• Ocean and Polar Research
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• v.30 no.1
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• pp.109-117
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• 2008

Application of biomarkers for assessing marine environmental health risk is a relatively new field. According to the National Research Council and the World Health Organization, biomarkers can be divided into three classes: biomarkers of exposure, biomarkers of effect, and biomarkers of susceptibility. In order to assess exposure to or effect of the environmental pollutants on marine ecosystem, the following set of biomarkers can be examined: detoxification, oxidative stress, biotransformation products, stress responses, apoptosis, physiological metabolisms, neuromuscular responses, reproductions, steroid hormones, antioxidants, genetic modifications. Since early 1990s, several biomarker research groups have developed health indices of marine organisms to be used for assessing the state of the marine environment. Biomarker indices can be used to interpret data obtained from monitoring biological effects. In this review, we will summarize Health assessment Index, Biomarker Index, Bioeffect Assessment Index and Generalized Linear Model. Measurements of biomarker responses and development of biomarker index in marine organisms from contaminated sites offer great a lot of information, which can be used in environmental monitoring programs, designed for various aspects of ecosystem risk assessment.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.563-573
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• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

• Progress in Medical Physics
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• v.33 no.4
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• pp.150-157
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• 2022

Purpose: Elekta synergy^® was commissioned in the Seoul National University Veterinary Medical Teaching Hospital. Recently, Chung-Ang University Gwang Myeong Hospital commissioned Elekta Versa HD^TM. The beam characteristics of both machines are similar because of the same Agility^TM MLC Model. We compared measured beam data calculated using the Elekta treatment planning system, Monaco^®, for each institute. Methods: Beam of the commissioning Elekta linear accelerator were measured in two independent institutes. After installing the beam model based on the measured beam data into the Monaco^®, Monte Carlo (MC) simulation data were generated, mimicking the beam data in a virtual water phantom. Measured beam data were compared with the calculated data, and their similarity was quantitatively evaluated by the gamma analysis. Results: We compared the percent depth dose (PDD) and off-axis profiles of 6 MV photon and 6 MeV electron beams with MC calculation. With a 3%/3 mm gamma criterion, the photon PDD and profiles showed 100% gamma passing rates except for one inplane profile at 10 cm depth from VMTH. Gamma analysis of the measured photon beam off-axis profiles between the two institutes showed 100% agreement. The electron beams also indicated 100% agreement in PDD distributions. However, the gamma passing rates of the off-axis profiles were 91%-100% with a 3%/3 mm gamma criterion. Conclusions: The beam and their comparison with MC calculation for each institute showed good performance. Although the measuring tools were orthogonal, no significant difference was found.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.96-105
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• 1998

The aim of this paper is to propose the method of WDHMM(Weighted DHMM), using the MLP-VQ for the improvement of speaker-independent connect-digit recognition system. MLP neural-network output distribution shows a probability distribution that presents the degree of similarity between each pattern by the non-linear mapping among the input patterns and learning patterns. MLP-VQ is proposed in this paper. It generates codewords by using the output node index which can reach the highest level within MLP neural-network output distribution. Different from the old VQ, the true characteristics of this new MLP-VQ lie in that the degree of similarity between present input patterns and each learned class pattern could be reflected for the recognition model. WDHMM is also proposed. It can use the MLP neural-network output distribution as the way of weighing the symbol generation probability of DHMMs. This newly-suggested method could shorten the time of HMM parameter estimation and recognition. The reason is that it is not necessary to regard symbol generation probability as multi-dimensional normal distribution, as opposed to the old SCHMM. This could also improve the recognition ability by 14.7% higher than DHMM, owing to the increase of small caculation amount. Because it can reflect phone class relations to the recognition model. The result of my research shows that speaker-independent connected-digit recognition, using MLP-VQ and WDHMM, is 84.22%.

• Journal of Technology Innovation
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• v.20 no.2
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• pp.161-187
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• 2012

Recently, environmentally friendly technology are becoming important due to reconsideration about climate change and environmental pollution. In addition, as well as technical skills and social interaction through an analysis of the nonlinear transition management and policy implementation are emerging. This study of the development of photovoltaic industry in Korea 10 years analyze with strategic niche management (SNM) based on the theoretical and multi-layered perspective (MLP) is used as the analytical framework. Choose the gerverment-support project for niche technology, through a process of quantifying and alnalyze the phase transition to Regime with the numerical method and policy vision, learning effects, and network that key elements of SNM, MLP. Through the analysis of the photovoltaic industry technology-commercialization phase was investigated. This conventional overall and step-by-step model for technical management is proposed to replace exiting linear and narrow method and through the case study its validity was confirmed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.4
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• pp.387-396
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• 2004

A scheme to evaluate the number of users and cell coverage of a WCDMA supporting multi-rate traffic is newly presented through calculation of the realizable Erlang capacity from a derived blocking probability and the path loss from the COST231 Walfisch-Ikegami(W) model. We evaluate the voice-data Erlang capacities at various data rates of 15 kbps to 960 kbps and it is shown that they have a linear relationship to each other. When the E$\_$b//N$\_$o/ is low from 4 ㏈ to 3 ㏈ in case of voice capacity of 50 Erlang at 8 kbps, the result shows the increase for the data capacity of 10 Erlang and the enlargement of 100 m for the cell coverage at low rate of 15 kbps, and the increase of 0.11 Erlang and the enlargement of 40 m at high rate of 960 kbps. The increase of the blocking probability results in the increase of the Erlang capacity, but not an effect on the cell coverage, and the increase of active users in a cell results in the decrease of the coverage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.443-453
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• 2013

The hybrid test is one of the most advanced test methods to predict the structural dynamic behavior with the interaction between a physical substructure and a numerical modeling in the hybrid control system. The purpose of this study is to perform the multi-directional dynamic test of a steel frame structure with the real-time hybrid system and to evaluate the validation of the results. In this study, FEAPH, nonlinear finite element analysis program for hybrid only, was developed and the hybrid control system was optimized. The inefficient computational time was improved with a fixed number iteration method and parallel computational techniques used in FEAPH. Furthermore, the previously used data communication method and the interface between a substructure and an analysis program were simplified in the control system. As the results, the total processing time in real-time hybrid test was shortened up to 10 times of actual measured seismic period. In order to verify the accuracy and validation of the hybrid system, the linear and nonlinear dynamic tests with a steel framed structure were carried out so that the trend of displacement responses was almost in accord with the numerical results. However, the maximum displacement responses had somewhat differences due to the analysis errors in material nonlinearities and the occurrence of permanent displacements. Therefore, if the proper material model and numerical algorithms are developed, the real-time hybrid system could be used to evaluate the structural dynamic behavior and would be an effective testing method as a substitute for a shaking table test.

• Economic and Environmental Geology
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• v.48 no.2
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• pp.147-160
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• 2015

In 2014, on 31 August and 1 September, the emissions of $CH_4$, $CO_2$, and $O_2$ gases were measured six times using the closed chamber method from exposed tidal flat sediments in the same position relative to the low point of the tidal cycle in the Eoeun-ri, Taean-gun, on the Mid-western Coast of Korea. The concentrations of $CH_4$ in the air sample collected in the chamber were measured using gas chromatography with an EG analyzer, model GS-23, within 6 hours of collection, and the other gases were measured in real time using a multi-gas monitor. The gas emission fluxes (source (+), and sink (-)) were calculated from a simple linear regression analysis of the changes in the concentrations over time. In order to see the surrounding parameters (water content, temperature, total organic carbon, average mean size of sediments, and the temperature of the inner chamber) were measured at the study site. On the first day, across three measurements during 5 hours 20 minutes, the observed $CO_2$ flux absorption was -137.00 to $-81.73mg/m^2/hr$, and the $O_2$ absorption, measured simultaneously, was -0.03 to $0.00mg/m^2/hr$. On the second day using an identical number of measurements, the $CO_2$ absorption was -20.43 to $-2.11mg/m^2/hr$, and the $O_2$ absorption -0.18 to $-0.14mg/m^2/hr$. The $CH_4$ absorption before low tide was $-0.02mg/m^2/hr$ (first day, Pearson correlation coefficient using the SPSS statistical analysis is -0.555(n=5, p=0.332, pronounced negative linear relationship)), and $-0.15mg/m^2/hr$ (second day, -0.915(n=5, p=0.030, strong negative linear relationship)) on both measurement days. The emitted flux after low tide on both measurement days reached a minimum of $+0.00mg/m^2/hr$ (+0.713(n=5, p=0.176, linear relationship which can be almost ignored)), and a maximum of $+0.03mg/m^2/hr$ (+0.194(n=5, p=0.754, weak positive linear relationship)) after low tide. However, the absolute values of the $CH_4$ fluxes were analyzed at different times. These results suggest that rate for $CH_4$ fluxes, even the same time and area, were influenced by changes in the tidal cycle characteristics of surface sediments for understanding their correlation with these gas emissions, and surrounding parameters such as physiochemical sediments conditions.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.77-88
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• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.