• Journal of Wetlands Research
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• v.14 no.1
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• pp.75-87
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• 2012

Hyporheic zone is a region beneath and alongside a stream, river, or lake bed, where there is mixing of shallow groundwater and surfacewater. Hyporheic exchange controls a variety of physical, biogeochemical and thermal processes, and provides unique ecotones in a aquatic ecosystem. Field and experimental observations, and modeling studies indicate that hyporheic exchange is mainly in response to pressure gradients driven by the geomorphological features of stream beds. In the reach scale of a stream, pool-riffle structures dominate the exchange patterns. Flow over a pool-riffle sequence develops recirculation zones and stagnation points, and this flow structures make irregular pressure gradient which is driving force of the hyporheic exchange. In this study, 3 D hydro-dynamic model solves the Reynolds-averaged Navier-Stokes equations for the surface water and Darcy's Law and the continuity equation for ground water. The two sets of equations are coupled via the pressure distribution along the interface. Simulation results show that recirculation zones and stagnation points in the pool-riffle structures dominantly control the upwelling and downwelling patterns. With decrease of recirculation zones, length of donwelling zone formed in front of riffles is reduced and position of maximum downwelling point moves downward. The numerical simulation could successfully predict the behavior of hyporheic exchange and contribute the field study, river management and restoration.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Environmental Analysis Health and Toxicology
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• v.31
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• pp.10.1-10.8
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• 2016

Objectives Aromatase inhibitors that block estrogen synthesis are a proven first-line hormonal therapy for postmenopausal breast cancer. Although it is known that standardized extract of Ginkgo biloba (EGb761) induces anti-carcinogenic effects like the aromatase inhibitors, the effects of EGb761 on steroidogenesis have not been studied yet. Therefore, the effects of EGb761 on steroidogenesis and aromatase activity was studied using a H295R cell model, which was a good in vitro model to predict effects on human adrenal steroidogenesis. Methods Cortisol, aldosterone, testosterone, and $17{\beta}$-estradiol were evaluated in the H295R cells by competitive enzyme-linked immunospecific assay after exposure to EGb761. Real-time polymerase chain reaction were performed to evaluate effects on critical genes in steroid hormone production, specifically cytochrome P450 (CYP11/ 17/19/21) and the hydroxysteroid dehydrogenases ($3{\beta}$-HSD2 and $17{\beta}$-HSD1/4). Finally, aromatase activities were measured with a tritiated water-release assay and by western blotting analysis. Results H295R cells exposed to EGb761 (10 and $100{\mu}g/mL$) showed a significant decrease in $17{\beta}$-estradiol and testosterone, but no change in aldosterone or cortisol. Genes (CYP19 and $17{\beta}$-HSD1) related to the estrogen steroidogenesis were significantly decreased by EGb761. EGb761 treatment of H295R cells resulted in a significant decrease of aromatase activity as measured by the direct and indirect assays. The coding sequence/Exon PII of CYP19 gene transcript and protein level of CYP19 were significantly decreased by EGb761. Conclusions These results suggest that EGb761 could regulate steroidogenesis-related genes such as CYP19 and $17{\beta}$-HSD1, and lead to a decrease in $17{\beta}$-estradiol and testosterone. The present study provides good information on potential therapeutic effects of EGb761 on estrogen dependent breast cancer.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.7
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• pp.690-698
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• 2010

Contamination of groundwater resources by organic chemicals has become an issue of increasing environmental concern. Surfactant-enhanced aquifer remediation (SEAR) is widely recognized as one of the most promising techniques to remediate organic contaminations in-situ. Solutions of surfactant or surfactant with polymer are used to dramatically expedite the process, which in turn, may reduce the treatment time of a site compared to use of water alone. In the design of surfactant-based technologies for remediation of organic contaminated aquifers, it is very important to have a considerable analysis using extensive numerical simulations prior to full-scale implementation. This study investigated the formation and flow of microemulsions during SEAR of organic-contaminated aquifer using the finite difference model UTCHEM, a three-dimensional, multicomponent, multiphase, compositional model. The remediation process variables considered in this study were the sequence of injection fluids, the injection and extraction rate, the concentrations of polymer in surfactant slug and chase water, and the duration of surfactant injection. For each variable, temporal changes in injection and production wells and spatial distributions of relative saturations in the organic phase were compared. Cleanup time and cumulative organic recovery were also quantified. The study would provide useful information to design strategies for the remediation of nonaqueous phase liquid-contaminated aquifers.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1597-1609
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• 2004

A lot of lipreading system has been proposed to compensate the rate of speech recognition dropped in a noisy environment. Previous lipreading systems work on some specific conditions such as artificial lighting and predefined background color. In this paper, we propose a real-time lipreading system which allows the motion of a speaker and relaxes the restriction on the condition for color and lighting. The proposed system extracts face and lip region from input video sequence captured with a common PC camera and essential visual information in real-time. It recognizes utterance words by using the visual information in real-time. It uses the hue histogram model to extract face and lip region. It uses mean shift algorithm to track the face of a moving speaker. It uses PCA(Principal Component Analysis) to extract the visual information for learning and testing. Also, it uses HMM(Hidden Markov Model) as a recognition algorithm. The experimental results show that our system could get the recognition rate of 90% in case of speaker dependent lipreading and increase the rate of speech recognition up to 40～85% according to the noise level when it is combined with audio speech recognition.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of Life Science
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• v.31 no.1
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• pp.83-89
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• 2021

Uu-ilys, an i-type lysozyme from spoon worm (Urechis unicinctus), is an innate immune factor that plays an important role in the defense against pathogens. It also possesses non-enzymatic antibacterial activity. Thus, there is a possibility to develop an antimicrobial model peptide from Uu-ilys. In this study, we report the design, production, and antibacterial activity of an Uu-ilys analog that exhibits antibacterial activity. The Uu-ilys structure was fragmented according to its secondary structures to predict the regions with antimicrobial activity using antimicrobial peptide (AMP) prediction tools from different AMP databases. A peptide containing the C-terminal fragment was predicted to exert antimicrobial activity. The chosen fragment was designated as an Uu-ilys analog containing the C-terminal fragment, Uu-ilys-CF. To examine the possibility of developing an AMP using the sequence of Uu-ilys-CF, recombinant fusion protein (TrxA-Uu-ilys-CF) was produced in an expression system that was heterologous. The produced fusion protein was cleaved after methionine leaving Uu-ilys-CF free from the fusion protein. This was then isolated through high performance liquid chromatography and reverse phase column, CapCell-Pak C18. The antibacterial activity of Uu-ilys-CF against different microbial strains (four gram-positive, six gram-negative, and one fungal strain) were assessed through the ultrasensitive radial diffusion assay (URDA). Among the bacterial strains tested, Salmonella enterica was the most susceptible. While the fungal strain tested was not susceptible to Uu-ilys-CF, broad spectrum antibacterial activity was observed.

• Environmental Analysis Health and Toxicology
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• v.15 no.4
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• pp.131-137
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• 2000

The release of hazardous waste materials into the environment poses serious risks in humans and ecosystems. The risk assessment of environmental pollutants including hazardous chemicals requires a comprehensive measurement of hazard and exposure of the chemicals that can be achieved by toxicity evaluation using a biological system such as biomarkers. In this report we have tried to develop a biomarker used to elucidate a molecular basis of, and to monitor abnormal behaviors caused by diazinon in Japanese medaka (Oryzias latipes) as a model organism. First, an attempt was made to clone tyrosine hydroxylase gene from Japanese medaka that would be a candidate for a biomarker for neuronal modulations and behaviors. For monitoring experiments at behavioral and molecular biological levels, the fish were treated under different sublethal conditions of diazinon and their behavioral responses were observed . In this study we have successfully cloned a partial TH gene from the medaka fish through PCR screening of an ovary cDNA library. DNA sequencing analysis revealed that the amplified fragment was 327 bp encoding 109 amino acids. Comparing the DNA sequence of medaka TH with other species, TH gene revealed the DNA sequence was completely identical to that of rat TH. In the RT-PCR, 330 Up of mRNA was consistently amplified in all the treated samples including control There were no significant differences in the TH expression level regardless of treating concentrations (1∼5,000 ppb) and time (0∼48 hr) The reason appeared to be that RT-PCR was not performed using through a quantitative analysis normalized against an actin gene expression. Organ or tissue - specific detection of TH activity and mRNA as biomarkers will be a useful monitoring tool for neurobehavioral changes in fish influenced by toxic chemicals. Furthermore, quantitative analysis of locomotive patterns and its correlation with the neurochemical and molecular data would be highly useful in measuring toxicity and hazard ofvarious environmental pollutants.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.251-258
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• 2010

'Naturalness' is the important "ness" which is a key factor in image quality assessment. 'Naturalness' is a representive factor depending on the context of the image which arouses different emotions. The Image Quality Circle was split into two steps. The first step is predicting the visual perceptual attribute which are lightness, colourfulness, hue and contrast. The next step is SSE which is dependent to image contents. In this study the image contents are grouped in genres. The images were rendered using four different colour attributes which are lightness, contrast, colourfulness and hue. Using a scale, the score of image quality and SSE was asked to each participant for all rendered images. A seven-point category scale of increasing amount of "ness" is used as a quantitative adjectives sequence. The image quality model was built by combining the SSEs for each scene. The SSEs, where vividness is common, are considered as independent variables to predict the image quality score. Then the vividness model was built using colour attributes as variables to predict the vividness of each scene (genre). Vividness is an important factor of naturalness which the meaning is different for all scenes that links the naturalness and image quality. The vividness meaning was different for each scene (genre). Therefore, the colour attributes that express the vividness would depend on the image content.