• Title/Summary/Keyword: NOx mechanisms

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Effect of Intake Flow Control Method on Part Load Performance in SI Engine(1) - Comparison of Throttling and Masking (스파크점화기관에서 흡기제어 방식이 부분부하 성능에 미치는 영향(1) - 스로틀링과 마스킹의 비교)

  • Kang, Min Gyun;Ohm, Inyong
    • Transactions of the Korean Society of Automotive Engineers
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    • v.22 no.2
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    • pp.156-165
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    • 2014
  • This paper is the first investigation on the effect of flow control methods on the part load performance in a spark ignition engine. For comparison of the methods, two control devices, port throttling and masking, were applied to a conventional engine without any design change of the intake port. Steady flow evaluation shows that steady flow rates per unit opening area and swirl ratio are very low compared with the port throttling and saturated from mid-stage valve lift, however, swirl increases slightly as the lift is higher in case of 1/4 masking control. In the part load performance, the effect of simple port throttling on lean misfire limit expansion is limited and insufficient; on the other hand a masking improves the limit considerably without any port modification for increasing swirl. Also the results show that the intake flow control improves the combustion with following two mechanisms: stratification induced by the combination of the flow pattern and the fuel injection timing attribute to ignition ability and the intensified flow ensure fast burn. In addition fuel consumption reduces under the flow controls and the reduction rate is different according to the operation conditions and control methods. At the Stoichiometric and/or low speed and low load the throttling method is more advantageous; however vice versa at lean and high load condition. Finally, the throttling is more efficient for HC reduction than masking, on the other side the NOx emissions increase under the masking and decrease under the port throttling compared with conventional port scheme.

Self-Organizing Fuzzy Polynomial Neural Networks by Means of IG-based Consecutive Optimization : Design and Analysis (정보 입자기반 연속전인 최적화를 통한 자기구성 퍼지 다항식 뉴럴네트워크 : 설계와 해석)

  • Park, Ho-Sung;Oh, Sung-Kwun
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.55 no.6
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    • pp.264-273
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    • 2006
  • In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, 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 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). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

Evolutionary Design of Radial Basis Function-based Polynomial Neural Network with the aid of Information Granulation (정보 입자화를 통한 방사형 기저 함수 기반 다항식 신경 회로망의 진화론적 설계)

  • Park, Ho-Sung;Jin, Yong-Ha;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.4
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    • pp.862-870
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    • 2011
  • In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

Design of Data-centroid Radial Basis Function Neural Network with Extended Polynomial Type and Its Optimization (데이터 중심 다항식 확장형 RBF 신경회로망의 설계 및 최적화)

  • Oh, Sung-Kwun;Kim, Young-Hoon;Park, Ho-Sung;Kim, Jeong-Tae
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.3
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    • pp.639-647
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    • 2011
  • In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

The protective effects of polyphenol-rich black chokeberry against oxidative stress and inflammation (폴리페놀 함유 블랙 초크베리의 산화적 스트레스 및 염증에 대한 보호 효과)

  • Jeon, Sohyeon;Kim, Bohkyung
    • Korean Journal of Food Science and Technology
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    • v.52 no.2
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    • pp.138-143
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    • 2020
  • Black chokeberry (Aronia melanocarpa) has been suggested to exert antioxidant and anti-inflammatory effects due to its high polyphenol content. However, the mechanisms underlying the effects of black chokeberry on the alterations of nuclear factor E2-related factor 2 (NRF2) and nuclear factor κB (NF-κB) in macrophages have not been thoroughly studied. In this study, we investigated the protective effects of polyphenol-rich black chokeberry extract (CBE) against lipopolysaccharide (LPS)-induced oxidative stress and inflammation in RAW 264.7 macrophages. CBE significantly attenuated the increase of cellular reactive oxygen species (ROS) levels and the nuclear translocation of NRF-2 in LPS-stimulated macrophages. The mRNA abundances of Nrf2 and its downstream antioxidant genes were significantly decreased in LPS-stimulated macrophages. The LPS-induced mRNA expression of proinflammatory cytokines was significantly inhibited by reducing the nuclear translocation of NF-κB by CBE. These data suggest that black chokeberry may be used for the prevention of oxidative stress and inflammation-associated disease.

Effect of Paeoniae Radix Alba on a thioacetamide induced liver fibrosis mice model (Thioacetamide로 유발된 간섬유증 동물 모델에서 백작약이 미치는 효능)

  • Lee, Se Hui;Lee, Jin A;Shin, Mi-Rae;Seo, Bu-Il;Roh, Seong-Soo
    • Korean Journal of Food Science and Technology
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    • v.53 no.5
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    • pp.544-552
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    • 2021
  • This study investigated the anti-fibrotic and antioxidant effects of Paeonia Radix Alba water extract (PR) on thioacetamide (TAA)-induced liver fibrosis in a mouse model and its underlying mechanisms. Liver fibrosis was induced by intraperitoneal injection of TAA (three times a week) for 8 weeks. Furthermore, silymarin (50 mg/kg body weight) and PR (200 mg/kg body weight) were administered for 8 weeks. PR treatment downregulated aspartate aminotransferase (AST), alanine aminotransferase (ALT), ammonia, and myeloperoxidase levels. Moreover, PR treatment downregulated NOX2 and p47phox and upregulated antioxidant enzymes by activating the Nrf2/Keap1 signaling pathway. Furthermore, PR inhibited the factors associated with fibrosis, such as α-SMA and collagen I. AMPK/SIRT1 was upregulated by PR treatment. Overall, these results suggest that PR attenuates liver fibrosis by regulating the Nrf2/Keap1 and AMPK/SIRT1/NF-κB signaling pathways through the inhibition of oxidative stress. Hence, PR has potential as a remedy for preventing and treating liver fibrosis.