In this study, contribution evaluation method applying Independent Component Analysis (ICA) was proposed. The necessity of applying ICA to the contribution evaluation was investigated through numerical simulation. The simulation modeled a scenario where the vibration/noise sources were physically overlapped in a small space, and their frequency characteristics were similar. For comparison between the conventional contribution evaluation method and the proposed method, the contribution evaluation was performed using the ordinary and partial contribution evaluation methods. Through this analysis, it was confirmed that the proposed method can identify contributions by restoring the signal when the frequency characteristics of the vibration/noise sources were similar, and their positions overlapped. These results confirm that the contribution evaluation method based on independent component analysis is effective in appropriately analyzing vibration/noise sources when their frequency characteristics are similar, and their positions overlap.

콩은 높은 단백질 함량과 다양한 기능적 특성으로 인해 식품 및 사료 산업에 필수적인 작물이다. 그러나 농촌 인구의 고령화와 저렴한 수입 콩으로 인해 국내 콩 생산량은 꾸준히 감소하고 있다. 이러한 문제를 해결하기 위해서는 농업 기계 기술의 발전이 필수적이며, 특히 콩수확기의 선별 메커니즘을 개선하는 것이 중요하다. 따라서 본 연구는 CFD-DEM 결합 시뮬레이션을 사용하여 콩수확기의 선별장치 내의 유동 역학과 입자 움직임을 분석하여 선별 효율성을 향상시키는 것을 목표로 했다. 경남농업기술원에서 재배한 진풍 콩(Glycine max (L.) Merrill) 품종을 실험에 사용하였다. 선별장치는 콩, 콩대, 줄기를 분리하여 콩알만 수집하도록 설계되었다. 실험 중에는 콩 줄기를 균일하게 투입하여 분리율과 수집률을 측정하였다. 또한 유동 분석을 위해 표준 k-ε 난류 모델을 사용하였으며, CFD-DEM 결합 방법을 사용하여 선별 장치 내의 내부 유동과 입자 움직임을 시뮬레이션하였다. 추가로 CFD 분석 결과를 DEM 시뮬레이션에 활용하여 Ganser 항력 모델을 적용하여 콩과 콩대의 분리 특성을 분석하였다. 마지막으로 CFD-DEM 결합 시뮬레이션을 통해 콩수확기의 선별 장치 성능을 평가하고 최적의 팬 회전속도를 결정하였다. 실험에서 팬 회전속도는 각각 900 rpm, 1,000 rpm, 1,100 rpm, 1,200 rpm으로 설정하였다. 실제 선별장치에서 측정한 풍구 회전 시의 공기 유속과 시뮬레이션 에서 팬 회전으로 발생한 공기 유속 간의 RMSE 값은 0.64 m/s에서 1.12 m/s로 나타났다. 풍구 회전수에 대한 콩의 수집률을 시뮬레이션 결과 풍구 회전수가 증가할수록 수집률이 감소했으며, 900 rpm일 때 최대 94.08%의 수집률을 보였다. 콩대와 콩줄기 분리율의 경우 900 rpm과 1,000 rpm에서 55%~60%로 낮은 효율을 보였다. 1,100 rpm에서 86.38%, 1,200 rpm일 때 86.14%의 분리율이 측정되었다. 콩 수집률과 콩대 분리율 모두에서 최적의 성능을 발휘하려면, 풍구 회전수는 1,100 rpm이 적절한 것으로 보인다.

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.

지진취약도를 산정하기 위해서는 목표 부지의 특성을 제대로 표현할 수 있는 입력 지진파의 산정이 중요하다. 본 논문에서는 국내 외 강진 및 중‧약진 지역에서의 입력 지진파에 대한 단자유도 모델의 지진취약도를 분석하였다. 분석을 위한 첫 번째 단계로, 국외 강 진 기록 중 근/원거리에서 측정한 2개의 입력 지진파 세트와 국내 중·약진 지역 특성에 적합한 입력 지진파 2개의 세트, 총 4개의 입력 지진파 세트를 선정하였다. 대상 구조물로는 3가지 고유주기에 대한 비선형 단자유도 모델을 적용하였고, 취약도 분석을 위해 증분동 적해석을 이용하였다. 또한, 4가지 손상 상태를 정의하고, 손상 상태 각각에 대해 4가지 입력 지진파 세트의 고유주기별 지진취약도 결과를 제시하였다.

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.

This study aimed to investigate the effect of visual input enhancement (VIE) on the comprehension of reading texts and the learning of two grammatical forms: English relative clauses and articles. Individual learners’ working memory (WM) capacity was also tested to explore its impact on the effectiveness of VIE. A total of 48 Korean college learners of English were assigned into three groups: (a) relative group receiving VIE on relative clauses (b) article group receiving VIE on articles, and (c) a control group receiving no VIE. Results showed that VIE did not have any negative effect on the learners’ reading comprehension. Rather, it had positive effects on the learning of the two grammatical forms. According to the findings, VIE on relative clauses enhanced the learners’ receptive knowledge of the grammatical form, whereas VIE on articles enhanced the learners’ productive knowledge of the form. There was a potential link between the effectiveness of VIE and the learners’ working memory processing ability. Pedagogical implications are also discussed based on these findings.

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.

This study attempts to analyze the economic impact of the service robot industry using Input-Output analysis, which is conducted based on Demand-driven model, the Leontief price model, the Backward and Forward Linkage Effects, and the Exogenous Methods. In a Demand-driven model analysis, we can conclude that the service robot industry contains characteristics of both the manufacturing industry and the service industry, which causes a positive impact on the overall industry by compensating for the weaknesses of the two industries. The Leontief price analysis indicates when wages in the service robot industry increase, prices related to robot manufacturing also increase. Also, when profits in the service robot industry increase, prices related to service provision increase, too. The Backward and Forward Linkage Effects analysis shows that the service robot industry is highly sensitive to the current economic condition and has a great influence on the service industry. The service robot industry can highlight the aspect of service characteristics when the manufacturing industry is in recession and vice versa. In addition, the service robot industry can be regarded as a value-adding and domestic economy promoting industry which utilizes knowledge of information and communication technologies. It is important to foster the service robot industry in South Korea, which is in economic recession to provide an opportunity to stimulate the growth of both service and robot industries.

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

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 analyze the structure, status and economic ripple effects of the fisheries processing industry in Korea using interindustry analysis. Five input-output tables published over the past twenty years have been reclassified with a focus on the fisheries processing sector. Through these multi-period tables, we analyzed changes in the inducing effects in production, value added and employment as well as the backward-forward linkage effects. As a result of the analysis, it was found that the industrial scale of the fisheries processing industry is very small compared to other food manufacturing industries. The backward linkage effect of the fisheries processing industry was greater than that of other industries, but the forward linkage effect was rather low. This means that the fisheries processing industry can be greatly affected by industrial depression of the downstream industries such as fishery and aquaculture. Production and employment-inducing effects of the fisheries processing industry have shown a decreasing trend in recent years. This reflects the reality that intermediate inputs are gradually being replaced by imports from domestic production due to the expansion of market opening and the depletion of fishery resource. In the future, it is necessary to prepare a strategy to increase the value-added productivity of the fisheries processing sector and foster it as an export industry.

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.

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.

The study investigated how task types such as input or output could affect Korean EFL learners’ vocabulary learning at both short- and long-term periods after treatment. Forty two college students in Korea were randomly assigned to one of the four tasks. Based on Involvement Load Hypothesis, each task induced the same or different involvement loads: Read without glossary (Input), Gap with glossary (Input & Output), Gap without glossary (Input & Output), and Sentence and write (Output). Receptive and productive vocabulary knowledge was measured right after the treatment and a month later. The output-oriented tasks were found to be more effective than the input-oriented tasks regardless of type of vocabulary knowledge. The current study concluded that modalities such as input or output other than task-induced involvement load can contribute to Korean EFL learners’ vocabulary learning. The pedagogical discussion will be made at the conclusion.

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

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.