This study performed the seismic response analysis of an LNG storage tank supported by a disconnected piled raft foundation (DPRF) with a load transfer platform (LTP). For this purpose, a precise analytical model with simultaneous consideration of Fluid-Structure Interaction (FSI) and Soil-Structure Interaction (SSI) was used. The effect of the LTP characteristics (thickness, stiffness) of the DPRF system on the seismic response of the superstructure (inner and outer tanks) and piles was analyzed. The analytical results were compared with the response of the piled raft foundation (PRF) system. The following conclusions can be drawn from the numerical results: (1) The DPRF system has a smaller bending moment and axial force at the head of the pile than the PRF system, even if the thickness and stiffness of the LTP change; (2) The DPRF system has a slight stiffness of the LTP and the superstructure member force can increase with increasing thickness. This is because as the stiffness of the LTP decreases and the thickness increases, the natural frequency of the LTP becomes closer to the natural frequency of the superstructure, which may affect the response of the superstructure. Therefore, when applying the DPRF system, it is recommended that the sensitivity analysis of the seismic response to the thickness and stiffness of the LTP must be performed.

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

We have observed a phenomenon where the internal X capacitors of the input EMI filter experienced damage during operation. To solve the problem, we have analyzed the malfunction by identifying the characteristics and operating principles of EMI filter. Based on this analysis, we have derived improvement strategies and validated them through experiments. This paper help some people prevent the similar problem when developing the similar equipment and solve the similar problem of the similar equipment.

In order to investigate the optimum condition of the autofrettage process for the diesel engine fuel injection pipe, different values of autofrettage pressure, pressure rising time, pressure holding time, and repetition of autofrettage process were applied. Autofrettage was performed by applying the hydrostatic internal pressures of 604MPa, 535MPa, 500MPa on the fuel injection pipe, corresponding to theoretical 50%, 30%, and 20% overstrain levels, respectively. The autofrettage residual stresses in the injection pipe were experimentally determined by using X-ray diffractometer. As the overstrain level increased, the magnitude of compressive residual stress at the bore increased. It was found that the rising time to reach the autofrettage pressure, holding time at the autofrettage pressure, and repeated application of the autofrettage pressure on the pipe had no significant influence on the residual stress distributions.

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.

The purpose of this study is to compare the effects of input- and output-based planning (reading a sample passage vs. writing a draft) on the oral performance of L2 learners with low-proficiency. In this study, 16 Korean female junior college students of low English proficiency were divided into two different planning groups. The reading group was required to read a sample passage of the given topic, designed to encourage “noticing” and “focus on form” using input enhancement, while the writing group was asked to write a draft of their speech, using only their own L2 knowledge. After such planning activities, both groups recorded their assigned speaking tasks using Kakao Talk. Eight planning activities and oral performances were completed over the period of the semester. In order to compare the effects of input- and output-based planning on the improvement of overall proficiency, pre- and post-tests, in which the students produced the same narratives, were analyzed using Mann-Whitney U and Wilcoxon signed-rank tests. Furthermore, this study explored any difference in speaking performance after each type of planning and what the learners were actually doing during planning time. The results showed that output-based planning had positive effects on speaking performance and its repeated practice led to the improvement of overall proficiency.

선박의 주묘 위험성을 평가할 수 있는 프로그램이 개발되어 있지만 선박의 제원에 해당되는 다양한 입력요소들을 직접 찾아서 입력해야 하므로 VTS 관제사가 정박지에 정박 중인 선박들로부터 이러한 입력요소들을 모두 확인하여 프로그램을 활용하는 것은 현실적으로 어려운 상황이다. 이에 본 연구에서는 VTS 관제사 입장에서 선박으로부터 쉽게 획득할 수 있는 총톤수(GT)를 독립변수로 설정하고 프로그램 입력요소들을 종속변수로 하여 선형 및 비선형 회귀분석을 실시하였다. 다항식 모델(선형)과 멱급수 모델(비선형)의 적합도를 비교한 결과, 컨테이너선과 벌크선의 경우에는 모든 입력요소에서 멱수급 모델이 적합한 것으로 평가되었다. 하지만 탱커선의 경우에는 수선간장, 선폭, 흘수는 멱수급 모델이 적합하고, 정면풍압면적, 앵커의 무게, 의장수, 묘쇄공으로부터 선저까지의 높이는 다항식 모델이 더 적합한 것으로 평가되었다. 또한 탱커선의 정면풍압면적 요소를 제외한 다른 나머지 종속변수들은 모두 결정계수가 0.7 이상으로 높은 적합도를 보였다. 따라서 주묘 위험성 평가 프로그램의 입력요소 중 외력 요소, 해저 저질, 수심 및 앵커 체인의 신출량을 제외한 나머지 입력요소들은 선박의 총톤수만 입력하면 회귀분석 모델식에 의해 자동으로 입력됨으로써 주묘 위험성 평가가 가능할 것으로 판단된다.

In this analysis, the analytical model was verified through the normal mode analysis of the piston for the 2.9 liter IDI (indirect injection) engine. Heat transfer analysis was carried out by selecting two cases of applied temperature using the validated model. The first case was a condition of 350℃ on the piston upper surface and 100℃ on the piston body and inner wall. In the second case, the conditions were set to give a temperature of 400℃ on the upper surface of the piston and 100℃ on the piston body and the inner wall. In addition, the temperature distribution due to heat transfer was obtained for the pistons with boundary conditions of two cases, and then the thermal stress distribution due to thermal expansion was obtained using the input. Using this analysis result, the thermal stress caused by thermal expansion due to the thermal conduction of the piston is examined and used as the basic data for design.

In an effort to find a way to optimize the learning condition, this study examined the effects of two types of input frequency distribution, skewed-first distribution (SFD) versus balanced distribution (BD), and two types of focus-on-form techniques, input processing (IP) versus input enhancement (IE), on the learning of English participles by Korean middle school students. A total of 91 students participated in this study, divided into five subgroups: SFD + IP, SFD + IE, BD + IP, BD + IE, and Control. The learning outcomes and the generalizability of the learned knowledge were measured through scaled judgment tasks and picture description tasks, immediately after treatment as well as one week later. The results showed that the input frequency distribution factor did not have statistically significant effects on learning and generalizability, while the degrees of explicitness of focus-on-form techniques had statistically significant effects on both. The interaction effects of the two main factors were not statistically significant. More detailed findings are presented with some pedagogical implications for Korean EFL classrooms.

기존의 확률론적 안전성 평가의 신뢰도 제고를 위하여 잘 알려진 입력 파라미터의 일반적인 분포에 새롭게 측정된 신뢰도 있는 데이터를 결합하여 사후분포를 구할 수 있는 베이지안 업데이팅 방법론을 제안하였다. 마코프체인 몬테 칼로 샘플링 기법의 알고리듬을 통한 GoldSim 모듈도 개발하였다. 복수의 입력 파라미터의 사전분포에 대해 연속적으로 사후분포를 구 해낼 수 있는 베이지안 업데이팅이 가능하도록 개발된 이 모듈을 GoldSim 템플릿 형태의 기존의 GSTSPA 프로그램으로 이행하여 보다 신뢰도 있는 확률론적 방사성폐기물 처분 시스템 안전성 평가가 가능하도록 하였다. 이는 기존에 존재하는 사 전분포의 일반적인 형태는 취하되 새롭게 얻어지는 실제 측정치나 전문가들의 의견을 기존의 분포에 적용하여 보다 더 높은 믿음을 갖는 입력 파라미터의 사후분포를 얻을 수 있게 한다. 균열암반 내 핵종 이동에 관련된 몇 개의 입력 파라미터의 사전분포의 세차례의 연속적 업데이팅을 통해 프로그램의 유용성도 예시하였다. 이 연구를 통하여 처분시스템과 같이 장기적 평가가 필요하고 넓은 모델링 지역을 가지며 측정된 입력자료가 부족한 경우 보다 더 믿음직한 방법으로 안전성 평가를 수행할 수 있는 것을 보였다.

Building Information Modeling (BIM) tools have not only increased the use of technology in the design process, but also increased the need for more information standard systems. The object classification system consists of 327 types of construction results obtained from 25 kinds of facilities, 174 types of parts, and 207 types of construction parts. In the previous study, the property classification system was developed into 4 major classifications, 13 middle classifications, 58 small classifications (category), and 333 attribution information of roads and rivers. It is extremely difficult to input the property information according to such extensive object classification. In addition, the development of external applications such as Revit plug-ins has created a need to automate specific and repetitive tasks. Therefore, following the BIM property classification system, an attribute input program was implemented for the system to enhance the productivity and convenience of the BIM users.

This study investigated the relative effects of input-based versus output-based activities on the learning of English unaccusative constructions. A total of 73 high school students were randomly assigned into two experimental groups and one control group. Of the two experimental groups, the input-based input enhancement group (IE group) experienced a reading passage with the learning targets visually enhanced. The output-based dictogloss group (DG group) performed the dictogloss task with the identical passage given to IE group. The control group (CG group) did not experience the learning targets at all. Results showed that IE group statistically significantly outperformed CG group both at the immediate and the delayed posttests, while there was no significant difference between IE group and DG group. In terms of the generalizability of the learned knowledge, however, it was DG group that outperformed CG group at the immediate posttest. Again, there was no difference between IE group and DG group. More detailed findings are provided, along with some implications for English classrooms in Korea.

Wireless sensors are more favorable in measuring structural response compared to conventional sensors in terms of them being easier to use with no issues with cables and them being considerably cheaper. Previous tests have been conducted to analyze the performance of MEMS (Micro Electro Mechanical Systems) sensor in sinusoidal excitation tests. This paper analyzes the performance of in-built MEMS sensors in devices by comparing with an ICP sensor as the reference. Earthquake input amplitude excitation in shaking table tests was done. Results show that MEMS sensors are more accurate in measuring higher input amplitude measurements which range from 100gal to 250gal than at lower input amplitudes which range from 10gal to 50gal. This confirms the results obtained in previous sinusoidal tests. It was also seen that natural frequency results have lower error values which range from 0% to 3.92% in comparison to the response spectra results. This also confirms that in-built MEMS sensors in mobile devices are good at estimating natural frequency of structures. In addition, it was also seen that earthquake input amplitudes with more frequency contents (Gyeongju) had considerably higher error values than Pohang excitation tests which has less frequency contents.

Pseudo-static approach has been conventionally applied for the design of gravity type quay walls. In this method, seismic coefficient (kh), expressed in terms of acceleration due to gravity, is used to convert the real dynamic behavior to an equivalent pseudo-static inertial force for seismic analysis and design. Therefore, the calculation of an appropriate kh considering frequency characteristics of input earthquake is critical for representing the real dynamic behavior. However, the definitions of kh, which is used for simplified analysis in Korea, focuses only on convenience that is easy to use, and the frequency characteristics of input earthquake are not reflected in the kh definitions. This paper evaluates the influences of the frequency characteristics of input earthquake on kh by initially reviewing the kh definitions in the existing codes of Japan for port structures and then by performing a series of dynamic centrifuge tests on caisson gravity quay walls of different earthquake input motions (Ofunato, Hachinohe). A review of the existing codes and guidelines has shown that the kh values are differently estimated according to the frequency characteristics of input earthquake. On the other hand, based on the centrifuge tests, it was found that the permanent displacements of wall are more induced when long-period-dominant earthquake is applied.

Current seismic design provisions such as ASCE 7-10 provide criteria for selecting ground motions for conducting response history analysis. This study is the sequel of a companion paper (I – Ground Motion Selection) for assessment of the ASCE 7-10 criteria. To assess of the ASCE 7-10 criteria, nonlinear response history analyses of twelve single degree of freedom (SDF) systems and one multi-degree of freedom (MDF) system are conducted in this study. The results show that the target seismic demands for SDF can be predicted using the mean seismic demands over seven and ten ground motions selected according to the proposed method within an error of 30% and 20%, respectively

For estimating the seismic demand of buildings, most seismic design provisions permit conducting linear and nonlinear response history analysis. In order to obtain reliable results from response history analyses, a proper selection of input ground motions is required. In this study, an accurate algorithm for selecting and scaling ground motions is proposed, which satisfies the ASCE 7-10 criteria. In the proposed algorithm, a desired number of ground motions are sequentially scaled and selected from a ground motion library without iterations.

신뢰성 해석 및 신뢰성기반 최적설계는 불확실성을 고려한 확률변수를 입력 값으로 요구하며, 확률변수는 모수적․비모수 적 통계모델링 방법을 사용하여 확률분포함수의 형태로 정량화 된다. 신뢰성 해석과 같은 통계적 해석은 입력되는 확률분포 함수의 특성이 결과값에 영향을 미치게 되며, 확률분포함수는 통계모델링 방법에 따라 다른 형태를 가지게 된다. 본 연구에 서는 모수적 통계모델링 방법인 순차적 통계모델링 방법과 비모수적 방법인 커널밀도추정을 사용하여 데이터의 개수에 따 른 통계모델링의 결과를 분석하였다. 또한 수치예제를 통해 두 가지 기법에 따른 신뢰성 해석의 결과를 분석하였고, 데이터 의 개수에 따른 적절한 기법을 제안하였다.

In this paper, we propose a method for replaying game streaming video using informations of input-key and object states. Up to the present, the played game contents have been just recorded and replayed for game streaming video recording function provided by the game company, this required a large volume of storage device. By the proposed method, it is more efficient for sharing the game informations between the users and game companies, and between the users and other users because it need much less storage device than the present method. And also the game user can handle the additional game informations.

In this study, we analysed the influence of the flow and performance of the generated micro gas turbine with different rotational speeds. CFD analysis was also performed using commercial code called ANSYS CFX 14.5. It adopted the turbine of radial flow type suitable for power generation for small rotating equipment. The number of blades was designed with each radial turbine 8, vane nozzle 14. The boundary layer flow analysis was widely used to the SST model. Mass flow rate of the turbine was 1.45 kg/s. While the rpm varies from 38,000rpm to 48,000rpm under various boundary conditions, the distributions of Mach number and pressure were numerically analyzed for electric power output, turbine isentropic efficiency. We analyze the inlet pressure, outlet temperature, electric power output, turbine isentropic efficiency. It compared maximum and minimum revolution through CFD analysis. Power output of maximum revolution has been increased 18% more than minimum revolution. Isentropic efficiency of maximum revolution has been increased 16.5% more than minimum revolution. Therefore, maximum power output was 332kW, amaximum isentropic efficiency was 33.2%.