• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.210-210
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• 2000

In this paper, we have investigated the properties of multi-layer perceptron (MLP) for odour patterns classification and concentration estimation simultaneously. When the MLP may be has a fast convergence speed with small error and excellent mapping ability for classification, it can be possible to use for classification and concentration prediction of volatile chemicals simultaneously. However, the conventional MLP, which is back-Propagation of error based on the steepest descent method, was difficult to use for odour classification and concentration estimation simultaneously, because it is slow to converge and may fall into the local minimum. We adapted the Levenberg-Marquardt(LM) algorithm [4,5] having advantages both the steepest descent method and Gauss-Newton method instead of the conventional steepest descent method for the simultaneous classification and concentration estimation of odours. And, We designed the artificial odour sensing system(Electronic Nose) and applied LM-BP algorithm for classification and concentration prediction of VOC gases.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.2
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• pp.107-113
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• 2011

This study was performed for the application of the exposure limit in work field and the relative noxious gas concentration was estimated by using a miniature ship. The noxious gas concentration about light and shade number distribution of measuring subject field was estimated by using floating visualization image from a miniature ship in a large wind tunnel. About the concentration estimation, the correlation of concentration distribution drawn from the existing experimental results was used, the estimation result satisfied LTEL standard.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.6
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• pp.571-575
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• 2005

A simple, but effective on-line method for estimating the mycelial cell mass concentration from agitation speed data, a most readily-available process variable, has been developed for DO-stat cultures of Agaricus blazei. The dynamic change of dissolved oxygen concentration (DOC) in the initial transient period and the change in yield were considered in the development of the estimation algorithm or estimator. Parameters in the estimation algorithm were calculated from the agitation speed data at 20% of DOC. The proposed estimator could accurately predict the cell mass concentration regardless of DOC levels in the tested range of $10{\sim}40%$, showing a good extrapolation capability.

• Journal of Soil and Groundwater Environment
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• v.20 no.4
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• pp.51-65
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• 2015

Indoor inhalation of vapors intruded into buildings is an important exposure pathway in volatile organic compoundscontaminated sites. Site-specifically measured indoor air concentration is preferentially used for risk assessment. However, when indoor air concentration of VOC is not measured, the indoor air concentration needs to be estimated from soil concentration or measured soil gas concentration of the VOC. Some risk assessment guidance (e.g., Korea Ministry of Environment (KMOE) and American Society for Testing and Materials (ASTM) International guidance) estimate the indoor air concentration from soil concentration while other guidances (e.g., United States Environmental Protection Agency (USEPA) and Dutch National Institute for Public Health (RIVM)) estimate it from measured soil gas concentration. This study derived indoor inhalation risks of intruded benzene in two benzene-contaminated residential areas with four different risk assessment guidances (i.e., KMOE, USEPA, ASTM, and Dutch RIVM) and compared the derived risks. The risk assessment results revealed that indoor air estimation approach from soil concentration could either underestimate (when the contaminant is not detected in soil) or overestimate (when the contaminant is detected in soil even at negligible concentration) the indoor air inhalation risk. Hence, this paper recommends to estimate indoor air concentration from soil gas concentration, rather than soil concentration. Discussions about the various indoor air concentration estimation approaches are provided.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.5
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• pp.86-92
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• 1999

Effluent concentration estimation equations for wetland system were developed throught statistical analysis of treatment wetland experimental data. Existin g empirical equations were reviewed for thier accuracy with experimental data, and compared with the estimatin equations. About 70 experimental data sets were used for multiple regression, and variables include influent concentration, hydraulic loading rate, average daily air temperature , and plant coverage. The estimatin equations developed for BOD5 , SS ,T-P, and T-N predicted effluent concentrations moderately well, and coefficient fo determination ($R^2$) for them was 0.74 , 0.60, 0.59 and 0.58 respectively. The equations obtained from same data but excluding plant coverage showed relatively lower $R^2$ than the former case, and it was 0.66, 0.52, 0.41 and 0.57 respectively. The EPA, WPCF , and Kadlec and Knight equations worked poorly and $R^2$ for them was significantly lower than the estimation equation developed in the study. The reason might be that the existing equations were oversimplified that they did ot include important parameters such as air temperature and plant coverage. Therefore, developing reasonable estimation equations from experiment under realistic condition is highly recommended rather than using exiting estimation equations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of Environmental Science International
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• v.2 no.3
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• pp.207-216
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• 1993

The concentrations of $SO_2$ and $SO_3$ were measured to estimate a new developed long time exposure $SO_2$ sampler at Onsan industrial area considering the meteorological factors from June to October, 1992. The mean concentration of $SO_3$ by $PbO_2$ method was 0.924 mg $SO_3 / 10cm^2$ $PbO_2$/day and their high values were shown in the center of the industrial area, which show potential pollution due to the increase of industrial activities and micrometeorological factors in and around the sites. As a result of statistical correlation between $SO_2$ concentration by new sampling method and $SO_3$ concentration by $PbO_2$ method in July and August, 1992, correlation coefficients were high (r=0.87, 0.91) and shown more than 0.83 value in the high concentration data set, which was arbitrarily divided into 7~10${\mu}l$$SO_2$ concentration in an attempt to further investigate these relationships. Therefore, use of new developed long time exposure TEX>$SO_2$ sampler is good for TEX>$SO_2$ measurement and valuable for estimation of air quality in the urban and industrial area. Key Words : a new developed long time exposure TEX>$SO_2$ sampler, correlation coefficients, high, $SO_2$ measurement, estimation of air Quality.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1990.11a
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• pp.58-62
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• 1990

The size and concentration of wear debris in lubricating oil often reveal the operating condition of the rotating machinery. To evaluate the possible application of light attenuation measurement for the estimation of wear debris concentration in the lubricating oil, the light transmittance through the lubricating oil cell contaminated with various concentrations of diatomite particles was measured, the attenuation coefficient was estimated from the transmittance measurement and the coefficients were compared with those obtained from the scattering theory. The comparision showed good agreements between them. It is also noted that the experimentally determined attenuation coefficient showed almost linear relation with particle weight concentrations for the concentrations within the range of 2000 ppm. For the case of 0 ppm weight concentration of diatomite particles in the lubricating oil cell, the thickness of the cell required to give $100 \mu W$ light attenuation is 7.75 mm. This result indicates that the light attenuation method will be one of the possible candidates of machine failure diagnostic sensors for the estimation of wear debris concentration in the lubricating oil.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.417-428
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• 2001

Spatial distribution of groundwater contaminant concentration has special characteristics such as approximate symmetric profile, for example, in the transversal direction to groundwater flow direction, a certain ratio in directional propagation distances, etc. To obtain a geophysically appropriate semivariogram which is a key factor in estimation of groundwater contaminant concentration at desired locations, these special characteristics should be considered. Specifically, the concepts of symmetry and ratio are considered in this paper. By applying these two concepts, significant improvement of semivariograms, estimation variances, and final estimation results compared with the ones by conventional approaches which usually do not account for symmetry and ratio are shown using field experimental data.

• Journal of Korean Institute of Industrial Engineers
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• v.15 no.2
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• pp.103-108
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• 1989

PERT formulae for the mean and variance of activity time are near exact only over a short interval of the concentration parameter which is defined as the sum of the two shape parameters of the beta distribution. Aiming a better estimation of the mean and variance of activity time, we propose a method of subjectively estimating this concentration parameter via estimating the probability of completing the activity within a specified time interval.