• 상하수도학회지
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• 제35권6호
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• pp.417-424
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• 2021

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.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2000년도 학술발표회 발표논문집
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• pp.623-628
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• 2000

This study were conducted to evaluate the mass balance of phosphorus and nitrogen with cropping in experimental pot where the mass of input concentration, and fertilization were variable. Four treatments include CSWNF, TWCF, SWNF. And these cases were compared to the control case of tap water irrigation with conventional fertilization (CONTROL). Fertilization were following conventional fertilization , N : P : K = 11kg : 7kg : 8kg. Input water loading was CSWNF (N:48.7g ,P:3.6g), TWCF(7.8g, 0.6g), SWHF(38.8g ,2.9g), TWNF(38.8g, 2.9g ) and CONTROL(0g ,0g) The result is nitrogen decrease rate; TWCF(19.2%), SWHF(14.9%), CSWNF(9.2%) and SWCF(5.6%). phosphorous decrease rate ; TWCF (10%), SWHF(3.7%), SWCF(0.9%) and CSWNF(0.3%).

• Environmental Engineering Research
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• 제17권2호
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• pp.89-94
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• 2012

Pyrolysis/gasification technology utilizes an energy conversion technique from various waste resources, such as biomass, solid waste, sewage sludge, and etc. to generating a syngas (synthesis gas). However, one of the major problems for the pyrolysis gasification is the presence of tar in the product gas. The tar produced might cause damages and operating problems on the facility. In this study, a gliding arc plasma reformer was developed to solve the previously acknowledged issues. An experiment was conducted using surrogate benzene and naphthalene, which are generated during the pyrolysis and/or gasification, as the representative tar substance. To identify the characteristics of the influential parameters of tar decomposition, tests were performed on the steam feed amount (steam/carbon ratio), input discharge power (specific energy input, SEI), total feed gas amount and the input tar concentration. In benzene, the optimal operating conditions of the gliding arc plasma 2 in steam to carbon (S/C) ratio, 0.98 $kWh/m^3$ in SEI, 14 L/min in total gas feed rate and 3.6% in benzene concentration. In naphthalene, 2.5 in S/C ratio, 1 $kWh/m^3$ in SEI, 18.4 L/min in total gas feed rate and 1% in naphthalene concentration. The benzene decomposition efficiency was 95%, and the energy efficiency was 120 g/kWh. The naphthalene decomposition efficiency was 79%, and the energy yield was 68 g/kWh.

• 한국멀티미디어학회논문지
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• 제25권9호
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• pp.1266-1272
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• 2022

With the recent development of computer vision technology, image processing-based mechanical devices are being developed to realize autonomous driving. The camera-taken images of image processing-based machines are invisible due to scattering and absorption of light in foggy conditions. This lowers the object recognition rate and causes malfunction. The safety of the technology is very important because the malfunction of autonomous driving leads to human casualties. In order to increase the stability of the technology, it is necessary to apply an efficient haze removal algorithm to the camera. In the conventional haze removal method, since the haze removal operation is performed regardless of the haze concentration of the input image, excessive haze is removed and the quality of the resulting image is deteriorated. In this paper, we propose an automatic haze removal method that removes haze according to the haze density of the input image by applying Ngo's Haziness Degree Evaluator (HDE) to Kim's haze removal algorithm using Hazy Particle Map. The proposed haze removal method removes the haze according to the haze concentration of the input image, thereby preventing the quality degradation of the input image that does not require haze removal and solving the problem of excessive haze removal. The superiority of the proposed haze removal method is verified through qualitative and quantitative evaluation.

• 한국의류학회지
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• 제18권1호
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• pp.23-30
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• 1994

The objective of this study was to analyze the effect of the dissolution of detergent on washing and rinsing efficiency, and the possibility of saving detergent in washing. Washing and rinsing efficiency were reviewed in three different ways : A method was to input dissolved detergent by the use of rapid detergent dissolution instrument, B method was to input dissolved detergent beforehand, and C method was to input undissolved detergent. The results were as follows 1. A method > B method > C method was shown in detergency with washing time and three method's gaps were reduced because detergent dissolution effect was reduced by mechanical action in proportion of washing time. Especially, according to detergency of A method of 0.07% and C method of 0.1% was appeared approximately, saving detergent was expected by rapid dissolution of detergent. 2. A method > B method > C method was shown in detergency with washing temperature. As the washing temperature rose, the detergent reached chemically more activated state and become easily soluble. It resulted for the detergent to penetrate and adhere to laundry easily. 3. A method > B method > C method was shown in detergency with detergent concentration and C method was more sensitive than A, B method against change of detergent cocentration. Rapid detergent dissolution made it possible not only to enhance the washing efficiency but also to save the detergent because detergency of A method in low concentration is higher than that of C method. 4. A method $\geq$ B method > C method was shown in rinsing ratio with detergent input method. It indicated input dissolved detergent was advantageous in rinsing. 5. The result of anion surfactant concentration test of each process with detergent input method was shown like that A method In B method > C method in washing and reverse result was shown in rinsing. The anion surfactant concentration of C method was low during washing but high during rinsing. This was identical with previous results which shown the washing and rinsing efficiency improved with dissolved detergent usasge. 6. C method > B method > A method was shown in the degree of remaining detergent after rinsing. There was no remaining detergent after second rinsing in A and B method, but in C method four rinsings were required for the same result. Consequently, in A and B methods, less water and electricity were used, and less abrasion of cloth.

• 조명전기설비학회논문지
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• 제15권1호
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• pp.70-77
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• 2001

본 논문에서는 고도정수처리설비에 활용되는 오존접촉조의 반응 특성을 퍼지 모델 형태로 표현하는 방법을 제안한다. 퍼지모델에 사용될 입력 및 출력 변수들은 오존처리의 목적과 정수장의 수질관리항목을 기준으로 선정하였다. 제안된 입력 변수들은 용존유기탄소농도, $UV_{254}$흡광도, 과망간산칼륨소비량, 주입오존농도, 수온 및 접촉시간이며, 출력변수들은 용존유기탄소농도, $UV_{254}$흡광도 및 과망간산칼륨소비량이다. 입력변수들에 대한 소속도 함수들은 삼각형 형태로 설계하였으며, 파이롯플랜트에서 취득한 조업데이터를 참고하여 소속도를 결정했다. 퍼지모델의 결론부는 선형식 형태로 설계하였으며, 선형식에 포함되는 상수들은 조업데이터를 이용하여 최소자승법으로 구했다. 또한 출력 변수들간에 상호 영향이 없다는 특성으로부터 전체 퍼지모델로을 각 변수별로 독립적인 기능을 갖는 3개의 부분 퍼지모델로 분할하여 설계함으로서 계산과 이해의 편리를 도모하였다. 모의실험을 통해 제안된 퍼지모델의 타당성을 확인해 본 결과, 모델의 튜닝시에 사용한 입력 데이터에 대해 퍼지모델의 출력이 조업데이터와 거의 동일함을 알 수 있었다.

• 한국산업보건학회지
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• 제29권3호
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• pp.358-367
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• 2019

Objectives: This study aimed to review model algorithms and input parameters applied to some exposure models and to compare the simulated estimates using an exposure scenario from each model. Methods: A total of five exposure models which can estimate inhalation exposure were selected; the Korea Ministry of Environment(KMOE) exposure model, European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment(ECETOC TRA), SprayExpo, and ConsExpo model. Algorithms and input parameters for exposure estimation were reviewed and the exposure scenario was used for comparing the modeled estimates. Results: Algorithms in each model commonly consist of the function combining physicochemical properties, use characteristics, user exposure factors, and environmental factors. The outputs including air concentration ($mg/m^3$) and inhaled dose(mg/kg/day) are estimated applying input parameters with the common factors to the algorithm. In particular, the input parameters needed to estimate are complicated among the models and models need more individual input parameters in addition to common factors. In case of CEM, it can be obtained more detailed exposure estimates separating user's breathing zone(near-field) and those at influencing zone(far-field) by two-box model. The modeled exposure estimates using the exposure scenario were similar between the models; they were ranged from 0.82 to $1.38mg/m^3$ for concentration and from 0.015 to 0.180 mg/kg/day for inhaled dose, respectively. Conclusions: Modeling technique can be used for a useful tool in the process of exposure assessment if the exposure data are scarce, but it is necessary to consider proper input parameters and exposure scenario which can affect the real exposure conditions.

• 한국산업보건학회지
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• 제30권2호
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• pp.236-248
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• 2020

Objectives: The purpose of this study was to evaluate the concentrations of particulate matter generated during coffee roasting and to study various factors affecting the concentrations. Methods: Differences in concentration levels were investigated based on various factors to understand the emission rates of particulate matter over time and to compare the mass and number concentrations according to their size. Sampling was performed in closed laboratories without the operation of air conditioning or ventilation. Optical Particle Sizer(OPS) was used as a measuring device. An OPS measures using a light-scattering method. Sampling was performed for sixty minutes at one-minute intervals. The background concentration was measured for about 30 minutes before starting of coffee roasting. The concentrations of particulate matter generated during coffee roasting were monitored until roasted coffee beans were removed from the roaster and cooled down. Several factors affecting the concentrations of particulate matter were investigated, which includes the origins of green beans, the roasting level, and the input amount of green beans. Results: The results of this study may be summarized as follows: 1) There was no difference in particulate matter concentration levels by the origin of the green beans, but a statistically significant difference in concentration levels by roasting level and the input amount of green beans; The higher the roasting level, the higher was the particulate matter concentration. The more green beans we put in the roaster, the higher were the concentrations; 2) The PM10 mass concentrations increased over time. The average concentration after roasting was higher than the average concentration during roasting; 3) In the distribution of mass and number concentration by particle diameter, the majority of particles was below 2.5 ㎛. Conclusions: Persons who work in roastery cafes can be exposed to high concentrations of particulate matter. Therefore, personal exposure and risk assessment should be conducted for roastery cafe workers.

• 한국건설순환자원학회논문집
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• 제8권1호
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• pp.129-133
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• 2020

우레아-포름알데히드(UF) 캡슐막의 마이크로캡슐 합성 공정에 있어서 반응 폐액에 포함된 포름알데히드의 잔류량을 GC-MS로 분석하였다. 포름알데히드의 반응에 영향을 주는 주된 인자로서 pH, 암모늄클로라이드 투입량, 온도의 3가지를 선정하고, 이 인자들이 반응 폐액 중의 포름알데히드 농도에 미치는 영향을 조사하였다. 실험 조건 중 암모늄클로라이드를 0.025g 투입한 경우에는 캡슐막 형성이 안 되거나 캡슐막이 약한 경향이 있어서 기본적으로 이 조건은 마이크로캡슐화에 부적절한 것으로 판단된다. 본 연구의 실험 조건에서 포름알데히드 잔류량이 최소화되는 마이크로캡슐화 조건은 70℃의 온도와 2.5의 pH에서 암모늄클로라이드를 0.050g 투입하는 조건임이 확인되었다. 본 연구의 결과는 보다 안전한 작업환경에서 마이크로캡슐화를 수행하는데 기여할 수 있을 것으로 기대된다.

• 한국미생물·생명공학회지
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• 제21권4호
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• pp.354-365
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• 1993

A novel control method with automatic tuning of PID controller parameters has been developed for efficient regulation of dissolved oxygen concentration in fed-batch fermentations of Escherichia coli. Agitation speed and oxygen partial pressure in the inlet gas stream were chosen to be the manipulated variables. A heuristic reasoning allowed improved tuning decisions from the supervision of control performance indices and it coule obviate the needs for process assumptions or disturbance patterns. The control input consisted of feedback and feedforword parts. The feedback part was determined by PID control and the feedforward part is determined from the feed rate. The proportional gain was updated on-line by a set of heuristics rules based on the supervision of three performance indices. These indices were output error covariance, the average value of output error, and input covariance, which were calculated on-line using a moving window. The integral and derivative time constants were determined from the period of output response. The specific growth rate was maintained at a low level to avoid acetic acid accumulation and thus to achieve a high cell density. The specific growthe rate was estimated from the carbon dioxide evolution rate. In fed-batch fermentation, the simutaneous control of dissolved oxygen concentration (at 0.2; fraction of saturated value) and specific growth rate (at 0.25$hr^{-1}$) was satisfactory for the entire culture period in spite of the changes in the feed rate and the switching of control input.