• Archives of Pharmacal Research
• /
• v.13 no.3
• /
• pp.227-232
• /
• 1990

A Physiologically based pharmacokinetic model was used to describe the distribition and elimination of cefriazone in the rat. To validate the practical application of the model, the effect of cffeine on the model was also examined. The model consisted of eleven compartments representing the major sites for ceftriaxone distribution including carcass which served as a residual compartment. Elimination was represented by renal and hepatic (metabolic biliary )excretion with GI secretion and re-absorption. The drug concentrations in most of the tissues were simulated using flow limited equations while brain levels were simulated using membrane limited passive diffusion distribution. The experimental data were obtained by averaging the concentration of drug in the plasma and tissues of five rats after i. v. injection of cefriazone 100 mg/kg without and with caffeine 20 mg/kg. The data for the amount of ceftriazone excreted in urine and gut contents were used to apportion total body clearance. HPLC with UV detection was used for the assay with 0.1-0.2 $\mu$g/ml sensitivity. The great majority of drug concentrations with and without caffeine show reasonably good agreements to the simulation results within 20%. The effect of caffeine on renal and hepatic clearances was apparent with 18.8% and 18.6% increase in the model values, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3668-3684
• /
• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• Journal of Information Processing Systems
• /
• v.17 no.2
• /
• pp.337-351
• /
• 2021

Automatically recognizing facial expressions in video sequences is a challenging task because there is little direct correlation between facial features and subjective emotions in video. To overcome the problem, a video facial expression recognition method using spatiotemporal recurrent neural network and feature fusion is proposed. Firstly, the video is preprocessed. Then, the double-layer cascade structure is used to detect a face in a video image. In addition, two deep convolutional neural networks are used to extract the time-domain and airspace facial features in the video. The spatial convolutional neural network is used to extract the spatial information features from each frame of the static expression images in the video. The temporal convolutional neural network is used to extract the dynamic information features from the optical flow information from multiple frames of expression images in the video. A multiplication fusion is performed with the spatiotemporal features learned by the two deep convolutional neural networks. Finally, the fused features are input to the support vector machine to realize the facial expression classification task. The experimental results on cNTERFACE, RML, and AFEW6.0 datasets show that the recognition rates obtained by the proposed method are as high as 88.67%, 70.32%, and 63.84%, respectively. Comparative experiments show that the proposed method obtains higher recognition accuracy than other recently reported methods.

• Proceedings of the Plant Resources Society of Korea Conference
• /
• 2019.04a
• /
• pp.100-100
• /
• 2019

This study attempted to establish a High Performance Liquid Chromatography (HPLC) analysis method for the determination of (-)-epicatechin gallate as a part of the quality control for the development of functional cosmetic materials from Penthorum chinense Pursh extracts. HPLC was performed on a Unison US-C18 column ($4.6{\times}250mm$, $5{\mu}m$) with a gradient elution of 0.05% (v/v) trifluoroacetic acid (TFA) and methyl alcohol at a flow rate of 1.0 mL/min at $30^{\circ}C$. The analyte was detected at 280 nm. The HPLC method was performed in accordance with the International Conference on Harmonization (ICH) guideline (version 4, 2005) of analytical procedures with respect to specificity, precision, accuracy, and linearity. The limits of detection and quantitation were 0.11 and 0.33 mg/mL, respectively. Calibration curves showed good linearity (r2 > 0.9999), and the precision of analysis was satisfied (less than 0.6%). Recoveries of quantified compounds ranged from 99.51 to 101.92%. This result indicates that the established HPLC method is very useful for the determination of marker compound in P. chinense Pursh extracts.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.3037-3054
• /
• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• Geomechanics and Engineering
• /
• v.25 no.4
• /
• pp.295-302
• /
• 2021

Cross-borehole electrical resistivity tomography (ERT) is an effective groundwater detection tool in geophysical investigations. In this paper, an artificial water injection test was conducted on a small clay sample, where the high-resolution cross-borehole ERT was used to investigate the moisture migration law over time. The moisture migration path can be two-dimensionally imaged based on the relationship between resistivity and saturation. The hydraulic conductivity was estimated, and the magnitude ranged from 10^-11 m/s to 10^-9 m/s according to the comparison between the simulation flow and the saturation distribution inferred from ERT. The results indicate that cross-borehole ERT could help determine the resistivity distribution of small size clay samples. Finally, the cross-borehole ERT technique has been applied to investigate the self-sealing characteristics of clay.

• Journal of Korea Multimedia Society
• /
• v.24 no.1
• /
• pp.21-29
• /
• 2021

With the recent increase in the number of households raising pets, various engineering studies have been underway for pets. The final purpose of this study is to automatically generate situation-sensitive captions that can express implicit intentions based on the behavior and sound of cats by embedding the already mature behavioral detection technology of pets as basic element technology in the video capturing research. As a pilot project to this end, this paper proposes a high-level capturing system using optical-flow, RGB, and sound information of cat videos. That is, the proposed system uses video datasets collected in an actual breeding environment to extract feature vectors from the video and sound, then through hierarchical LSTM encoder and decoder, to identify the cat's behavior and its implicit intentions, and to perform learning to create context-sensitive captions. The performance of the proposed system was verified experimentally by utilizing video data collected in the environment where actual cats are raised.

• Journal of Sensor Science and Technology
• /
• v.30 no.1
• /
• pp.25-29
• /
• 2021

In this study, we successfully developed a highly reliable ultrasonic sediment sensor to detect the sediment levels in sewer pipes in harsh environments. The ultrasonic transducer employed in the ultrasonic sediment sensor was designed so as to possess a simple structure. The developed sensor was carefully optimized by simulating the electromechanical characteristics, radiated sound wave pressures, and directivity via finite element analysis. It was also designed to possess a simple mounting structure minimizing the flow disturbance in a 400-mm sewer pipe; additionally, eight ultrasonic transducers were arranged in a four-channel mode, allowing for measurement of the sediment height in five easy steps. Through experimental evaluations, we verified the performance of the ultrasonic sediment-level sensor and its industrial applicability. The results suggested that although the precision value was notably low at 15 mm, the sediment detection performance was adequate; therefore, the developed sensor can potentially be used in industrial applications.

• Journal of the Korean Society of Safety
• /
• v.36 no.4
• /
• pp.1-11
• /
• 2021

Piping systems comprising pumps and valves are essential in the power plant, oil, and defense industry. Their purpose includes a stable supply of the working fluid or ensuring the target system's safe operation. However, piping system accidents due to leakage of toxic substances, explosions, and natural disasters are prevalent In addition, with the limited maintenance personnel, it becomes difficult to detect, isolate, and reconfigure the damage of the piping system and recover the unaffected area. An autonomous recovery piping system can play a vital role under such circumstances. The autonomous recovery algorithms for the piping system can be divided into low-pressure control algorithms, hydraulic resistance control algorithms, and flow inventory control algorithms. All three methods include autonomous opening/closing logic to isolate damaged areas and recovery the unaffected area of piping systems. However, because each algorithm has its strength and weakness, appropriate application considering the overall design, vital components, and operating conditions is crucial. In this regard, preliminary research on algorithm's working principle, its design procedures, and expected damage scenarios should be accomplished. This study examines the characteristics of algorithms, the design procedure, and working logic. Advantages and disadvantages are also analyzed through simulation results for a simplified piping system.

• Natural Product Sciences
• /
• v.27 no.4
• /
• pp.264-273
• /
• 2021

Simultaneous quantification of multiple marker compounds in herbal medicine by high performance liquid chromatography (HPLC) analysis is still a challenge due to the complexity in various parameters to be considered and co-existing multi-components. As a case study, a reliable HPLC method for simultaneous quantification of paeoniflorin from Paeoniae Radix and decursin from Angelicae Gigantis Radix in various commercial herbal medicine was developed based on analytical quality by design (AQbD) strategy. As a first step, risk assessment was performed to select the critical method parameters (CMPs) which were decided as organic mobile phase ratio and column oven temperature. In order to evaluate the effect of the CMPs on critical method attributes (CMAs) of peak resolution and tailing, central composite design (CCD) was employed. The final chromatographic conditions were optimized as follows: column- C₁₈, 4.6 × 250 mm, 5 ㎛ particle size; mobile phase- A: acetonitrile, B: 0.1% acetic acid water; detection wavelength- 235 nm for paeoniflorin, 325 nm for decursin; column oven temperature- 25℃; flow rate- 1.0 mL/min; gradient mobile phase system as Time (min) : % A, 0:14, 25:14, 30:50, 60:50, 61:100, 65:100, 66:14, 75:14. The method was successfully validated according to the International Conference on Harmonization (ICH) guidelines and piloted for ten commercial herbal medicines.