대부분의 원전 설비의 내진 해석에는 해석이 비교적 간편하고, 설계에 보수성을 적절히 반영할 수 있어 대부분 기기가 설치된 위치에서의 층응답스펙트럼 혹은 In-structure response spectrum을 이용한 응답스펙트럼 해석을 주로 이용하고 있다. 설비 공급자 는 설계 시방서에 층응답스펙트럼 선도의 형태로 입력 지진파 자료를 받게 되는데, 필요시 이를 바탕으로 인공 지진파을 만들어 해석 혹은 시험을 수행한다. 설계지반응답스펙트럼의 경우 RG 1.60에 주어지고 SRP 3.7.1의 요건에 따라 인공 지진파 시간 이력을 생성하 나, 층응답스펙트럼의 경우 명확은 기준이 없어 이를 따르고 있다. 층응답스펙트럼은 구조물의 동특성이 반영되기 때문에 지반응답스 펙트럼에 비해 형태가 복잡하여 기존의 P-CARES 등의 인공 지진파 생성 프로그램을 이용할 경우 SRP 3.7.1의 요건에 맞는 시간 이력 인공 지진파를 얻기 위해서는 상당한 노력이 필요하다. 본 연구에서는 수치 최적화를 이용하여 복잡한 형태의 층응답스펙트럼이 라도 SRP 3.7.1의 요건 내에서 그 형태를 따르는 인공 지진파 시간 이력을 효율적으로 생성할 수 있는 절차를 개발하였다.

강화학습은 지속적으로 변화하는 환경에서 최적의 해결책을 제시할 수 있도록 구현되는 머신러닝 알고리즘으로 시간 및 조건에 따라 변화하는 시스템의 최적화에 우수한 성능을 보이는 장점을 가지고 있다. 따라서, 최근 운영 조건과 시간에 따라 변화하는 상하수도 시설 및 취수원 등 현장 물환경 관리 최적화에 강화학습을 적용하기 위한 연구에 대한 관심이 높아지고 있다. 본 연구에서는 강화학습이 상하수도 시설 및 물환경 관리에 적용된 사례를 분석하였다. 상하수도 시설의 운영시 시설 운영의 목적에 맞는 처리수 수질을 유지하면서 운영에 필요한 에너지 소비 및 비용을 최소화하는 노력이 중요하다. 강화학습은 데이터에 기반한 반복적인 분석을 통해 시스템 운영의 최적 조건을 학습할 수 있으며, 다양한 연구 사례에서 강화학습의 적용을 통해 상하수도 시설 등의 운영 효율 개선이 가능함을 보여주었다. 하수처리 시설의 경우 강화학습을 활용하여 운영비의 많은 부분을 차지하는 폭기조 산소 공급과 내부 반송 펌프 운전을 최적화할 수 있으며, 정수장의 경우 약품 투입량 절감 등을 통해 운영비 절감 효과를 달성할 수 있음을 확인하였다. 또한, 용수 공급망과 저류조 운영의 최적화를 통해 상수도 및 하천 현장의 오염 발생을 저감할 수 있음을 알 수 있었다. 본 연구를 통해 강화학습을 활용하여 기존의 경험에 기반한 시설 운영 방식의 한계를 개선하고 상하수도 시설 운영 및 물환경 관리 효율 향상에 기여할 수 있음을 확인하였다

In order to confirm the optimal conditions for the LED(Light Emitting Diode) wire bonding process, the lead bump ball process optimization was analyzed. In the wire bonding process, it is an important process in which electrical characteristics operate by connecting the Au wire to the LED chip and lead frame. In the wire bonding method, various wire bonding processes, including thermocompression and ultrasonic bonding, were dealt with, and various variables affecting the lead bump ball process of wire bonding were analyzed through process variable analysis. Key variables for wire bonding working conditions were identified and optimized using the Response Surface Method(RSM) of Design of Experiments(DOE), the interaction between variables was confirmed through factor setting experiments, and the process was optimized using the RSM. This paper aims to improve the performance of the lead bump ball process by designing experiments with 5 factors at 3 levels and analyzing 4 response variables to find optimal conditions. It was confirmed that the performance of the lead bump ball process improved under optimized conditions, and as a result, optimal conditions that satisfied the targets for most reaction values, with the exception of ball diameter (BD), were secured.

This study investigates the impact of solar paper panel tilt angles on the flight endurance of solar powered the drone. To address the limited flight time of conventional battery powered drones, photovoltaic solar paper panels were mounted at varying angles 0°, 15°, 30°, and 45° tested under consistent conditions. Experimental results showed that a 30° tilt angle produced the highest power output, leading to about 14% increase in flight duration compared to a flat configuration. These findings demonstrate that optimizing panel orientation significantly improves energy efficiency and drone performance. This work provides practical insight into the design of lightweight solar UAVs and highlights the feasibility of simple tilt adjustments as a low complexity alternative to active solar tracking systems.

하수처리장 유출수의 수질 예측은 수질 사고의 사전 대응 및 처리장의 안정적인 운영을 위해 필수적인 요소이다. 최근 머신러닝을 활용한 예측 모델링에서 예측 성능 향상과 과적합 방지를 위해 다양한 교차 검증법과 하이퍼파라미터 최적화 기법이 활용되고 있으나, 하수처리장 데이터는 시간적 의존성과 급격한 변동성이 내재되어 있어 과적합에 취약하고 안정적인 모델 구축에 어려움이 따른다. 본 연구에서는 이러한 데이터 특성을 효과적으로 반영할 수 있는 최적의 모델링 파이프라인을 구축하고자 하였으며, XGBoost 모델을 기반으로 유출수 내 총질소 농도를 예측하였다. 예측 성능 평가 지표로는 평균 제곱근 오차(Root Mean Square Error, RMSE), 결정계수(coefficient of determination, R2), RMSE 오차 개선율(the rate of improvement on RMSE, RIRRMSE) 그리고 계산 시간을 사용하였다. 기본적인 Hold-out 방식의 성능을 기준으로 K-fold, 시계열 교차 검증(Time Series Cross Validation, TSCV), 블록 시계열 교차 검증(Blocked Time Series Cross Validation, BTSCV) 기법의 예측 성능을 분석한 결과, BTSCV는 인접한 데이터만을 고려하는 방식으로 시간적 의존성과 급변 특성을 효과적으로 반영하여 가장 높은 RIR(36.37%)을 기록하였다. 또한, 하이퍼파라미터 최적화(그리드 서치와 베이지안 최적화) 기법과의 다양한 교차 검증법의 조합을 통해 모델 성능과 과적합 방지 효과를 분석한 결과, BTSCV와 베이지안 최적화의 결합은 짧은 계산 시간(364.64초)과 함께 가장 높은 RIR(64.93%)을 보였으며, 훈련 및 평가 데이터 간 성능 차이도 최소화되어 일반화된 예측 모델로서의 효과성이 입증되었다. 따라서 본 연구는 하수처리장 시계열 데이터의 특성에 적합한 BTSCV와 베이지안 최적화 기법을 결합한 모델링 파이프라인 전략을 제안하며, 향후 실시간 수질 모니터링 및 하수처리장 운영 효율성 제고에 기여할 수 있을 것으로 기대된다.

본 연구는 풋고추 수경재배에 ICT 기술을 적용하여 측정된 누적 일사량을 기반으로 급액량을 설정하고, 이를 바탕으로 실생묘와 접목묘의 생육 및 생산성에 미치는 영향을 평가하고 자 수행되었다. 실험재료로는 실생묘 ‘순한길상’과 접목묘 ‘순한길상’(접수) + ‘BN901’(대목)을 사용하였으며, 65－70 일 간 육묘한 뒤 코이어배지에 8월 하순에 정식하여 이듬해 4 월까지 재배하였다. 급액량은 ICT 센서를 통해 측정한 누적 일사량을 기준으로 T1－T4의 4수준으로 처리하였다. 풋고 추 실생묘 수경재배 시 급액량 T2(생육초기－중기: 50－ 160mL·100J-1·plant-1) 처리 이상에서 생육초기 과실의 품질 차이가 일부 있었으나, 정식 후 54일차 이후로는 품질에 차이 가 없고, 생산량 증가도 미미하기 때문에 양액 사용효율을 고 려하였을 때 적정 급액량은 T2로 판단되며, 이때의 배액률은 사분위수 범위(Q3-Q1)를 기준으로 생육초기 13－31%, 생 육중기 20－34%로 관찰되었다. 접목묘의 경우 T4(70－240) 처리에서 총생산량 6,306g·plant-1으로 유의하게 높았으며, 실생묘 T4 처리구 보다 약 34% 증수하였다. 이를 통해 풋고추 수경재배의 접목묘 도입 시 장기간 세력 유지와 생산성 향상 에 유리하며, 실생묘와는 차별화된 급액 전략이 필요할 것으 로 판단된다. 또한, 엽병 즙액 및 엽 조직 내 총질소, 총인, 칼륨 함량 분석 결과, 생육 후기로 갈수록 총질소와 총인은 감소하 고 칼륨은 증가하는 경향을 보였으며, 실생묘에서 엽병 즙액 내 N, P, K 함량이 전반적으로 높게 나타났다. 그러나 엽병 즙 액 분석값과 엽 조직 내 함량 간 상관성은 질소에서 낮았고, 생 산성과의 관련성도 미미하여, 즙액 분석을 활용한 생육 진단 의 신뢰도를 높이기 위해서는 시료 채취 위치, 수분 상태, 엽령 등 명확한 기준 설정이 요구된다.

As demand grows for electric vehicles and advanced mobility technologies, developing materials for permanent magnets has become increasingly essential. Among them, SmCo-based permanent magnets are gaining attention due to their superior thermal stability compared to conventional NdFeB magnets, making them promising candidates for high-temperature motor applications. However, optimizing the magnetic properties of SmCo alloys remains challenging due to their complex phase structures and elemental interactions. In this study, we develop and optimize machine learning (ML) models to predict the saturation magnetization of SmCo permanent magnets using only composition-based descriptors. A dataset comprising various SmCo alloys was analyzed, with features extracted using Matminer and Pymatgen modules. We applied Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Regression (SVR) models and compared their regression performance using R2 score and Root-mean-squared-error (RMSE). The RF model demonstrated the best generalization and prediction accuracy. To identify the most influential features, we used permutation feature importance. Further, we refined the feature set using a genetic algorithm (GA), ultimately selecting 9 key features that yielded the highest model performance (R2 = 0.963, RMSE = 4.22 emu/g). This study highlights the potential of combining machine learning with genetic optimization to accelerate the design of high-performance, thermally stable SmCo permanent magnets.

To optimize the electrochemical properties of Ni-rich cathode materials, CPAN@SC-NCM811 is prepared via surface modification of single-crystalline LiNi0.8Co0.1Mn0.1O2 cathode material by adding 1, 2 and 3 wt.% of polyacrylonitrile, respectively. Significantly, the results obtained from X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM) verify the successful synthesis of CPAN@SC-NCM811 cathode, which exhibits better electrochemical properties compared to SC-NMC811. After thorough milling and calcination of 2 wt.% polyacrylonitrile with SC-NCM811, the initial discharge specific capacity of prepared S2 sample is 197.7 mAh g− 1 and the capacity retention reached 89.2% after 100 cycles at a rate of 1.0 C. Furthermore, the S2 sample exhibits superior rate performance compared to the other three samples, in which these superior electrochemical properties are largely attributed to the optimal ratio of conductive cyclized polyacrylonitrile coatings. Overall, this work offers guidelines for modifying the surface of SC-NCM811 cathode materials for lithium-ion batteries with exceptional cycling and rate performance.

This paper describes the use of approximation in Collaborative Optimization (CO) method, one of the Multidisciplinary Design Optimization (MDO) techniques. The approximation is used to model the result of a disciplinary design, optimal discrepancy function value, as a function of the interdisciplinary target variables passed from system level to the discipline. The optimal discrepancy function value is used to examine the interdisciplinary compatibility constraint (discrepancy function = 0) duringthe system level optimization. However, the peculiar shape of the compatibility constraint makes it difficult to exploit well–developed conventional approximation methods. This paper introduces the combination of neural network classification and kriging to resolve this problem. In addition, for the purpose of enhancing the accuracy of the approximation, the approximation is continuously updated using the information obtained from the system level optimization. This iterative process is continued until acceptable convergence is achieved.

Spirodela polyrhiza (L.) has been known as greater duckweed or great duckmeat. It is native inhabited in Korea. It is considered as a rich source of primary metabolites including protein, carbohydrates, and fats. Thus, it is considered as an alternative food source for the future. In addition, it has a strong phytoremediation capacity to remove various environmental pollutants, especially inorganic elements and pesticides. With a variety of duckweed’s application, there is an urgent need to develop a cultivation method for a sustainable supply of S. polyrhiza. In this study, an indoor vertical farm has been introduced to optimize duckweed cultivation. Indoor cultivated S. polyrhiza showed about 2-fold higher fresh weight than outdoor cultivated duckweed. Contents of inorganic elements were also significantly reduced in indoor cultivated S. polyrhiza. Especially, lead (Pb), cadmium (Cd), and arsenic (As) were approximately 10-fold decreased in indoor cultivated duckweed. On the other hand, contents of proteins and fats were significantly increased in indoor cultivated S. polyrhiza, while carbohydrates were found more in outdoor cultivated S. polyrhiza. Increasing N content in a homemade nutrition solution also enhanced fresh and dried weights of S. polyrhiza by about 1.8-fold in comparison with other commercial nutrition solutions. Proliferation rate (%) was doubled every 24 hours in this indoor vertical farm, indicating the accomplishment of a sustainable supply for S. polyrhiza. Further studies need to be undertaken to cultivate other duckweeds such as Wolffia arrhiza and Lemna minor using the same indoor farming system.

To address the issues of slow magnetization current tracking speed, prolonged magnetization time, and low accuracy during magnetic particle testing of ship castings, forgings, and welded components, this study designed a high-precision rapid current tracking control system. By integrating the predictive characteristics of the Newton interpolation algorithm with the robustness of PID control, a compound control algorithm with a pre-judgment mechanism was developed. An innovative three-phase zero-crossing detection circuit architecture was also implemented, combining high-speed A/D converters and CS5460 chips to optimize current tracking methods, resolving the conflict between initial tracking phase deviation and dynamic process overshoot in conventional approaches. Experimental results demonstrated that this method significantly improves magnetization speed, achieving target current tracking within 0.5 seconds with errors below 2%, meeting the design requirements for non-destructive testing in ship welding applications.

Due to cognitive differences, traditional perceptual engineering (KE) frequently relies too heavily on designers' experience in analyzing customers' emotional demands, which can result in product designs that deviate from users' expectations. This work suggests a thorough evaluation approach that combines the particle swarm optimization-support vector regression (PSO-SVR) model and perceptual engineering to increase the scientificity and precision of design choices. The approach first determines the subjective weights of users' emotional needs using spherical fuzzy hierarchical analysis (SFAHP). Next, it uses the entropy weighting method to determine the objective weights. Finally, it combines the subjective and objective data using game theory to produce a more rational evaluation system. Finally, the emotional prediction model based on PSO-SVR is constructed to realize the accurate mapping between emotional needs and design features. The empirical study shows that“speed”, “dynamic”and“luxury” are the core emotional demands of users, and the algorithm's prediction results are highly consistent with users' actual evaluations, which strongly verifies the accuracy of the model. Compared with the traditional KE method, the model better integrates subjective experience and objective data and provides more practical support for the design of flybridge yachts.

The number of significant issues on many welding processes are often connected to high productivity and manufacturability at low costs. The research on welding processes in the literature has reported several research activities, but there is still scope for improvement in most industrial settings. The primary goal of this research is to determine the best super-TIG welding settings to use for groove welding. First, in order to determine the quality characteristics and risks associated with them, concepts and frameworks of quality by design (QbD) which is a new standard in pharmaceutical area in order to improve drug qualities were integrated into this process optimization. Second, stepwise experimental design approaches including a factorial design as well as a response surface methodology (RSM) were customized and performed for this specific automated super-TIG welding process. Third, based on experimental design results, the optimal operating conditions with both design space (i.e., acceptable range of operating conditions) and safe operating space (i.e., safe range of operating conditions) were obtained. Finally, a case study including QbD steps, stepwise experimental design approaches, design and operating spaces, the optimal factor settings, and their association validation results was conducted for verification purposes.

In the context of increasingly uncertain maritime logistics environments, container Demurrage and Detention (D&D) charges pose a significant challenge to both carriers and shippers. Traditional policies typically impose separate cost structures for container pickup (demurrage) and container return (detention), yet such separate impositions often fail to capture the interconnected nature of operational delays and the pervasive uncertainty present in hinterland container flows. This study addresses the problem of D&D decision-making under uncertainty by proposing a merged free time policy that integrates both D&D charges into a unified framework. By merging the free time allocated for both pickup and return processes, the proposed policy aims to enhance operational flexibility, reduce overall logistics costs, and provide a more predictable cost structure for carriers while improving service quality for shippers. To achieve these objectives, we develop a mathematical optimization model that incorporates stochastic pickup and return scenarios, thereby reflecting the uncertainties in container availability and transportation delays. The model embeds a strategic decision-making process between carriers and shippers through a hierarchical framework to jointly optimize free time allocations and penalty structures. Numerical experiments based on simulated data demonstrate that the merged free time policy outperforms traditional separate policies by improving container turnover efficiency and mitigating the negative impact of uncertainty on operational performance. Our findings offer valuable insights into cost management and risk reduction in maritime logistics and contribute to the literature by providing a comprehensive strategy for D&D management that supports more collaborative hinterland container operations and enhances overall supply chain resilience.