• Journal of Physiology & Pathology in Korean Medicine
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• v.35 no.6
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• pp.219-227
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• 2021

The purpose of this study is to analyze the side effects (ADR) of Traditional Chinese Medicine (TCM) injections by age, injection type, symptoms, and causes, and to find preventive solutions for ADR. For the ADR of TCM injection data collected during the search period from January 1, 2010 to December 31, 2020, the correlation between each section was analyzed by subdividing it into age, injection type, symptoms and causes. CNKI, PubMed, and EMBASE were used to collect the clinical data. 'Chinese herbal injection', 'Traditional Chinese Medicine injection', 'Chinese herbal injection side effect', 'Chinese herbal injection adverse drug reaction' were used for the keyword from the database. All data were collected mainly for TCM injection and the causes of ADR due to TCM injection. However, data not related to the relevant study or TCM injection were excluded from this study. Among a total of 941 studies collected during the search period from January 1, 2010 to December 31, 2020, a total of 10 studies were selected for final analysis. In 1462 clinical data sets, ADR by gender was higher in males than females. By age, 41 to 60 years were the most common. The incidence of ADR by injection type was highest in the blood regulating injection type. Data analysis showed Xueshuantong injection had the highest ADR. Among the symptoms of ADR, skin diseases were the most common. The most common cause of ADR was the unreasonable use of drugs. In China, for ADR management, the use of TCM injections is recommended according to the basic principles for the clinical use of TCM injections established by the Chinese government. In this study, we analyzed the current status and causes of ADR in TCM injections, and found a preventive solution. It is expected that it can be used as basic data to increase the usability of pharmacopuncture and herbal medicines in Korea in the future.

• Journal of ILASS-Korea
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• v.18 no.1
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• pp.61-65
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• 2013

In case of GDI engine, shape of injected fuel and injection mass are one of the most important factors for good fuel efficiency and power. But it should be too inefficient and difficult to acquire injection mass data by experiment because condition in engine vary with temperature, pressure, and so on. So, this paper suggests the AMESim (Advanced Modeling Environment for Simulation of Engineering Systems) as simulation program to calculate injection mass. For both simulation and experiment, n-heptane is used as fuel. In AMESim, I modeled the GDI injector and simulated several cases. In experiment, I acquired the injection mass using Bosch method to apply ambient pressure. The AMESim show reasonable result in comparison with experimental data especially at injection pressure 15 MPa. Other conditions are also in good accord with experimental data but error is a little bit large because the injection mass is so low.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.958-962
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• 2003

We acquired data of injection molding machine in operation and stored the data in database. We acquired the data of injection molding machine for fault detection and diagnosis (FDD) continuously and estimated the fault results with a fuzzy algorithm. Many of FDD are applied to a huge system, nuclear power plant and a computer numerical control(CNC) machine for processing machinery. But, the research of FDD is rare in injection molding machine compare with computer numerical control machine. We appraise the accuracy of the FDD and the limit of the application to the injection molding machine. We construct the fault detection and diagnosis system based on fuzzy algorithm in the injection molding machine. Data of operating injection molding machine are acquired in order to improve the reliability of detection and diagnosis.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.8
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• pp.1101-1110
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• 1998

Computational investigation was conducted to examine the performance of a high pressure common-rail fuel injection system which is used to power a passenger car direct injection (Dl) diesel engine. The pipe flows were modeled by one dimensional wave equation and solved by implicit FDM Each volume of injector was considered as chambers with orifice nozzle in connections. These simulation results were compared with the experimental data of Ganser Hydromag. The comparison of needle life and rate of injection between simulation data and experimental data showed quite a good agreement Different shape of injection rate can be made by adjusting the size of inlet orifice and exit orifice in the piston chamber The pilot injection was accomplished by adjusting command signal.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.115-120
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• 2010

The reduction rate of fuel consumption by showing the fuel injection data for driver was measured in this study. The fuel injection data are composed of injection period, real time fuel economy and average fuel economy. The fuel consumption was measured by processing the voltage signal of injector and driven distance by GPS sensor. The fuel consumption was reduced by driving more carefully, i.e driving more steady without sudden acceleration and deceleration watching these fuel injection data. The reduction rate was up to 37% and the rate increased as the driver is customed to this driving pattern.

• Journal of the korean Society of Automotive Engineers
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• v.14 no.5
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• pp.54-60
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• 1992

This paper deals with the results of injection characteristics and the influence parameters upon the fuel injection performance of the inline injection system in a diesel engine. In this study, the characteristics of the injection rate, the injection pressure and the injection duration have been investigated by changing the injection parameters. The predicted results and injection performance are compared to the measured data from the injection test system.

• Journal of ILASS-Korea
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• v.17 no.2
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• pp.77-85
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• 2012

The CFD simulation of diesel spray tip penetrations were compared with 0-D simulation for experimental data obtained with common rail injection system. The simulated four injection patterns include single, pilot and split injections. The CFD simulation of the spray penetration over these injection patterns was performed using the KIVA-3V code, which was implemented with both the standard KIVA spray and original gas jet sub-models. 0-D simulation of the spray tip penetration with time-varying injection profiles was formulated based on the effective injection velocity concept as an extension of steady gas jet theory. Both the CFD simulation of the spray tip penetration with the standard KIVA spray model and 0-D simulation matched better with the experimental data than the results of the gas jet model for the entire fuel injection patterns.

• Design & Manufacturing
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• v.13 no.3
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• pp.53-58
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• 2019

This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.

• Design & Manufacturing
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• v.15 no.4
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.04a
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• pp.27-32
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• 2003

The wavelike flow mark phenomenon is one of the surface defects that can arise during the injection stage of the injection molding process. We have performed a numerical analysis using a finite element method for the injection molding to verify the validity of 'Go-over' hypothesis. Also, we have compared the results of numerical analysis with available experimental data. Numerical analysis results of the flow marks are qualitatively in good agreement with experimental data of reference, but are quantitatively deviated from experimental data in a consistent manner. A parametric study has been performed to examine the correlative effects of various injection molding processing parameters and material properties on the flow mark size.