• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1377-1385
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• 2016

Conventional X-band marine radar has been used as one of the effective tools for collecting and retrieving ocean surface information parameters for three decades. Several wave information extracting algorithms have been designed in such a way that they can be utilized for efficiently estimating sea surface wave parameters such as current velocities, wave direction, significant wave heights in VTS (Vessel Traffic Service). However, their performances are still restricted. For the purpose of overcoming the performance limits, in this paper, first the conventional algorithms are analyzed and their performances are compared, and then a new control algorithm is proposed. Furthermore, we try to improve the estimation performances of typical wave parameters including wave directions and significant wave heights by introducing linear regression model in the process of computing wave information extraction. Through several simulations with the X-band radar images, it is shown that the proposed method is very effective in estimating the wave information compared to the real measured buoy data.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.305-311
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• 2020

This study presents a probabilistic time series forecast of ship structural response using Bayesian inference combined with Volterra linear model. The structural response of a ship exposed to irregular wave excitation was represented by a linear Volterra model and unknown uncertainties were taken care by probability distribution of time series. To achieve the goal, Volterra series of first order was expanded to a linear combination of Laguerre functions and the probability distribution of Laguerre coefficients is estimated using the prepared data by treating Laguerre coefficients as random variables. In order to check the validity of the proposed methodology, it was applied to a linear oscillator model containing damping uncertainties, and also applied to model test data obtained by segmented hull model of 400,000 DWT VLOC as a practical problem.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.54-60
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• 2004

Face recognition is one of the problems to be solved by appearance based matching technique. However, the appearance of face image is very sensitive to variation in illumination. One of the easiest ways for better performance is to collect more training samples acquired under variable lightings but it is not practical in real world. ]:n object recognition, it is desirable to focus on feature extraction or normalization technique rather than focus on classifier. This paper presents a simple approach to normalization of faces subject to directional illumination. This is one of the significant issues that cause error in the face recognition process. The proposed method, ICR(illumination Compensation based on Multiple Linear Regression), is to find the plane that best fits the intensity distribution of the face image using the multiple linear regression, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experimental results show a significant improvement of the recognition accuracy.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.8
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• pp.444-451
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• 2016

Most research on membrane fouling models in the past are based on theoretical equations in lab-scale experiments. But these studies are barely suitable for applying on the full-scale spot where there is a sequential process such as filtration, backwash and drain. This study was conducted in submerged membrane system which being on operation auto sequentially and treating wastewater from G-water purification plant in Incheon. TMP had been designated as a fouling indicator in constant flux conditions. Total volume of inflow and SS concentration are independent variables as major operation parameters and time-series analysis and prediction of TMP were conducted. And similarity between simulated values and measured values was assessed. Final prediction model by using genetic algorithm was fully adaptable because simulated values expressed pulse-shape periodicity and increasing trend according to time at the same time. As results of twice validation, correlation coefficients between simulated and measured data were $r^2=0.721$, $r^2=0.928$, respectively. Although this study was conducted limited to data for summer season, the more amount of data, better reliability for prediction model can be obtained. If simulator for short range forecast can be developed and applied, TMP prediction technique will be a great help to energy efficient operation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• The Korean Journal of Nuclear Medicine
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• v.29 no.1
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• pp.48-53
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• 1995

This study investigates a simple mathematical model for the quantitative estimation of regional myocardial blood flow in experimental canine coronary artery thrombosis using Rb-82 dynamic myocardial positron emission tomography. The coronary thrombosis was induced using the new catheter technique by narrowing the lumen of coronary vessel gradually, which finally led to partial obstruction of coronary artery. Ten Rb-82 dynamic myocardial PET scans were performed sequentially for each experiment using our 5, 10 and 20 second acquisition protocol, respectively, and three regions of interest were drawn on the transaxial slices, one on left ventricular chamber for input function and the other two on normal and decreased perfusion segments for the flow estimation in those regions. Single compartment model has been applied to the measured sets of regional PET data, and the rate constants of influx to myocardial tissue were calculated for regional myocardial flow estimates with the three parameter fits of raw data by the Levenberg-Marquardt method. The results showed that, (1) single compartment model suggested by Kety-Schmidt could be used for the simple estimation of regional myocardial blood flow, (2) the calculated regional myocardial blood flow estimates were dependent on the selection of input function, which reflected partial volume effect and left ventricular wall motion, and (3) mathematically fitted input and tissue time activity curves were more suitable than the direct application of the measured data in terms of convergence.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Korean Journal of Environmental Biology
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• v.26 no.3
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• pp.252-263
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• 2008

The purpose of this study was to determine trophic state, based on nutrients (TN, TP), transparency (SD), and chlorophyll-${\alpha}$ (Chl) and identify their empirical relations of TN-Chl, TP-Chl and Chl-SD depending on the dataset used along with dynamics of conductivity and suspended solids. Analysis of trophic states showed that more than half of 36 reservoirs were judged as eutrophic-hypertrophic conditions depending on the trophic variables. Seasonal values of TP varied by nearly 500% and showed greater in August than any other months. In contrast, TN varied within less than 90% and all monthly mean values of TN were never fall less than 1.2 mg L$^{-1}$ indicating low seasonal variations and high ambient concentrations (eutrophic-hypertrophic state). Analysis of empirical relations in the trophic variables showed that transparency had greater functional relations with Chl (R$^2$=0.31, p<0.001) than TP (R$^2$=0.15, p<0.001) and TN (R$^2$=0.20, p<0.001). Ratios of TN : TP in the ambient water indicated that most reservoirs showed a potential phosphorous limitation on the algal growth. Thus, algal biomass, based on Chl values, was more regulated by phosphorous than nitrogen. Analysis of linear regression model, based on log-transformed annual mean values, showed that only 30% in the variation of Chl was explained by TP (R$^2$=0.295, p=0.001, n=36) and 15% by TN (R$^2$=0.151, p=0.019, n=36). However, linear regression model, based on individual system, showed that Chl-TP model had strong positive relations (R$^2$=0.62, p=0.002, n=12), whereas the model had no any relations (p=0.892, n=12). Overall, our data suggested that averaging effect in the empirical model developments may influence the significance in the statistical analysis.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.87-100
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• 2009

The quality of early cost estimates is critical to the feasibility analysis and budget allocation decisions for public capital projects. Various researches have been attempted to develop cost prediction models in the early stage of a construction project. However, existing studies are limited on its applicability to actual projects because they focus primarily on a specific phase as well as utilize restricted information while the amount of information collectable differs from one another along with the project stages. This research aims to develop two-staged cost estimation model for the schematic planning and preliminary design process of a construction projects, considering the available information of each phase. In the schematic planning stage where outlined information of a project is only available, the Case-Based Reasoning model is used for easy and rapid elicitation of a project cost based on the extensive database of more than 90 actual highway construction projects. Then, the representing quantity-based model is proposed for the preliminary design stage where more information on the quantities and unit costs are collectable based on the alternative routes and cross-sections of a highway project. Real case studies are used to demonstrate and validate the benefits of the proposed approach. Through the two-stage cost estimation system, users are able to hold a timely prospect to presume the final cost within the budge such that feasibility study as well as budget allocation decisions are made on effectively and competitively.

• Journal of Korean Society of Forest Science
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• v.108 no.4
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• pp.592-599
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• 2019

This study aimed to develop an equation for estimating stem volume for Larix kaempferiin South Korea using independent variables, diameter at breast height (DBH), and height as being closely associated with stem volume. Analysis was conducted on the growth performance of 2,840 Larix kaempferi samples across South Korea after felling them and gleaning diameter data according to both stem height and stem analyses. In order to test the fitness of six different stem taper equations, empirical assessment was conducted for fitness index (FI), bias, mean, and absolute deviation (MAD), and coefficient variation (%CV). The two selectedmodels found to be optimal were the following: model one (V=a+bDBH²), established by employing DBH only; and model four (V=a+bDBH²H), established by utilizing DBH and height, respectively. The findings of non-linear regression indicated statistical significance (p < 0.05) in a and b, which were the coefficients for the intercepts and slopes of the models. The FI of the models ranged between 94% and 99%, and the bias was close to zero, while MAD ranged from 0.01 to 0.05, and %CV from 5.97 to 14.43, indicating a high level of fitness. Thus, using the suggested models, the basic information necessary for forest management was obtained, and an estimation of the stem volume was effected without delay soon after effecting DBH and height measurements.