Recently, considerable attention has been given to nickel-based superalloys used in additive manufacturing. However, additive manufacturing is limited by a slow build rate in obtaining optimal densities. In this study, optimal volumetric energy density (VED) was calculated using optimal process parameters of IN718 provided by additive manufacturing of laser powder-bed fusion. The laser power and scan speed were controlled using the same ratio to maintain the optimal VED and achieve a fast build rate. Cube samples were manufactured using seven process parameters, including an optimal process parameter. Analysis was conducted based on changes in density and melt-pool morphology. At a low laser power and scan speed, the energy applied to the powder bed was proportional to and not . At a high laser power and scan speed, a curved track was formed due to Plateau-Rayleigh instability. However, a wide melt-pool shape and continuous track were formed, which did not significantly affect the density. We were able to verify the validity of the VED formula and succeeded in achieving a 75% higher build rate than that of the optimal parameter, with a slight decrease in density and hardness.

본 연구는 고속스핀에코 기법을 이용한 MRI 검사에서 영상 변수가 전자파 흡수율(SAR)과 온도 증가에 미치는 영향을 평가해 보고자 하였다. 이를 위해 인체 등가 조직 팬텀을 제작하였고 같은 조건에서 재위상화 RF의 FA와 ETL을 증가시키 며 MRI 검사를 시행하였다. SAR는 장비에서 계산된 두부 SAR값을 사용하였고 팬텀의 온도 변화는 양성자 공명 주파수를 이용해 계산하였다. 실험 결과, FA를 60°에서 180°까지 증가시켰을 때 SAR는 약 8배까지 상승하였고 팬텀의 온도 상승의 폭은 약 2.8배(0.21°-0.599°) 증가하였다. 그리고 다중 회귀 분석 결과, FA는 SAR와 온도 상승의 관계에서 표준화 계수 가 각각 0.935, 0.741로 나타나 높은 상관관계를 보였다. ETL은 15에서 30까지 증가시켰을 때 SAR는 약 2배 증가하였 다. 하지만 팬텀의 온도 상승 폭은 오히려 39.2%(0.53°-0.303°) 감소하였다. 다중 회귀 분석 결과에서도 ETL은 SAR의 관계에서 표준화 계수가 0.741로 양의 상관관계를 보였지만 온도 상승의 경우 표준화 계수가 –0.482로 나타나 음의 상관 관계가 있었다. 이는 ETL이 15에서 30까지 증가함에 따라 검사 시간이 약 43% 짧아져 전자파의 노출 시간이 감소한 것이 원인이 될 수 있다. 결국, FSE 기법을 이용한 MRI 검사에서는 재위상화 FA는 최소화하고 기준 SAR 범위 내에서 ETL을 최대로 적용하면 전자파로 인한 온도 상승을 최소화할 수 있어 환자 안전을 증진 시킬 수 있을 것으로 기대한다.

Seismic demand on nonstructural components (NSCs) is highly dependent on the coupled behavior of a combined supporting structure- NSC system. Because of the inherent complexities of the problem, many of the affecting factors are inevitably neglected or simplified based on engineering judgments in current seismic design codes. However, a systematic analysis of the key affecting factors should establish reasonable seismic design provisions for NSCs. In this study, an idealized 2-DOF model simulating the coupled structure-NSC system was constructed to analyze the parameters that affect the response of NSCs comprehensively. The analyses were conducted to evaluate the effects of structure-NSC mass ratio, structure, and NSC nonlinearities on the peak component acceleration. Also, the appropriateness of component ductility factor (R p) given by current codes was discussed based on the required ductility capacity of NSCs. It was observed that the responses of NSCs on the coupled system were significantly affected by the mass ratio, resulting in lower accelerations than the floor spectrum-based response, which neglected the interaction effects. Also, the component amplification factor (a p) in current provisions tended to underestimate the dynamic amplification of NSCs with a mass ratio of less than 15%. The nonlinearity of NSCs decreased the component responses. In some cases, the code-specified R p caused nonlinear deformation far beyond the ductility capacity of NSCs, and a practically unacceptable level of ductility was required for short-period NSCs to achieve the assigned amount of response reduction.

In this study, stiffness evaluation was conducted on the main member, front cross member, and rear cross member, which are three components of sub-frame for SUVs (sports utility vehicles), through mode analysis. As for the design variables used in the analysis, the maximum frequency was examined by varying the width and height of each of the three parts into four types. Of course, the weight at this time is minimized, and the mode is set as a constraint that only bending occurs and no distortion occurs. As a result of the analysis, the member affecting the 1st mode was the rear cross member, and the member having the greatest influence on the 2nd mode was the front cross member. In addition, the member with the greatest influence on the 3rd mode appeared as the rear cross member, indicating that this part had the greatest effect on the bending stiffness.

CALMET (California Meteorological Model), which is a meteorological subroutine for an air quality dispersion mode (CALPUFF; California Puff Model), closely related with the land surface structure. In this study, the sensitivity of micro-meteorological parameters including wind speed and roughness height, Albedo, Bowen ratio, soil heat flux, and leaf area index were closely evaluated with change of land-use in relation to urban development. As a result, although no consistent dependence of roughness height on surface wind was found, it showed that high value of surface roughness could lead to the increase of friction velocity, influence the Monin-Obukhov length and the mixing height. At the same time, the increasing Albedo reduced friction velocity and mixing height. Thus, it was concluded for the CALMET modelling that it is necessary to first define the roughness height, Albedo, and Bowen ratio according to land-use.

월파된 파도를 이용한 파력발전시스템을 월파수류형 파력발전기 OWEC(Overtopping Wave Energy Converter)라고 한다. OWEC의 성능은 발전 시스템은 특성상 파도의 파고와 주기의 영향을 받는다. 파도는 해양에 따라 파고, 주기, 파도 방향 등의 특성이 다르고 이러 한 파도의 다양한 특성 때문에 OWEC는 안정적인 전력을 생산하기 어렵다. 따라서 각 바다의 특성에 따른 OWEC의 적절한 형상에 관한 연구가 필요하다. 본 연구에서는 SPH(Smoothed Particle Hydrodynamics) 입자법을 사용하여 OWEC의 램프 설계가 hydraulic efficiency에 미치 는 영향을 확인했다. 총 10개의 모델을 설계하였으며, 선택된 매개변수에 따라 램프의 설계 파라미터를 선택하고 사면의 형상을 변경하여 시뮬레이션을 수행하였다. 해석 결과로부터 구한 유량을 기초로 hydraulic efficiency를 산출하였다. 계산된 hydraulic efficiency를 바탕으로 각 변수가 사면의 형상에 따른 월파 성능에 미치는 영향을 확인하였다. 본 연구에서는 특정 해역에 따른 OWEC 램프의 적절한 형상에 대한 방향을 제시하였다.

In this study, the frequency response analysis of a bistable electromagnetic vibration energy harvester is performed, based on an electromagnetic oscillator model, to investigate its nonlinear dynamic behaviors. The displacement and current responses are obtained, by the direct integration of the model, with the variations of mechanical and electromagnetic parameters. It is shown that the operating frequency band of the system can be broadened by the increase in mechanical parameters(inertial mass and Q-factor), but it does not depend significantly on any electromagnetic parameters(an external load resistance and the internal resistance of a coil). On the other hand, the output current of the energy harvester is affected only by the electromagnetic parameters (specifically, the sum of two resistances). Thus, the mechanical and electromagnetic parameters of the electromagnetic energy harvester must be designed properly, respectively, for broader and more efficient performance.

해수에 유출된 유류는 대기와 해수와의 접촉을 통해 다양한 풍화 과정(증발, 확산, 분산, 유화, 용해, 산화, 침전 및 생물분 해 등)을 통해 물리·화학적 변화와 함께 생물학적 분해과정을 겪는다. 본 연구에서는 여러 가지 풍화작용 중 가장 즉각적이고 빠르며 오염물질의 질량에 가장 큰 영향을 미치는 인자라고 알려진 증발(evaporation)에 대한 영향을 확인해보고자 하였다. 휘발유, 등유, 경유 를 대상으로 25 ℃(해수 연평균 온도)와 35℃(적도 부근 온도) 조건에서 유류의 휘발특성을 비교하였다. 이를 위해, 일정 기간마다 채취 한 유류를 전처리하여 GC/MS 분석을 수행하고, 탄화수소의 변화량을 계산하여 비교하였다.

본 연구는 이러한 단점을 보완하기 위해 철근을 대체하여 내산화성과 전기저항이 높은 GFRP 보강근을 적용한 도상슬래브의 최적 변수해석을 수행하였다. 철도 궤도슬래브에 적용되는 철근은 열차 운행 중 신호전류의 손실을 일으켜 열차의 안정성을 저해하며, 철 근의 부식으로 내구성이 저하될 수 있다. GFRP 보강근의 직경 및 배근 개수 변화가 전체 콘크리트 도상슬래브의 휨강도 및 균열제어 에 미치는 영향을 유한요소 변수해석을 통하여 상세분석하였다. 해석 결과, GFRP 보강근의 직경 및 배근을 합리화하여 제안하였으며 이러한 경우 기존 배근보다 더욱 경제적인 단면을 도출할 수 있음을 알 수 있었다. 본 연구로부터 도출된 결과는 향후 GFRP 보강근을 적용하여 도상슬래브를 설계하는 경우 보다 합리적이고 경제적인 단면을 산정할 수 있는 가이드라인이 될 수 있을 것으로 기대된다.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

It is getting more intensified with the competition among participating companies for global market share in major industrial fields. The situation is accelerating especially within the top 5 market share, and these include electric vehicles, semiconductors, chemicals, and shipbuilding industries. The key to the advantage over the competition within a strategic group is which company leads the innovation in the field. On-the-ground innovation refers to job-based innovation. This paper aims to analyze job unit innovation in the structure of empowerment, LMX, and job crafting. Existing studies on job crafting have suggested a causal structure based on job design in the traditional sense, and there are not many scholars who study the causal structure using a job situational model. Therefore, this paper takes an approach from the perspective of the job situation. As a result of the study, LMX showed a moderating effect on the relationship between autonomy provision and job crafting. While, in the relationship between meaing-giving and cognitive crafting, there is no significant moderating effect shown on the relationship between autonomy provision and cognitive crafting. Therefore, the results of the analysis in this study suggest that the meaning of jobs and participation in decision-making should be managed in an integrated way in structural and design areas, not just qualitative factors such as empowerment and leadership.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

Most sensors are affected by temperature, so they are tested in advance and used for temperature compensation. However, sensor affected by the temperature hysteresis is not compensated. This is because even if compensation is made in the form of a general n-th polynomial, the effect of hysteresis remains the same. In this paper, a method of compensating accelerometer biases with hysteresis using a new parameter C was studied. This technique goes beyond finding the appropriate variable for compensation and is a method of creating the parameter itself with a combination of new variables. As a result, most errors could be eliminated.