Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering “demand spike” during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

The aim of this study was to investigate the physicochemical characteristics of hand drip, Aeropress, espresso, Moka, Mukka, and Brikka coffee brews. Espresso showed higher antioxidant activity, total solids, caffeine, chlorogenic acid, total phenolic compounds, and trigonelline concentrations compared to the other coffee brews studied. In terms of extraction efficiency, Brikka and Mukka showed higher total solids, as well as caffeine and chlorogenic acid contents, whereas drip coffee brew showed higher trigonelline content than other coffee brews. Meanwhile, principle component analysis (PCA) was conducted based on the aroma profile by e-nose. Samples were gathered into distinct groups that represented their brewing methods. Despite the similarity in brewing principle between Moka and other high-temperature brewing methods (i.e., Mukka and Brikka), the location of Moka coffee brew was closer to that of espresso in PCA, which was consistent with the PCA result conducted by e-tongue.

The purpose of this study was to compare differences in the main food components of quinoa (Chenopodium quinoa Willd.) cultivated in Hongcheon after steaming, boiling, and roasting. Among the general components, crude protein, fat, and ash content were the highest in raw quinoa. Dry matter and carbohydrate content was the highest in steamed quinoa, while total dietary fiber content was highest in roasted quinoa. Total amino acid contents were the highest in boiled quinoa and lowest in steamed quinoa. Fatty acid content was highest in raw quinoa and lowest in boiled quinoa. The mineral (calcium, potassium, and phosphorus) and vitamin content was most enriched in raw quinoa, while iron, magnesium, zinc, and manganese were highest in boiled quinoa. For free sugars, the fructose and sucrose levels were highest in raw quinoa, while glucose level was highest in roasted quinoa. The water-soluble vitamin and free sugar contents were lowest in boiled quinoa. In summary, nutritional levels of vitamins vulnerable to heat and unsaturated fatty acids decreased after cooking with heat, while those of amino acids and saturated fatty acids increased after cooking with heat, although there were variables based on different cooking methods.

This study was aimed to examine inorganic fouling and fouling reduction method in direct contact membrane distillation(DCMD) process. Synthetic seawater of NaCl solution with CaCO3 and CaSO4 was used for this purpose. It was found in this study that both CaCO3 and CaSO4 precipitates formed at the membrane surface. More fouling was observed with CaSO4(anhydrite) and CaSO4･0.5H2O(bassanite) than CaSO4･2H2O(gypsum). CaCO3 and gypsum were detected at the membrane surface when concentrates of SWRO(seawater reverse osmosis) were treated by the DCMD process, while gypsum was found with MED(multi effect distillation) concentrates. Air backwash(inside to out) was found more effective in fouling reduction than air scouring.

Seismic responses due to the dynamic coupling between a primary structure and secondary system connected to a structure are analyzed in this study. The seismic responses are compared based on dynamic coupling criteria and according to the error level in the natural frequency, with the recent criteria being reliant on the error level in the spectral displacement response. The acceleration responses and relative displacement responses of a primary structure and a secondary system for a coupled model and two different decoupled models of two degrees-of-freedom system are calculated by means of the time integration method. Errors in seismic responses of the uncoupled models are reduced with the recent criteria. As the natural frequency of the secondary system increases, error in the natural frequency decreases, but seismic responses of uncoupled models can be underestimated compared to that of coupled model. Results in this paper can help determine dynamic coupling and predict uncoupled models’ response conservatism.

목적 : 본 연구에서는 체계적 고찰을 통해 조현병 환자의 실행기능 향상을 위한 작업치료 중재 방법 및 효과를 알아보고자 하였다. 연구방법 : 2007년 1월부터 2018년 12월까지 ScienceDirect, Pubmed와 CINAHL에 등재된 논문 중 ‘schizophrenia’, ‘occupational therapy’, ‘executive function’으로 자료를 검색하였다. 그 중 조현병 환자를 대상으로 작업치료 중재를 적용한 10편의 연구를 선정하여 분석하였다. 결과 : 선정된 10편의 연구에서 작업치료 중재 종류는 작업치료 훈련중재, 물건 구매하기 훈련중재, 인지 훈련중재, 직업훈련 프로그램 중재의 네 가지로 분류하였다. 이러한 중재들은 방법과 목적에 따라 실제 일상생활의 수행도, 실행기능, 전반적인 인지 그리고 심리사회적 기능에 대한 향상에 효과가 있는 것으로 나타났다. 종속변인은 인지기능, 실행기능, 수단적 일상생활활동, 쇼핑 등의 순서로 높은 빈도를 나타내었고, 10편의 연구에서 총 38개의 평가도구가 사용되었으며, 대부분 인지기능을 확인하기 위한 도구들이었다. 결론 : 본 연구를 통하여 지역사회 내 조현병 환자의 실행기능 향상을 위한 작업치료 중재의 종류와 효과, 종속변인 및 평가 도구에 대해 확인할 수 있었다. 이러한 결과는 작업치료사가 조현병 환자에게 작업치료 중재를 제공할 때 목적에 따라 중재 방법을 선택하는 근거로 활용할 수 있을 것이다.

Recently, the importance of air filters used in air purifiers and ventilation systems is emphasized in Korea. As a result, air filter test reports are required by users to ensure the removal efficiency of particulate matter. However, the tests are conducted for the filter material alone, which lead to a possible discrepancy between the test report and actual efficiency when applied to actual devices. Therefore, in this study, the removal efficiency data of the filter test reports were compared with actual filter efficiency data after application to the ventilation systems for some ventilation systems in the market. For ventilation system A, the field test results using filter leakage test method were slightly lower than those in the test report but nearly the same. For ventilation system B, the field test result was much higher than reported in the test report. This was due to the broad range of particle sizes measured using the filter leakage test method. The field tests using the particle counter method showed that the removal efficiency of ventilation system A for 0.3 μm was under 50% which translates to less than half of those of the filter test reports. For ventilation system B, the removal efficiency was 15%~21%. much lower than reported in the filter test reports. The lower removal efficiencys are mainly assumed to be caused by leakage of the filter installation among other factors. Therefore, the field test methods for the particulate matter removal efficiency of ventilation systems should be established to verify actual efficiency and improve the efficiency in the future.

The purpose of this study is to provision the standard method for ensuring the reliability of measuring indoor air quality in public transportation. The objective is to determine the difference in the measured concentration values according to various conditions. These variables include measurement conditions, measurement equipment, measurement points, and measurement time. The value differences are determined by measuring the PM10 and CO2 concentration of subways, and express buses and trains, which are targets of indoor air quality management. The concentration of CO2 was measured by the NDIR method and that of PM10 was measured by the gravimetric method and light-scattering method. Statistically, the results of the concentration comparison according to the measurement points of the public transportation modes were not significantly different (p > 0.05), and it is deemed that the concentration is not affected by the measurement points. In terms of the concentration analysis results according to the measurement method, there was a difference of the concentration between the gravimetric and light scattering method. In the case of the light scattering method, the concentration differed depending on whether it was corrected with standard particles in the laboratory environment.

한국의 탐사보도 수용자를 설문조사해서 탐사보도 역할 인식, 이용량, 인구사회학적 요인이 비윤리적, 위법적인 탐사보도 취재방법 허용정도에 미치는 영향과 취재의 자유에 대한 인식을 분석하였다. 수용자들은 탐사보도가 사회감시와 정보제공 역할을 한다고 생각하면서도 비윤리적이고 위법적인 취재방법에 대해서는 부정적이어서 언론의 자유는 절대적 자유가 아니라고 인식하였다. 수용자들은 취재방법과 취재목적에 따라 취재의 자유에 차이가 있다고 생각하는 것으로 분석되었다. 취재방법이 개인 프라이버시를 침해할수록 제한적으로 생각하고, 취재목적의 공익성이 높으면 넓게 생각하였다. 수용자들이 사회감시역할을 중시하면 비윤리적이고 위법적 취재방법이라도 수용하였고, 정보제공역할을 중시하면 부정적으로 생각하였다. 탐사보도 이용량은 많은 취재방법에 정적인 영향을 주어서 탐사보도 이용만족도가 높았다. 남성보다는 여성이, 연령과 학력은 낮을수록, 수입은 많을수록 취재방법을 더 허용하였다. 환경과 건강 분야 보도 이용량이 많을수록 취재방법을 더 허용하고, 교육, 정부, 범죄 분야 보도를 많이 이용할수록 부정적이었다. 한국 수용자들은 언론의 환경과 건강 분야 감시를 중시하였다.

The concentrations of volatile organic compounds (VOCs) and odor-inducing substances were measured using selected ion flow tube mass spectrometers (SIFT-MS) and a drone equipped with an air quality monitoring system. SIFT-MS can continuously measure the concentration of VOCs and odor-inducing substances in realtime without any pre-treating steps for the sample. The vehicle with SIFT-MS was used for real-time measurement of VOC concentration at the site boundaries of pollution sources. It is possible to directly analyze VOCs concentration generated at the outlets by capturing air from the pollution sources with a drone. VOCs concentrations of nine spots from Banwol National Industrial Complex were measured by a vehicle equipped with SIFT-MS and were compared with the background concentration measured inside the Metropolitan Air Quality Management Office. In three out of the nine spots, the concentration of toluene, xylene, hydrogen sulfide, and methyl ethyl ketone was shown to be much higher than the background concentration. The VOCs concentrations obtained using drones for high-concentration suspected areas showed similar tendencies as those measured using the vehicle with SIFTMS at the site boundary. We showed that if both the drone and real-time air quality monitoring equipment are used to measure VOCs concentration, it is possible to identify the pollutant sources at the industrial complex quickly and efficiently check sites with high concentrations of VOCs.

Industrial Motors diagnostic equipment is highly dependent on the automation system, so if there are defects in the automation equipment, it can only rely on the operator’s intuitive judgment.To help with intuitive judgment, Park’s Vactor Approach(PVA) represents the current signal as a pattern of circles, so it can tell if a fault occurs when the circle is distorted. However, the failure to judge the degree of distortion of the circle pattern is the basis of the fault, so it will face difficulties. In this paper, in order to compare the faults of PVA, the period of d-axis current of PVA pulsation was mastered, so that two phase differences occurred in the same signal source. Through experiments, it is confirmed that this is a 90 degree cross formation of PVA, which is convenient for judging from the vision that there is no fault, thus helping the operator to make intuitive judgment.

정공 수송 층 (HTL)은 PSC의 효율 및 안정성을 증가시키기 위해 페로브스카이트 태양 전지 (PSC)에서 중요한 역할을 한다. 본 연구에서, 우리는 PSCs에서 HTL 스핀 코팅 및 블레이드 코팅 방법으로 니켈 산화물 구리 산화물 (NiO-CuO) 나노 입자 (NPs) 박막을 준비하였다. 스핀 코팅 및 블레이드 코팅 된 NiO-CuO 필름의 필름 특성은 원자력 현미경 (AFM)을 사용하여 조사하었고, 장치 성능에 대한 효과는 J-V 특성, 양자 효율 및 광 강도의 Voc 의존성을 사용하여 조사하었다. 결과적으로, 스핀 코팅으로 15.28 % 효율, 블레이드 코팅으로 11.18 % 효율을 달성하였다.

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

Under the situation which customer orders are cancelled unless all products in the order are delivered all at once, this paper concentrates on the purchase dependent demands and explores the systematic approach to implant the purchase dependence into the multi-product inventory model. First, by acknowledging that it is a challenging task to formulate a suitable inventory model for the purchase dependence, we derive the optimal solution condition using an EOQ model and extend the optimal solution condition to periodic review models. Then, through the comparison simulation of four inventory policies regarding several degrees of purchase dependence, we demonstrate that the inventory models which consider the purchase dependence generate less total cost than the inventory models which ignore the purchase dependence. In general, the inventory models which consider the purchase dependence reduce the loss of sales by maintaining more inventories, which results in reducing the total cost. Consequently, the simulation result supports the effectiveness of this paper’s approach. In addition, this paper uses the individual order period and joint order period obtained from the EOQ model for the multi-product inventory model. Through the in-depth analysis of comparing the two models, we observe that the model of using the joint order period produces less total cost when the degree of purchase dependence is high, but the model of using the individual order period produces less total cost when the degree of purchase dependence is low.