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.

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.

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

The management of algal bloom is essential for the proper management of water supply systems and to maintain the safety of drinking water. Chlorophyll-a(Chl-a) is a commonly used indicator to represent the algal concentration. In recent years, advanced machine learning models have been increasingly used to predict Chl-a in freshwater systems. Machine learning models show good performance in various fields, while the process of model development requires considerable labor and time by experts. Automated machine learning(auto ML) is an emerging field of machine learning study. Auto ML is used to develop machine learning models while minimizing the time and labor required in the model development process. This study developed an auto ML to predict Chl-a using auto sklearn, one of most widely used open source auto ML algorithms. The model performance was compared with other two popular ensemble machine learning models, random forest(RF) and XGBoost(XGB). The model performance was evaluated using three indices, root mean squared error, root mean squared error-observation standard deviation ratio(RSR) and Nash-Sutcliffe coefficient of efficiency. The RSR of auto ML, RF, and XGB were 0.659, 0.684 and 0.638, respectively. The results shows that auto ML outperforms RF, and XGB shows better prediction performance than auto ML, while the differences between model performances were not significant. Shapley value analysis, an explainable machine learning algorithm, was used to provide quantitative interpretation about the model prediction of auto ML developed in this study. The results of this study present the possible applicability of auto ML for the prediction of water quality.

For a plastic diffusion lens to uniformly diffuse light, it is important to minimize deformation that may occur during injection molding and to minimize deformation. It is essential to control the injection molding condition precisely. In addition, as the number of meshes increases, there is a limitation in that the time required for analysis increases. Therefore, We applied machine learning algorithms for faster and more precise control of molding conditions. This study attempts to predict the deformation of a plastic diffusion lens using the Decision Tree regression algorithm. As the variables of injection molding, melt temperature, packing pressure, packing time, and ram speed were set as variables, and the dependent variable was set as the deformation value. A total of 256 injection molding analyses were conducted. We evaluated the prediction model's performance after learning the Decision Tree regression model based on the result data of 256 injection molding analyses. In addition, We confirmed the prediction model's reliability by comparing the injection molding analysis results.

본 연구는 데이터를 기반으로 한 인공지능 기계학습 기법을 활용하여 온실 내부온도 예측 시뮬레이션 모델을 개발을 수행 하였다. 온실 시스템의 내부온도 예측을 위해서 다양한 방법 이 연구됐지만, 가외 변인으로 인하여 기존 시뮬레이션 분석 방법은 낮은 정밀도의 문제점을 지니고 있다. 이러한 한계점 을 극복하기 위하여 최근 개발되고 있는 데이터 기반의 기계 학습을 활용하여 온실 내부온도 예측 모델 개발을 수행하였 다. 기계학습모델은 데이터 수집, 특성 분석, 학습을 통하여 개 발되며 매개변수와 학습방법에 따라 모델의 정확도가 크게 변 화된다. 따라서 데이터 특성에 따른 최적의 모델 도출방법이 필요하다. 모델 개발 결과 숨은층 증가에 따라 모델 정확도가 상승하였으며 최종적으로 GRU 알고리즘과 숨은층 6에서 r2 0.9848과 RMSE 0.5857℃로 최적 모델이 도출되었다. 본 연 구를 통하여 온실 외부 데이터를 활용하여 온실 내부온도 예 측 모델 개발이 가능함을 검증하였으며, 추후 다양한 온실데이 터에 적용 및 비교분석이 수행되어야 한다. 이후 한 단계 더 나아 가 기계학습모델 예측(predicted) 결과를 예보(forecasting)단 계로 개선하기 위해서 데이터 시간 길이(sequence length)에 따른 특성 분석 및 계절별 기후변화와 작물에 따른 사례별로 개발 모델을 관리하는 등의 다양한 추가 연구가 수행되어야 한다.

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry’s yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO2 and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of ‘Seolhyang’ (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

In this research, we evaluate on the disassemblability of recycling process for vehicle front door using the symbolic chart method and machine-learning algorithm. It is applied to the front door of 1600cc class vehicle, and then the conventional steel door and CFRP door were compared. Based on the principle symbolic chart method, the number of processes can be different according to decomposer proficiency of suitability of recycling process, so the evaluation method is required to supply this issue. The machine learning algorithm, and artificial intelligence method were applied and the applicable tools for each experiment were used to compensate the variations in the number of processes according to different proficiencies. Because CFRP front door has integrated components compare to steel door, so its disassemblability processes were decreased to 80 from 103 of the conventional steel door’s. It can be confirmed that the disassemblability was increased from the suitability of recycling equation. In case of the steel, disassemblability was approximately 60.6, in case of the CFRP is approximately 72 for car front door. Therefore, it can be concluded that the disassemblability of CFRP was better in the evaluation of suitability of recycling.

역삼투 해수담수화 공정에서 막 오염은 생산수량 감소 및 공정의 에너지 소비량 증가를 야기한다. 막간 차압 증가, 생산수량 감소 외에 막 저항 값의 증가는 막 오염 정도를 판단하는 수치로 사용이 가능하다. 특히 막 저항 값 기반의 세정은 막 오염 제어를 통해 역삼투 해수담수화 공정에서 막의 성능 유지 시 사용 가능하다. 이에 본 연구에서는 해수 수질 인자 및 공정 운전 인자에 기반하여 막 저항 값을 예측하는 알고리즘을 제안한다. 알고리즘은 해수담수화 플랜트의 운전 데이터에 기반하여 인자들과 막 저항 값 사이의 관계를 학습하고 검증과정을 거쳐 막 오염 발생 시점을 사전에 예측하는 방식으로 개발되었다. 예측 정확도를 분석하고 개발된 알고리즘의 수정을 통해 예측 정확도 향상을 위한 연구를 진행하였다.