• Journal of Ginseng Research
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• v.46 no.4
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• pp.515-525
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• 2022

Background: The incidence of ischemic cerebrovascular disease is increasing in recent years and has been one of the leading causes of neurological dysfunction and death. Ginsenoside Rg1 has been found to protect against neuronal damage in many neurodegenerative diseases. However, the effect and mechanism by which Rg1 protects against cerebral ischemia-reperfusion injury (CIRI) are not fully understood. Here, we report the neuroprotective effects of Rg1 treatment on CIRI and its possible mechanisms in mice. Methods: A bilateral common carotid artery ligation was used to establish a chronic CIRI model in mice. HT22 cells were treated with Rg1 after OGD/R to study its effect on [Ca²⁺]_i. The open-field test and poleclimbing experiment were used to detect behavioral injury. The laser speckle blood flowmeter was used to measure brain blood flow. The Nissl and H&E staining were used to examine the neuronal damage. The Western blotting was used to examine MAP2, PSD95, Tau, p-Tau, NOX2, PLC, p-PLC, CN, NFAT1, and NLRP1 expression. Calcium imaging was used to test the level of [Ca²⁺]_i. Results: Rg1 treatment significantly improved cerebral blood flow, locomotion, and limb coordination, reduced ROS production, increased MAP2 and PSD95 expression, and decreased p-Tau, NOX2, p-PLC, CN, NFAT1, and NLRP1 expression. Calcium imaging results showed that Rg1 could inhibit calcium overload and resist the imbalance of calcium homeostasis after OGD/R in HT22 cells. Conclusion: Rg1 plays a neuroprotective role in attenuating CIRI by inhibiting oxidative stress, calcium overload, and neuroinflammation.

• Journal of Mechanical Science and Technology
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• v.16 no.7
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• pp.1009-1018
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• 2002

The Representative Interactive Flamelet (RIF) concept has been applied to numerically simulate the combustion processes and pollutant formation in the direct injection diesel engine. Due to the ability for interactively describing the transient behaviors of local flame structures with CFD solver, the RIF concept has the capabilities to predict the auto-ignition and subsequent flame propagation in the diesel engine combustion chamber as well as to effectively account for the detailed mechanisms of soot formation, NOx formation including thermal NO path, prompt and nitrous 70x formation, and reburning process. Special emphasis is given to the turbulent combustion model which properly accounts for vaporization effects on the mixture fraction fluctuations and the pdf model. The results of numerical modeling using the RIF concept are compared with experimental data and with numerical results of the commonly applied procedure which the low-temperature and high-temperature oxidation processes are represented by the Shell ignition model and the eddy dissipation model, respectively. Numerical results indicate that the RIF approach including the vaporization effect on turbulent spray combustion process successfully predicts the ignition delay time and location as well as the pollutant formation.

• 연구논문집
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• s.23
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• pp.93-107
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• 1993

The chemistry and performance characteristics of the EBDS process have been introduced, in which experimental results from laboratory, test plant, and pilot plant studies agree very well and can be understood from detailed kinetic models. The parametric dependencies of the NOx and $SO_2$, removal yields on the input conditions have been discussed and formulated quantitatively. The process is best suited for flue gas with high $SO_2$, loadings. The operation conditions, such as dose, ammonia, and water additions, can be adjusted fast upon load changes. The process works waste water free and the major product is a mixture of ammonium nitrate and sulfate that can be used as fertilizer. The up-date results show that the EBDS technology is safe and competitive with other already well-established technologies. Due to these interesting features, the electron beam process has gained much international recognition. Demonstration units of 100MW have been proposed in the United States and Japan. Further pilot plants are under construction in Poland and China, countries that make abundant use of highsulfur coal. Additional research activities are under way to further improve the energy efficiency of process, and accelerator prices have been decreasing during the past 10 years. So the EBDS process has a good chance to start a new generation of emission-control technology.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• Journal of Life Science
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• v.32 no.6
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• pp.421-429
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• 2022

The expression of interleukin-1α (IL-1α) is elevated in monocytic cells, such as monocytes and macro-phages, within atherosclerotic arteries, yet the cellular molecules involved in cytokine upregulation remain unclear. Because peptidoglycan (PG), a major component of gram-positive bacterial cell walls, is detected within the inflammatory cell-rich regions of atheromatous plaques, it was investigated if PG contributes to IL-1α expression in monocytes/macrophages. Exposure of THP-1 monocytic cells to PG resulted in elevated levels of IL-1α gene transcripts and increased secretion of IL-1α protein. The transcription and secretion of IL-1α were abrogated by OxPAPC, an inhibitor of TLR2/4, but not by polymyxin B that inhibits lipopolysaccharide-induced TLR4 activation. To understand the molecular mechanisms of the inflammatory responses due to bacterial pathogen-associated molecular patterns (PAMPs) in diseased arteries, we attempted to determine the cellular factors involved in the PG-induced upregulation of IL-1α expression. Pharmacological inhibition of cell signaling pathways with LY294002 (a PI3K inhibitor), Akti IV (an inhibitor of Akt activation), rapamycin (an mTOR inhibitor), U0126 (a MEK inhibitor), SB202190 (a p38 MAPK inhibitor), SP6001250 (a JNK inhibitor), and DPI (a NOX inhibitor) also significantly attenuated the PG-mediated expression of IL-1α. These results suggest that PG induces the monocytic or macrophagic expression of IL-1α, thereby contributing to vascular inflammation, via multiple signaling molecules, including TLR2, PI3K/Akt/mTOR, and MAPKs.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• v.y2005m4
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• pp.274-280
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• 2005

Experimental investigations were carried out in an atmospheric pressure, optically accessible and laboratory-scale dump combustor. The objective of this study is to obtain the phase-resolved gas temperatures at different phases of the oscillating pressure cycle during unstable combustion. To see the effect of incomplete fuel-air mixing on phase-resolved temperature characteristics, CARS temperature measurements were performed. Results including phase-resolved averaged temperature, normalized standard deviation and temperature probability distribution functions (PDFs) were provided in this paper. It could be found that the profile of mean temperature showed the in-phase relationship with pressure cycle. Temperature PDFs give an insight on the flame behavior as well as NOx emission characteristics. These results would be expected to play an important role in better understanding of driving mechanisms and thermo-acoustic interactions.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.3
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• pp.106-112
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• 2012

Since air pollution problem by emissions from automotive vehicles has become social issues, lean-burn gasoline direct injection (GDI) engine is focused as an alternative to meet the requirement of reinforced emission regulation and improved fuel consumption. Spray-guided type DI combustion is promising technology, which characterized by the centrally mounted injector and closely positioned spark plug, since stable lean combustion can be realized even at ultra-lean mixture condition. In the present study, the effect of multi-ignition with developed charge coil on combustion and emission characteristics was investigated in optical accessible single cylinder engine. In order to fully understand the in-cylinder phenomena and the mechanisms of emission production, optical diagnostics, such as flame visualization was also carried out at frequently using operating condition. Multi-ignition is effective to improve fuel economy but increase NOx emission at flammability limit.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.45-51
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• 2006

In this paper, we propose a new architecture of Information Granulation based genetically optimized Self-Organizing Fuzzy Polynomial Neural Networks (IG_gSOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially information granulation and genetic algorithms. The proposed IG_gSOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). To evaluate the performance of the IG_gSOFPNN, the model is experimented with using two time series data(gas furnace process and NOx process data).