• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.1
• /
• pp.13-19
• /
• 2011

In this paper, a visual surveillance system using SOPC based NIOS II embedded processor and C2H compiler was implemented. In this system, the IP is constructed by C2H compiler for the output of the camera images, image processing, serial communication and network communication, then, it is implemented to effectively control each IP based on the SOPC and the NIOS II embedded processor. And, an algorithm which updates the background images for high speed and robust detection of the moving objects is proposed using the Adaptive Gaussian Mixture Model(AGMM). In results, it can detecte the moving objects(pedestrians and vehicles) under day-time and night-time. It is confirmed that the proposed AGMM algorithm has better performance than the Adaptive Threshold Method(ATM) and the Gaussian Mixture Model(GMM) from our experiments.

• Journal of the Korean GEO-environmental Society
• /
• v.6 no.1
• /
• pp.15-21
• /
• 2005

In this study, the utilization of coal-ash and phosphogypsum was considered as the evapotranspiration final landfill cover(ET cover) material. Cover material considered was the mixture of the weathered granite soil, coal-ash and phosphogypsum and so we sequentially performed the leaching test, column test and field model test to investigate the environmental effects of mixtures of coal-ash and phosphogypsum. In the leaching test, all materials had lower heavy metal concentration than the regulated threshold values. The column test and the review of related regulations were carried out to determine the optimum mixing ratio(OMR) and OMR was soil(4):coal-ash(1): phosphogypsum(1) on the volume base, which was applied to field model test. Field model tests were continued from February to June, 2004 in the soil box that was constructed with cement block. It was verified that coal-ash and phospogypsum mixed with soil was safe environmentally and the mixture of both wastes could improve the water retention capacity of cover materials.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.152-152
• /
• 2019

This study demonstrates a novel approach of remotely sensed estimates of stream flow at fifteen hydrological station in the Han River Basin, Korea. Multi-temporal data of the European Space Agency's Sentinel-1 SAR satellite from 19 January, 2015 to 25 August, 2018 is used to develop and validate the flow estimation model for each station. The flow estimation model is based on a power law relationship established between the remotely sensed surface area of water at a selected reach of the stream and the observed discharge. The satellite images were pre-processed for thermal noise, radiometric, speckle and terrain correction. The difference in SAR image brightness caused by the differences in SAR satellite look angle and atmospheric condition are corrected using the histogram matching technique. Selective area filtering is applied to identify the extent of the selected stream reach where the change in water surface area is highly sensitive to the change in stream discharge. Following this, an iterative procedure called the Optimum Threshold Classification Algorithm (OTC) is applied to the multi-temporal selective areas to extract a series of water surface areas. It is observed that the extracted water surface area and the stream discharge are related by the power law equation. A strong correlation coefficient ranging from 0.68 to 0.98 (mean=0.89) was observed for thirteen hydrological stations, while at two stations the relationship was highly affected by the hydraulic structures such as dam. It is further identified that the availability of remotely sensed data for a range of discharge conditions and the geometric properties of the selected stream reach such as the stream width and side slope influence the accuracy of the flow estimation model.

• Journal of Yeungnam Medical Science
• /
• v.35 no.2
• /
• pp.192-198
• /
• 2018

Background: Chronic inflammation can lower the seizure threshold and have influence on epileptogenesis. The components of red ginseng (RG) have anti-inflammatory effects. The abundance of peripherally derived immune cells in resected epileptic tissue suggests that the immune system is a potential target for anti-epileptogenic therapies. The present study used continuous electroencephalography (EEG) to evaluate the therapeutic efficacy of RG in intrahippocampal kainic acid (IHKA) animal model of temporal lobe epilepsy. Methods: Prolonged status epilepticus (SE) was induced in 7-week-old C57BL/6J mice via stereotaxic injection of kainic acid (KA, 150 nL; 1 mg/mL) into the right CA3/dorsal hippocampus. The animals were implanted electrodes and monitored for spontaneous seizures. Following the IHKA injections, one group received treatments of RG (250 mg/kg/day) for 4 weeks (RG group, n=7) while another group received valproic acid (VPA, 30 mg/kg/day) (VPA group, n=7). Laboratory findings and pathological results were assessed at D29 and continuous (24 h/week) EEG monitoring was used to evaluate high-voltage sharp waves on D7, D14, D21, and D28. Results: At D29, there were no differences between the groups in liver function test but RG group had higher blood urea nitrogen levels. Immunohistochemistry analyses revealed that RG reduced the infiltration of immune cells into the brain and EEG analyses showed that it had anticonvulsant effects. Conclusion: Repeated treatments with RG after IHKA-induced SE decreased immune cell infiltration into the brain and resulted in a marked decrease in electrographic seizures. RG had anticonvulsant effects that were similar to those of VPA without serious side effects.

• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.8
• /
• pp.1077-1083
• /
• 2019

Objective: Sow longevity is important for efficient and profitable pig farming. Recently, there has been an increasing interest in social genetic effect (SGE) of pigs on stress-tolerance and behavior. The present study aimed to estimate genetic correlations among average daily gain (ADG), stayability (STAY), and number of piglets born alive at the first parity (NBA1) in Korean Yorkshire pigs, using a model including SGE. Methods: The phenotypic records of ADG and reproductive traits of 33,120 and 11,654 pigs, respectively, were evaluated. The variances and (co) variances of the studied traits were estimated by a multi-trait animal model applying the Bayesian with linear-threshold models using Gibbs sampling. Results: The direct and SGEs on ADG had a significantly negative (-0.30) and neutral (0.04) genetic relationship with STAY, respectively. In addition, the genetic correlation between the social effects on ADG and NBA1 tended to be positive (0.27), unlike the direct effects (-0.04). The genetic correlation of the total effect on ADG with that of STAY was negative (-0.23) but non-significant, owing to the social effect. Conclusion: These results suggested that total genetic effect on growth in the SGE model might reduce the negative effect on sow longevity because of the growth potential of pigs. We recommend including social effects as selection criteria in breeding programs to obtain satisfactory genetic changes in both growth and longevity.

• Journal of Veterinary Science
• /
• v.24 no.3
• /
• pp.23.1-23.16
• /
• 2023

Background: Irritable bowel syndrome (IBS) is a functional bowel disorder (FBD). Objectives: To assess the therapeutic effects of paeoniflorin (PF) on IBS in rats. Method: Sixty male Sprague-Dawley rats were randomly divided into normal, model, positive drug, low-dose PF, medium-dose PF and high-dose PF groups (n = 10). After gavage for 2 consecutive weeks, the effect of PF on abdominal pain symptoms was assessed based on the abdominal withdrawal reflex (AWR) score, fecal water content and pathological changes in colon tissues. D-lactate, interleukin-1β (IL-1β), transforming growth factor-β (TGF-β) and tumor necrosis factor-α (TNF-α) were detected by enzyme-linked immunosorbent assay, and phosphorylated nuclear factor kappa B (p-NF-κB) p65 was detected by Western blotting. The abundance and diversity changes of intestinal flora were explored using 16S ribosomal RNA sequencing. Result: In PF groups, the mucosal morphology of colon tissues was intact, and the glands were arranged neatly and structured clearly, without obvious inflammatory cell infiltration. Compared with the model group, PF groups had significantly elevated pain threshold, and mRNA and protein levels of zonula occludens-1 (ZO-1) and occludin, decreased AWR score at 20 mmHg pressure, fecal water content, mRNA levels of IL-1β, TGF-β, and TNF-α, protein level of p-NF-κB p65 and level of serum D-lactate, and reduced levels of serum IL-1β, TGF-β, and TNF-α (p < 0.05, p < 0.01). PF groups had higher abundance of Lactobacillus, Akkermansia, Alistipes, and Bacteroides, but lower abundance of Desulfovibrio, Parasutterella, and Enterococcus than those of the model group. Conclusions: PF exerts therapeutic effects on IBS in rats probably by regulating the intestinal flora, and then up-regulating the expressions of ZO-1 and occludin in colon tissue while down-regulating the levels of IL-1β, TGF-β, TNF-α, D-lactate and p-NF-κB p65.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.36 no.5
• /
• pp.315-321
• /
• 2023

Long-span bridges are flexible structures with low natural frequencies and damping ratios, making them susceptible to vibrational serviceability problems. However, the current design guideline of South Korea assumes a uniform threshold of wind speed or vibrational amplitude to assess the occurrence of harmful vibrations, potentially overlooking the complex vibrational patterns observed in long-span bridges. In this study, we propose a pointwise vortex-induced vibration (VIV) detection method using a deep-learning-based signalsegmentation model. Departing from conventional supervised methods of data acquisition and manual labeling, we synthesize training data by generating sinusoidal waves with an envelope to accurately represent VIV. A Fourier synchrosqueezed transform is leveraged to extract time-frequency features, which serve as input data for training a bidirectional long short-term memory model. The effectiveness of the model trained on synthetic VIV data is demonstrated through a comparison with its counterpart trained on manually labeled real datasets from an actual cable-supported bridge.

• Journal of IKEEE
• /
• v.28 no.1
• /
• pp.6-13
• /
• 2024

This paper proposes an approach to detect abnormal data in automotive controller area network (CAN) using an unsupervised learning model, i.e. autoencoder and Gaussian kernel density estimation function. The proposed autoencoder model is trained with only message ID of CAN data frames. Afterwards, by employing the Gaussian kernel density estimation function, it effectively detects abnormal data based on the trained model characterized by the optimally determined number of frames and a loss threshold. It was verified and evaluated using four types of attack data, i.e. DoS attacks, gear spoofing attacks, RPM spoofing attacks, and fuzzy attacks. Compared with conventional unsupervised learning-based models, it has achieved over 99% detection performance across all evaluation metrics.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.127-137
• /
• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Korea Water Resources Association
• /
• v.33 no.2
• /
• pp.247-262
• /
• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.