This study addresses the optimal design methodology for switching between active electronically scanned array (AESA) radar operating modes to easily select the necessary information to reduce pilots' cognitive load and physical workload in situations where diverse and complex information is continuously provided. This study presents a procedure for defining a hidden Markov chain model (HMM) for modeling operating mode changes based on time series data on the operating modes of the AESA radar used by pilots while performing mission scenarios with inherent uncertainty. Furthermore, based on a transition probability matrix (TPM) of the HMM, this study presents a mathematical programming model for proposing the optimal structural design of AESA radar operating modes considering the manipulation method of a hands on throttle-and-stick (HOTAS). Fighter pilots select and activate the menu key for an AESA radar operation mode by manipulating the HOTAS’s rotary and toggle controllers. Therefore, this study presents an optimization problem to propose the optimal structural design of the menu keys so that the pilot can easily change the menu keys to suit the operational environment. K

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.

The topic of this study is the field of humanitarian logistics for disaster response. Many existing studies have revealed that compliance with the golden time in response to a disaster determines the success or failure of relief activities, and logistics costs account for 80% of the disaster response cost. Besides, the agility, responsiveness, and effectiveness of the humanitarian logistics system are emphasized in consideration of the disaster situation’s characteristics, such as the urgency of life-saving and rapid environmental changes. In other words, they emphasize the importance of logistics activities in disaster response, which includes the effective and efficient distribution of relief supplies. This study proposes a mathematical model for establishing a transport plan to distribute relief supplies in a disaster situation. To determine vehicles’ route and the amount of relief for cities suffering a disaster, it mainly considers the urgency, effectiveness (restoration rate), and uncertainty in the logistics system. The model is initially developed as a mixed-integer nonlinear programming (MINLP) model containing some nonlinear functions and transform into a Mixed-integer linear programming (MILP) model using a logarithmic transformation and piecewise linear approximation method. Furthermore, a minimax problem is suggested to search for breakpoints and slopes to define a piecewise linear function that minimizes the linear approximation error. A numerical experiment is performed to verify the MILP model, and linear approximation error is also analyzed in the experiment.

An aircraft engine is supposed to be used for a specified lifespan, and it is reused through the depot maintenance for a major overhaul when its cumulative service life reaches the lifespan. However, the depot maintenance takes a long time due to the complexity of its process, and thus to continuously operate aircraft, spare engines are required to substitute engines under renovation. Considering the limited capacity of the depot, the uniformity of the quantity and cycle of engines entering the depot maintenance provides an opportunity to the decrease of the spare engines and the improvement of aircraft availability. It is the purpose of engine life management. Furthermore, in establishing flight plans, the time for essential maintenance activities, such as pre/post-flight inspection, servicing, the preparation for next flight, has to guarantee. Especially, fighters additionally require much time for installing weapons for their mission. That is, the rearming procedure can be left out if the adjoining missions are identical. Otherwise, the rearming time is varyingly spent depending on the types of adjacent duties. Therefore, this study proposes the mathematical model for an aircraft-mission assignment considering engine life management and maintenance schedule, and it is formulated based on the time-space network. Moreover, to verify and validate the model, an example was developed by applying realistic aircraft operating environment and simulation to perform air operations for several days was fulfilled. The experimental results presented flight plans corresponding to the purpose of this study, such as engine life management and the assurance of maintenance time.

In the satellite operation phase, a ground station should continuously monitor the status of the satellite and sends out a tasking order, and a satellite should transmit data acquired in the space to the Earth. Therefore, the communication between the satellites and the ground stations is essential. However, a satellite and a ground station located in a specific region on Earth can be connected for a limited time because the satellite is continuously orbiting the Earth, and the communication between satellites and ground stations is only possible on a one-to-one basis. That is, one satellite can not communicate with plural ground stations, and one ground station can communicate with plural satellites concurrently. For such reasons, the efficiency of the communication schedule directly affects the utilization of the satellites. Thus, in this research, considering aforementioned unique situations of spacial communication, the mixed integer programming (MIP) model for the optimal communication planning between multiple satellites and multiple ground stations (MS-MG) is proposed. Furthermore, some numerical experiments are performed to verify and validate the mathematical model. The practical example for them is constructed based on the information of existing satellites and ground stations. The communicable time slots between them were obtained by STK (System Tool Kit), which is a well known professional software for space flight simulation. In the MIP model for the MS-MG problems, the objective function is also considered the minimization of communication cost, and ILOG CPLEX software searches the optimal schedule. Furthermore, it is confirmed that this study can be applied to the location selection of the ground stations.

With the increased interest in the quality of life of modern people, the implementation of the five-day working week, the increase in traffic convenience, and the economic and social development, domestic and international travel is becoming commonplace. Furthermore, in the past, there were many cases of purchasing packaged goods of specialized travel agencies. However, as the development of the Internet improved the accessibility of information about the travel area, the tourist is changing the trend to plan the trip such as the choice of the destination. Web services have been introduced to recommend travel destinations and travel routes according to these needs of the customers. Therefore, after reviewing some of the most popular web services today, such as Stubby planner (http://www.stubbyplanner.com) and Earthtory (http://www.earthtory.com), they were supposed to be based on traditional Traveling Salesman Problems (TSPs), and the travel routes recommended by them included some practical limitations. That is, they were not considered important issues in the actual journey, such as the use of various transportation, travel expenses, the number of days, and lodging. Moreover, although to recommend travel destinations, there have been various studies such as using IoT (Internet of Things) technology and the analysis of cyberspatial Big Data on the web and SNS (Social Networking Service), there is little research to support travel routes considering the practical constraints. Therefore, this study proposes a new mathematical model for applying to travel route recommendation service, and it is verified by numerical experiments on travel to Jeju Island and trip to Europe including Germany, France and Czech Republic. It also expects to be able to provide more useful information to tourists in their travel plans through linkage with the services for recommending tourist attractions built in the Internet environment.

NSS (Navigation satellite system) provides the information for determining the position, velocity and time of users in real time using satellite-networking, and is classified into GNSS (Global NSS) and RNSS (Regional NSS). Although GNSS services for global users, the exactitude of provided information is dissatisfied with the degree required in modern systems such as unmanned system, autonomous navigation system for aircraft, ship and others, air-traffic control system. Especially, due to concern about the monopoly status of the countries operating it, some other countries have already considered establishing RNSS. The RNSS services for users within a specific area, however, it not only gives more precise information than those from GNSS, but also can be operated independently from the NSS of other countries. Thus, for Korean RNSS, this paper suggests the methodology to design the satellite constellation considering the regional features of Korean Peninsula. It intends to determine the orbits and the arrangement of navigation satellites for minimizing PDOP (Position dilution of precision). PGA (Parallel Genetic Algorithm) geared to solve this nonlinear optimization problem is proposed and STK (System tool kit) software is used for simulating their space flight. The PGA is composed of several GAs and iterates the process that they search the solution for a problem during the pre-specified generations, and then mutually exchange the superior solutions investigated by each GA. Numerical experiments were performed with increasing from four to seven satellites for Korean RNSS. When the RNSS was established by seven satellites, the time ratio that PDOP was measured to less than 5 (i.e. better than ‘Good’ level on the meaning of the PDOP value) was found to 94.3% and PDOP was always kept at 10 or less (i.e. better than ‘Moderate’ level).

In order to manage essential parts that are required for the repairable parts services performed at the military maintenance depots, the United States Air Force developed the Repairability Forecasting Model (RFM). In the RFM, if the requirements of the parts are assumed to follow the normal probability distribution after applying means from the past data to the replacement rate and lead times, the chance of the AWP (Awaiting Parts) occurring is 50%. In this study, to counter the uncertainties of requirements and lead times from the RFM, the safety level concept is considered. To obtain the safety level for requirements, the binomial probability distribution is applied, while the safety level for lead time is obtained by applying the normal probability distribution. After adding this concept, the improved RFM is renamed as the ARFM (Advanced RFM), and by conducting the numerical stimulation, the effectiveness of the ARFM, minimizing the occurrence of the AWP, is shown by increasing the efficiency of the maintenance process and the operating rate of the weapon system.

본 연구에서는 나노기술을 이용하여 에너지 감쇄 기능을 활용한 내진용 감쇄기 개발에 대한 연구를 하였다. 구조물에 사용되는 기존의 유체 감쇄기를 대체하는 무기재료를 이용한 새로운 감쇄기에 대한 기초적 연구를 하였다. 완충역할을 하는 입자로는 미로구조를 가지는 실리카겔을 사용하였으며, 입자에 관련한 작동 유체로는 물을 사용하여 그 효과를 검증하였다. 콜로이드 감쇄기를 구현하기 위해서는 형성된 실리카 겔 입자의 표면을 유기 실리콘 매질을 이용한 소수화 코팅 처리를 하였다. 콜로이드 감쇄기의 이력곡선은 서로 다른 소수화 코팅 처리가 된 입자의 혼합과 소수화 처리시의 분자간 거리에 의해서 조절이 가능함을 알 수 있었다. 콜로이드 감쇄기의 에너지 소산양은 상이한 소수화 처리 정도와 서로 코팅처리량이 서로 다른 재료를 혼합함으로써 제어 할 수 있음을 확인하였다. 기존의 유압 감쇄기에서는 나타나지 않는 이러한 특징은 콜로이드 감쇄기가 충격 흡수 장치로 사용 가능하다는 사실을 입증한다.