Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

Aluminum alloys, known for their high strength-to-weight ratios and impressive electrical and thermal conductivities, are extensively used in numerous engineering sectors, such as aerospace, automotive, and construction. Recently, significant efforts have been made to develop novel aluminum alloys specifically tailored for additive manufacturing. These new alloys aim to provide an optimal balance between mechanical properties and thermal/ electrical conductivities. In this study, nine combinatorial samples with various alloy compositions were fabricated using direct energy deposition (DED) additive manufacturing by adjusting the feeding speeds of Al6061 alloy and Al-12Si alloy powders. The effects of the alloying elements on the microstructure, electrical conductivity, and hardness were investigated. Generally, as the Si and Cu contents decreased, electrical conductivity increased and hardness decreased, exhibiting trade-off characteristics. However, electrical conductivity and hardness showed an optimal combination when the Si content was adjusted to below 4.5 wt%, which can sufficiently suppress the grain boundary segregation of the α- Si precipitates, and the Cu content was controlled to induce the formation of Al2Cu precipitates.

In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak (Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha-1, with a range of 3.30-8.19 Mg C ha-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through processbased models.

본 연구에서는 중공사형 이산화탄소 분리막 모듈을 사용하여 수소개질기 배가스로부터 이산화탄소 포집을 목적 으로 한 분리막 공정 최적화 연구를 진행하였다. 랩스케일의 소형 분리막 모듈을 사용하여 혼합기체를 대상으로 이산화탄소 순도 90% 및 회수율 90%을 달성하는 2단 공정 조건을 도출하였다. 막 면적이 정해진 모듈의 분리막 공정에서는 스테이지-컷, 주입부 및 투과부 압력에 따라서 포집 순도 및 회수율이 모두 다르게 나타나기 때문에 운전 조건에 대한 최적화가 필수적이 다. 본 연구에서는 다양한 운전 조건에서 1단 분리막에서 보이는 공정 포집 효율의 한계를 확인하고, 높은 순도와 회수율을 동시에 달성하기 위한 2단 회수 공정을 최적화하였다.

This study selected two labor-intensive processes in harsh environments among domestic food production processes. It analyzed their improvement effectiveness using 3-dimensional (3D) simulation. The selected processes were the “frozen storage source transfer and dismantling process” (Case 1) and the “heavily loaded box transfer process” (Case 2). The layout, process sequence, man-hours, and output of each process were measured during a visit to a real food manufacturing factory. Based on the data measured, the 3D simulation model was visually analyzed to evaluate the operational processes. The number of workers, work rate, and throughput were also used as comparison and verification indicators before and after the improvement. The throughput of Case 1 and Case 2 increased by 44.8% and 69.7%, respectively, compared to the previous one, while the utilization rate showed high values despite the decrease, confirming that the actual selected process alone is a high-fatigue and high-risk process for workers. As a result of this study, it was determined that 3D simulation can provide a visual comparison to assess whether the actual process improvement has been accurately designed and implemented. Additionally, it was confirmed that preliminary verification of the process improvement is achievable.

Process-based models are effective in addressing spatially explicit dispersal of invasive species based on life mechanisms including birth, death, movement and response to environmental factors. An invading alien species, the western conifer seed bug (Leptoglossus occidentalis), spreads rapidly in the Korean peninsula since 1988. Process-based models were developed to include the rules occurred in population dynamics of the western conifer seed bug population. Passive movements were additionally linked to the models to present local and global transportations due to sapling trades. Simulation results presented the rapid dispersal of the pest species, comparable to field data. Model parameters including the Alle effect threshold and contribution of global transportation were adjusted to reveal spatially-explicit advancement patters of the species. Utilization of process-based models is further discussed in monitoring and management of forest insect pests in field conditions.

In order to solve the rapidly increasing domestic delivery volume and various problems in the recent metropolitan area, domestic researchers are conducting research on the development of “Urban Logistics System Using Underground Space” using existing urban railway facilities in the city. Safety analysis and scenario analysis should be performed for the safe system design of the new concept logistics system, but the scenario analysis techniques performed in previous studies so far do not have standards and are defined differently depending on the domain, subject, or purpose. In addition, it is necessary to improve the difficulty of clearly defining the control structure and the omission of UCA in the existing STPA safety analysis. In this study, an improved scenario table is proposed for the AGV horizontal transport device, which is a key equipment of an urban logistics system using underground space, and a process model is proposed by linking systematic STPA safety analysis and scenario analysis, and UCA and Control Structure Guidelines are provided to create a safety analysis.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

The most important thing in development of a process-based TSPA (Total System Performance Assessment) tool for large-scale disposal systems (like APro) is to use efficient numerical analysis methods for the large-scale problems. When analyzing the borehole in which the most diverse physical phenomena occur in connection with each other, the finest mesh in the system is applied to increase the analysis accuracy. Since thousands of such boreholes would be placed in the future disposal system, the numerical analysis for the system becomes significantly slower, or even impossible due to the memory problem in cases. In this study, we propose a tractable approach, so called global-local iterative analysis method, to solve the large-scale process-based TSPA problem numerically. The global-local iterative analysis method goes through the following process: 1) By applying a coarse mesh to the borehole area the size of the problem of global domain (entire disposal system) is reduced and the numerical analysis is performed for the global domain. 2) Solutions in previous step are used as a boundary condition of the problem of local domain (a unit space containing one borehole and little part of rock), the fine mesh is applied to the borehole area, and the numerical analysis is performed for each local domain. 3) Solutions in previous step are used as boundary conditions of boreholes in the problem of global domain and the numerical analysis is performed for the global domain. 4) steps 2) and 3) are repeated. The solution derived by the global-local iterative analysis method is expected to be closer to the solution derived by the numerical analysis of the global problem applying the fine mesh to boreholes. In addition, since local problems become independent problems the parallel computing can be introduced to increase calculation efficiency. This study analyzes the numerical error of the globallocal iterative analysis method and evaluates the number of iterations in which the solution satisfies the convergence criteria. And increasing computational efficiency from the parallel computing using HPC system is also analyzed.

In the engineered barrier system of deep geological disposal repository, complex physicochemical phenomena occur throughout the entire disposal time, consequently impacting the safety function. The bentonite buffer, a significant component of the engineered barrier system, can be geochemically altered due to the changes in host rock groundwater, temperature, and redox condition. Such changes may have direct or indirect effects on radionuclide migration in case of canister failure. Therefore, a modeling tool that accounts for coupled thermal-hydraulic-mechanical-chemical (THMC) processes is necessary for the safety assessment. To this end, the Korea Atomic Energy Research Institute (KAERI) has developed the APro, a modeling interface for conducting safety assessment of deep geological disposal repository. The APro considers coupled THMC processes that influence radionuclide migration. Here, the solute transport considering thermal and hydraulic processes are calculated using the COMSOL multi-physics, while geochemical reactions are carried out in PHREEQC. The two software are coupled using a sequential non-iterative operator splitting approach, and transport of non-water H, non-water O, and charge were additionally considered to enhance the coupling model stability. Finally, the applicability of APro to simulate long-term geochemical evolution of bentonite was demonstrated through benchmark studies to evaluate the effects of mineral precipitation/dissolution, temperature, redox, and seawater intrusion.

본 연구는 교수자가 학습자를 위해 긍정적 가치탐색을 효과적으로 적용할 수 있도록 4D 프로세스 기반 학습모형을 개발하고 학습유형을 분류하여 연구하는 것을 목적으로 하였다. 긍정적 가치탐색 교육 방법은 학습자의 사고방식과 행동 변화에 효과적이다. 또한, 의미와 가치발견에 중점을 둔 강점 기반 접근을 통해 학습 참여를 증진하고 지속 가능한 학습 환경과 배움을 실현할 수 있다. 이러한 교육적 효과는 긍정적 가 치 탐색의 4D 프로세스를 토대로 한 활동으로 이루어진다. 교육 현장에서 긍정적 가치탐색 4D 프로세스 를 보다 유용하게 활용하기 위해서는 교육목표와 지향하는 역량개발에 따라 4D 프로세스에 적합한 학습 유형 분류와 체계적이고 구조화된 학습모형 개발이 필요하다. 본 연구는 4D 프로세스 기반 4가지 학습유 형을 구조화하여 학습모형을 개발하고 모형타당화를 진행하였다. 4D 프로세스 기반 학습모형 구성요소 도 출은 선행 문헌의 검토와 분석을 통해 이루어졌고, 구성요소의 구조화는 사례연구를 통해 진행하였다. 그 리고 해당 분야 전문가 검토를 통한 타당성 평가를 3차에 걸쳐 실시하였다. Discover, Dream, Design, Destiny 4D 프로세스는 탐색과 발견, 사고와 상상, 공유와 구성, 발표와 실천으로 개선되어 적용되었다. 학습에 적합하도록 보완된 4D 프로세스는 도달할 학습 목표와 개발할 학습자의 역량에 따라 탐구형, 창의 형, 과제해결형, 실천형으로 세분화하여 개발되었다. 개발된 학습모형에서의 학습유형은 다양한 교육 환경 에 맞게 긍정적 가치탐색 활동이 선택적으로 운영될 수 있다는 이점이 있다.

Spin-off pyroprocessing technology and inert anode materials to replace the conventional carbon-based smelting process for critical materials were introduced. Efforts to select inert anode materials through numerical analysis and selected experimental results were devised for the high-throughput reduction of oxide feedstocks. The electrochemical properties of the inert anode material were evaluated, and stable electrolysis behavior and CaCu generation were observed during molten salt recycling. Thereafter, CuTi was prepared by reacting rutile (TiO2) with CaCu in a Ti crucible. The formation of CuTi was confirmed when the concentration of CaO in the molten salt was controlled at 7.5mol%. A laboratory-scale electrorefining study was conducted using CuTi(Zr, Hf) alloys as the anodes, with a Ti electrodeposit conforming to the ASTM B299 standard recovered using a pilot-scale electrorefining device.

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.