우와 같은 기상자료를 활용한 수문학적인 해석에는 한계가 있다. 예보자료를 수문학에 활용하기 위한 시·공간적 해상도 개선뿐만 아니라 방대한 기상 및 기후 자료의 예측성능을 개선하기 위한 다양한 연구가 진행되고 있다. 본 연구에서는 기상청이 제공하는 지역 앙상블 예측 시스템(Local ENsemble prediction System, LENS)와 종관기상관측시스템(ASOS) 및 방재기상관측시스템(AWS) 관측 데이터 및 동네예보에 기계학습 방법을 적용하여 수문학적 정량적 강수량 예측(Hydrological Quantitative Precipitation Forecast, HQPF) 정보를 생산하였다. 전처리 과정을 통해 모든 데이터의 시간해상도와 공간해상도를 동일한 해상도로 변환하였으며, 예측 변수의 인자 분석을 통해 기계학습의 예측 변수를 도출하였다. 기계학습 방법으로는 처리속도와 확장성을 고려하여 XGBoost(eXtreme Gradient Boosting) 방식을 적용하였으며, 집중호우에서의 예측정확도를 높이기 위해 확률매칭(PM) 방식을 적용하였다. 생산된 HQPF의 성능을 평가하기 위해 2020년에 발생한 14건의 호우 사상을 대상으로 태풍형과 비태풍형으로 구분하여 검증을 수행하였다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.259-262
• /
• 2022

정보화 시대 스마트폰이 대중화되고 실시간 인터넷 사용이 가능해짐에 따라, 본인을 식별하기 위한 사용자 인증이 필수적으로 요구된다. 대표적인 사용자 인증 기술로는 아이디와 비밀번호를 이용한 비밀번호 인증이 있지만, 키보드로부터 입력받는 이러한 인증 정보는 시각 장애인이나 손 사용이 불편한 사람, 고령층과 같은 사람들이 많은 서비스로부터 요구되는 아이디와 비밀번호를 기억하고 입력하기에는 불편함이 따를 뿐만 아니라, 키로거와 같은 공격에 노출되는 문제점이 존재한다. 이러한 문제점을 해결하기 위하여, 자신의 신체의 특징을 활용하는 생체 인증이 대두되고 있으며, 그중 목소리로 사용자를 인증한다면, 효과적으로 비밀번호 인증의 한계점을 극복할 수 있다. 이러한 화자 인식 기술은 KT의 기가 지니와 같은 음성 인식 기술에서 활용되고 있지만, 목소리는 위조 및 변조가 비교적 쉽기에 지문이나 홍채 등을 활용하는 인증 방식보다 정확도가 낮고 음성 인식 오류 또한 높다는 한계점이 존재한다. 상기 목소리를 활용한 사용자 인증 기술인 화자 인식 기술을 활용하기 위하여, 사용자 목소리를 학습시켰으며, 목소리의 주파수를 추출하는 MFCC 알고리즘을 이용해 테스트 목소리와 정확도를 측정하였다. 그리고 악의적인 공격자가 사용자 목소리를 흉내 내는 경우나 사용자 목소리를 마이크로 녹음하는 등의 방법으로 획득하였을 경우에는 높은 확률로 인증의 우회가 가능한 것을 검증하였다. 이에 따라, 더욱 효과적으로 화자 인식의 정확도를 향상시키기 위하여, 본 논문에서는 목소리에 잡음을 섞는 방법으로 화자를 인식하는 방안을 제안한다. 제안하는 방안은 잡음이 정확도에 매우 민감하게 반영되기 때문에, 기존의 인증 우회 방법을 무력화하고, 더욱 효과적으로 목소리를 활용한 화자 인식 기술을 제공할 것으로 사료된다.

• Advances in concrete construction
• /
• v.14 no.3
• /
• pp.185-194
• /
• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.106-107
• /
• 2022

The ship type identification of the surrounding ship is important information for navigators and VTS officers since they can estimate the maneuverability and near-future route of the ships. However, it is more than frequent that the information is not provided due to transmission trouble and seafarers' unfamiliarity with AIS. Thus, this study suggests predicting ship types through the Random Forest classifier after preparing a training and test dataset that contains ship features and types. The AIS data for Ulsan coast in 2018 was used for this study. The method may provide the effect that many navigators and VTS officers discuss and share the experience of predicting ship types.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.3
• /
• pp.139-147
• /
• 2023

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

• Wind and Structures
• /
• v.37 no.4
• /
• pp.303-314
• /
• 2023

In this study, two steady RANS turbulence models (SST k-ω and Realizable k-ε) and four unsteady turbulence models (URANS SST k-ω and Realizable k-ε, SST-SAS, and SST-IDDES) are evaluated with respect to their capacity to predict crosswind characteristics on high-speed trains (HSTs). All of the numerical simulations are compared with the wind tunnel values and LES results to ensure the accuracy of each turbulence model. Specifically, the surface pressure distributions, time-averaged aerodynamic coefficients, flow fields, and computational cost are studied to determine the suitability of different models. Results suggest that the predictions of the pressure distributions and aerodynamic forces obtained from the steady and transient RANS models are almost the same. In particular, both SAS and IDDES exhibits similar predictions with wind tunnel test and LES, therefore, the SAS model is considered an attractive alternative for IDDES or LES in the crosswind study of trains. In addition, if the computational cost needs to be significantly reduced, the RANS SST k-ω model is shown to provide relatively reasonable results for the surface pressures and aerodynamic forces. As a result, the RANS SST k-ω model might be the most appropriate option for the expensive aerodynamic optimizations of trains using machine learning (ML) techniques because it balances solution accuracy and resource consumption.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.7
• /
• pp.958-963
• /
• 2023

Lately, there has been increasing research on the prediction of motion performance using artificial intelligence for the safe design and operation of ships. However, compared to conventional ships, research on small fishing boats is insufficient. In this paper, we propose a model that estimates the motion response essential for calculating the motion performance of small fishing boats using a deep neural network. Hydrodynamic analysis was conducted on 15 small fishing boats, and a database was established. Environmental conditions and main particulars were applied as input data, and the response amplitude operators were utilized as the output data. The motion response predicted by the trained deep neural network model showed similar trends to the hydrodynamic analysis results. The results showed that the high-frequency motion responses were predicted well with a low error. Based on this study, we plan to extend existing research by incorporating the hull shape characteristics of fishing boats into a deep neural network model.

• Advances in nano research
• /
• v.15 no.5
• /
• pp.451-466
• /
• 2023

Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

• Smart Structures and Systems
• /
• v.32 no.5
• /
• pp.319-338
• /
• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.41-47
• /
• 2023

Financial instruments valuation continues to evolve due to various technological changes. Recently, there has been increased interest in valuation using machine learning and artificial intelligence, enabling the financial market to swiftly adapt to changes. This technological advancement caters to the demand for real-time data processing and facilitates accurate and effective valuation, considering the diverse nature of the financial market. Numerical analysis techniques serve as crucial decision-making tools among financial institutions and investors, acknowledged as essential for performance prediction and risk management in investments. This paper analyzes Korean patent trends of numerical analysis-based financial systems, considering the diverse shifts in the financial market and asset data to provide accurate predictions. This study could shed light on the advancement of financial technology and serves as a gauge for technological standards within the financial market.