목적 : 4차 산업혁명이 진행됨에 따라 타각적 굴절검사값, 수차 및 동공크기 등을 이용하여 최적의 안경처방값 을 도출해주는 머신러닝(machine learning)을 개발하고자 하였다. 방법: 시력에 영향을 줄 수 있는 안질환 및 전신질환이 없고 안구 수술 이력이 없는 근시안(1,000안)을 대상으로 진행하였다. I-Profilerplus(Zeiss, Berlin, Germany)를 사용하여 타각적 굴절이상도(objective-refraction) 및 안구수차(ocular wavefront-aberration), 동공 크기를 측정하였고, 자각적 굴절이상도(subjective-refraction)는 Visuphor500(Zeiss, Berlin, Germany)를 사용하여 구면 굴절력(S, Diopter), 원주 굴절력(C, Diopter), 난시 축(Ax, °)을 측정하였다. 측정 후, 파이썬(Python, version 3.10)을 이용하여 머신러닝 모델 생성 및 예측 성능을 확인하였다. 결과: 자각적 굴절이상도에서 구면 굴절력에 영향을 미치는 요인은 타각적 구면 굴절력, defocus aberration, spherical aberration, trefoil aberration 순으로 높았고, 원주 굴절력에 영향을 미치는 요인은 타각적 원주 굴 절력, defocus aberration, coma aberration, trefoil aberration 순으로 높았으며, 난시 축은 타각적 난시축만 영향을 미치는 것으로 나타났다. 구면 굴절력, 원주 굴절력, 난시 축의 자각적 굴절이상도와 머신러닝 예상값은 차이가 없는 것으로 나타났다(p=0.976, 0.948, and 0.349, respectively). 결론 : 자각적 굴절이상도를 예측하는 머신러닝 모델을 생성하였고, 해당 모델의 예측된 값과 자각적 굴절이상 도와 유의한 차이가 없는 것을 통해 예측 정확도를 확인하였으며 앞으로 개인 맞춤형 처방을 위한 정확한 안경처 방값을 도출하는데 기초자료가 될 수 있을 것으로 생각된다.

This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMYnormal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

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.

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure’s safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

The popularity of live streaming is driving the emergence of a new business model, known as live-streaming commerce (LSC). While there are more and more broadcasters in LSC, their behaviors and performance of them are significantly different. To have a better understanding of broadcasters, we employ different machine learning models to identify different portraits in both static and dynamic dimensions. We collect a rich live-streaming dataset from one leading platform in China. Our dataset features information for both broadcasters and viewers, including viewers’ purchasing behaviors, viewers’ records of posting words, broadcasters’ gender, the number of followers for broadcasters, and the live streaming show information, including the start and end time, and the viewers in each live streaming show. The rich textual information in broadcasters’ profile induction provides us a good opportunity to uncover different static portraits and the records in live streaming shows give us a chance to identify different dynamic behavioral portraits for broadcasters.

While machine learning has gained popularity in choice behavior modeling, most machine learning models are often complex, difficult to interpret, and even considered as black box. This study investigates machine learning methods for choice behavior modeling that provide interpretability of models’ output. We explore various approaches including (1) explicitly descriptive models such as tree-based models, (2) interpretation of predictive models through feature importance measures, and (3) recent advancements in prediction explanation methods such as LIME and SHAP (Shapley Additive exPlanations). We demonstrate the methods on consumers’ airport choice behavior in Seoul metropolitan area. Through the comparative analysis with traditional discrete choice models, we discuss advantages as well as limitations of machine learning models in consumer choice behavior modeling.

Consumers' online reviews have become more powerful in the Internet market. Consumers share reviews, post comments and constantly evaluate products online. In previous studies, the analysis of online reviews mainly focused on purchasing products based on consumers' own use experience, but in innovative products, it was difficult to find an analysis of product acceptor's response to product user reviews. In particular, there is no online review study of VR covered in this study. This study not only quantitatively analyzed online reviews of consumers who purchased VR products on Amazon, an online distribution site, but also qualitatively analyzed them through crawling. This study used Amazon's VR product user review, where purchases were confirmed, to select algorithms that are more likely to be matched by predicting a helpful review and presenting a predictive model. In addition, the online review extracted deep text associated with Helpful and conducted topical modeling. As a result, topics related to 1) experience in use, 2) post-product evaluation, 3) product composition and peripherals, 4) immersion, and 5) comfort were highly acceptable to potential inmates. To enhance the acceptability of innovative products through online reviews, it is not just highlighting the product advantages of VR, but also suggests that the link between smartphones and applications can bring in more potential users. Also, interworking with other peripheral devices (speakers or screens) can be predicted as a way to increase the acceptability of VR products. From a marketing perspective, this study has found targeted topics that help consumers in pioneering the VR market, which will help potential customers create the services they want.

Fast-paced advancements in technology demand swift adaptation and presents new opportunities and challenges for the optimization of communication, especially for advertisers. Digitalization and new developments in ICT have brought significant changes to the ways in which information, especially promotional messages, is disseminated to consumers. Additionally, with explosive interests in anticipation of fully autonomous vehicles, this study identifies and addresses the potential to optimize communication in an under examined digital media environment – in-vehicle infotainment system. Therefore, this study proposes a text-image embedding method recommender system for the personalization of multimedia contents and advertisements for in-vehicle infotainment systems. Unlike most previous research, which focuses on textual-only or image-only analyses, the current study explores the understanding, development and application of text embedding models and image feature extraction methods simultaneously in the context of target advertisement research. Overall, this study highlights the need to adapt to the ever-evolving technological landscape to optimize communication in various digital media environments. With the proposed text-image embedding method, this study offers a unique approach to personalizing multimedia content and advertisements in the under-explored digital media environment of in-vehicle infotainment systems.

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.

Gate valves are hydraulic components used to shut-off the water flow in water distribution systems. Gate valves may fail owing to various aspects such as leakage through seats, wearing of packing, and corrosion. Because it is considerably challenging to detect valve malfunctioning until the operator identifies a significant fault, failure of the gate valve may lead to a severe accident event associated with water distribution systems. In this study, we proposed a methodology to diagnose the faults of gate valves. To measure the pressure difference across a gate valve, two pressure transducers were installed before and after the gate valve in a pilot-scaled water distribution system. The obtained time-series pressure difference data were analyzed using a machine learning algorithm to diagnose faults. The validation of whether the flow rate of the pipeline can be predicted based on the pressure difference between the upstream and downstream sides of the valve was also performed.

Radioactive wastes, including used nuclear fuel and decommissioning wastes, have been treated using molten salts. Electrochemical sensors are one of the options for in-situ process monitoring using molten salts. However, in order to use electrochemical sensors in molten salt, the surface area must be known. This is because the surface area affects the current of the electrode. Previous studies have used a variety of methods to determine the electrode surface area in molten salts. One method of calculating the electrode surface area is to use the reduction current peak difference between electrodes with known length differences. The method is based on the reduction peak and has the benefit of providing long-term in-situ monitoring of surfaces immersed in molten salt. A number of assumptions have been made regarding this method, including that there is no mass transport by migration or convection; the reaction is reversible and limited by diffusion; the chemical activity of the deposit should be unity; and species should follow linear diffusion. For the purpose of overcoming these limitations, a variety of machine learning algorithms were applied to different voltammogram datasets in order to calculate the surface area. Voltammogram datasets were collected from multiarray electrodes, comprising a multiarray holder, two tungsten rods (1 mm diameter) working electrodes, a quasi-reference electrode, and a counter electrode. The multiarray electrode holder was connected to the auto vertical translator, which uses a servo motor, for changing the height of the rod in the molten salts. To make big and diverse data for training machine learning models, various concentrations of corrosion products (Cr, Fe) and fission products (Eu, Sm) in NaCl-MgCl2 eutectic salts were used as electrolyte; electrolyte temperatures were 500, 525, 550, 575, and 600°C. This study will demonstrate the potential of utilizing machine learning based electrochemical in situ monitoring in molten salt processing.

The increasing use of drones in terrorist attacks highlights the need for effective strategies to prevent and respond to drone terrorism. This study uses machine learning approach to identify factors that predict the success of drone terrorism and suggests policy alternatives for preventing such acts. Drone terrorism is becoming increasingly accessible due to advancements in information and communication technology, and events such as North Korea’s drone infiltration and the Russia-Ukraine war demonstrate the potential threat of drone attacks on Important National Facilities, including nuclear power plants. Using the Global Terrorism Database (GTD), this study analyzed drone terrorism incidents that occurred worldwide from 2016 to 2020. The study employed the Random Forest algorithm, which can incorporate multiple factors and their interactions, making it particularly suitable for social science research. The study provides new insights by deriving predictors that were previously overlooked in empirical analyses of drone terrorism. The findings of this study can aid in the establishment of anti-terrorism policies aimed at addressing the growing threat of drone terrorism. This can include the organization and expansion of the crisis management governance terrorism response council, the creation of a working manual through the partial revision of laws concerning drone terrorism response, and the implementation of anti-drone equipment and systems. Ultimately, the insights gained from this study can provide development of effective strategies aimed at preventing and responding to drone attacks. The study highlights the importance of proactive measures to mitigate the risks posed by drone technology in the context of terrorism.

Radioactive contaminants, such as 137Cs, are a significant concern for long-term storage of nuclear waste. Migration and retention of these contaminants in various environmental media can pose a risk to the surrounding environment. The distribution coefficient (Kd) is a critical parameter for assessing the behavior of these contaminants and can introduce significant errors in predicting migration and remediation options. Accurate prediction of Kd values is essential to assess the behavior of radioactive contaminants and to ensure environmental safety. In this study, we present machine learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict Kd values for Cs in soils. We used three different machine learning models, namely the random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN), to predict Kd values. The models were trained on 14 input variables from the JAEA-SDB, including factors such as Cs concentration, solid phase properties, and solution conditions which are preprocessed by normalization and log transformation. We evaluated the performance of our models using the coefficient of determination (R2) value. The RF, ANN, and CNN models achieved R2 values of over 0.97, 0.86, and 0.88, respectively. Additionally, we analyzed the variable importance of RF using out-of-bag (OOB) and CNN with an attention module. Our results showed that the initial radionuclide concentration and properties of solid phase were important variables for Kd prediction. Our machine learning models provide accurate predictions of Kd values for different soil conditions. The Kd values predicted by our models can be used to assess the behavior of radioactive contaminants in various environmental media. This can help in predicting the potential migration and retention of contaminants in soils and the selection of appropriate site remediation options. Our study provides a reliable and efficient method for predicting Kd values that can be used in environmental risk assessment and waste management.

To conduct numerical simulation of a disposal repository of the spent nuclear fuel, it is necessary to numerically simulate the entire domain, which is composed on numerous finite elements, for at least several tens of thousands of years. This approach presents a significant computational challenge, as obtaining solutions through the numerical simulation for entire domain is not a straightforward task. To overcome this challenge, this study presents the process of producing the training data set required for developing the machine learning based hybrid solver. The hybrid solver is designed to correct results of the numerical simulation composed of coarse elements to the finer elements which derive more accurate and precise results. When the machine learning based hybrid solver is used, it is expected to have a computational efficiency more than 10 times higher than the numerical simulation composed of fine elements with similar accuracy. This study aims to investigate the usefulness of generating the training data set required for the development of the hybrid solver for disposal repository. The development of the hybrid solver will provide a more efficient and effective approach for analyzing disposal repository, which will be of great importance for ensuring the safe and effective disposal of the spent nuclear fuel.