Modified automotive organic friction materials through infiltration of liquid carbon precursors

Kuo‑Jung Lee,Ting‑Yu Wu,Hsun‑Yu Lin,Huy‑Zu Cheng,Chih‑Feng Wang 한국탄소학회 2019 Carbon Letters Vol.29 No.4

This research combines the liquid carbon precursor infiltration process for carbon/carbon composites with the fabrication procedure for organic, carbon-matrix friction materials in automotive. In the densification process, different liquid carbon precursors and numbers of densification cycle are adopted to investigate the influence on physical and mechanical properties, microstructure and tribological behavior. Experimental results indicate that the infiltration of liquid carbon precursors could improve the physical, mechanical properties and tribological performances of organic friction materials. The open porosity decreases with the number of densification cycle. Both bulk density and hardness increase with the number of densification cycle. The resin-based specimens show higher hardness and lower open porosity than those of the pitch-based specimens after each densification cycle. The tribological measurement of specimens with different carbon precursors shows that the pitch-based specimen shows lower and more stable friction coefficients and exhibits lower weight losses in comparison with other carbon precursors. Morphological observations show that a large area of smooth lubricative film was easily presented on the worn surfaces of the pitch-based specimens, whereas it was seldom observed on the worn surfaces of the preform specimen and resin-based specimens.

The capsular polysaccharide of pyogenic liver abscessKlebsiella pneumoniae induces maturation and inflammatory response of human monocyte-derived dendritic cells

Kuo-Feng Hua,Chia-Yang Li,Feng-Ling Yang,Shih-Hsiung Wu 한국당과학회 2012 한국당과학회 학술대회 Vol.2012 No.1

The capsular polysaccharide (CPS) of pyogenic liver abscessKlebsiella pneumoniaeconsists of repeating units of the trisaccharide (→3)-□-D-Glc-(1→4)-[2,3-(S)-pyruvate]-□-D-GlcA-(1→4)-□-L-Fuc-(1→) and has the unusual feature of extensive pyruvation of glucuronic acid and acetylation of C2-OH or C3-OH of fucose. The present study investigates how CPS activates human monocyte-derived dendritic cells (DCs). Our experimental results show that CPS activates DCs by (1) increasing the expression of CD11c, CD40, CD80, CD83, CD86, and MHC-II (2) increasing the production of TNF-□, IL-1, IL-6, and IL-12p70 (3) increasing DC-elicited allogeneic T-cell proliferation, and (4) increasing the DC-driven Th1 response. In addition, CPS activates DCs through TLR4 and the pyruvation and the acetylation of CPS are important for its cytokine induction activity. Further, our results show that CPS activates TNF-□ and IL-6 secretion through JNK1/2, p38, NF-B,- PKC and ROS pathways in DCs.

An Exploratory Study of Cyber Group Development Process

Feng Yang Kuo,Chia Ping Yu 한국경영정보학회 2001 한국경영정보학회 학술대회논문집 Vol.2001 No.-

A Study of Web Searching Behaviors - Comparing the Effort - Accuracy Tradeoff Model with the Social Cognitive Theory

Feng Yang Kuo,Tsai Hsin Chu,Hong Ssu Hsieh 한국경영정보학회 2001 한국경영정보학회 학술대회논문집 Vol.2001 No.-

Ginsenoside compound K reduces the progression of Huntington's disease via the inhibition of oxidative stress and overactivation of the ATM/AMPK pathway

Kuo-Feng Hua,A-Ching Chao,Ting-Yu Lin,Wan-Tze Chen,Yu-Chieh Lee,Wan-Han Hsu,Sheau-Long Lee,Hsin-Min Wang,Ding-I. Yang,Tz-Chuen Ju 고려인삼학회 2022 Journal of Ginseng Research Vol.46 No.4

Background: Huntington's disease (HD) is a neurodegenerative disorder caused by the expansion oftrinucleotide CAG repeat in the Huntingtin (Htt) gene. The major pathogenic pathways underlying HDinvolve the impairment of cellular energy homeostasis and DNA damage in the brain. The protein kinaseataxia-telangiectasia mutated (ATM) is an important regulator of the DNA damage response. ATM isinvolved in the phosphorylation of AMP-activated protein kinase (AMPK), suggesting that AMPK plays acritical role in response to DNA damage. Herein, we demonstrated that expression of polyQ-expandedmutant Htt (mHtt) enhanced the phosphorylation of ATM. Ginsenoside is the main and most effectivecomponent of Panax ginseng. However, the protective effect of a ginsenoside (compound K, CK) in HDremains unclear and warrants further investigation. Methods: This study used the R6/2 transgenic mouse model of HD and performed behavioral tests,survival rate, histological analyses, and immunoblot assays. Results: The systematic administration of CK into R6/2 mice suppressed the activation of ATM/AMPK andreduced neuronal toxicity and mHTT aggregation. Most importantly, CK increased neuronal density andlifespan and improved motor dysfunction in R6/2 mice. Conversely, CK enhanced the expression of Bcl2protected striatal cells from the toxicity induced by the overactivation of mHtt and AMPK. Conclusions: Thus, the oral administration of CK reduced the disease progression and markedlyenhanced lifespan in the transgenic mouse model (R6/2) of HD.

Computerized Color Separation System for Printed Fabrics by Using Backward-Propagation Neural Network

Kuo, Chung-Feng Jeffrey,Su, Te-Li,Huang, Yi-Jen The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.5

Textile production must be coupled with hi-tech assistant system to save cost of labor, material, time. Therefore color quality control is one very important step in any textiles, however excellent the fabric material itself is, if it lacks good color, then it may still result in dull sale. Therefore, this paper proposes a printed fabrics computerized color separation system based on backward-propagation neural network, whose primary function is to separate rich color of printed fabrics pattern so as to reduce time-consuming manual color separation color matching of current players. What it adopted was RGB color space, expressed in red, green, and blue. Analyze color features of printed fabrics, use gene algorithm to find sub-image with same color distribution as original image of printed fabrics yet smaller area, for later color separation algorithm use. In terms of color separation algorithm, this paper relied on supervised backward-propagation neural network to conduct color separation of printed fabrics RGB sub-image, and utilized $PANTONE^{(R)}$ standard color ticket to do color matching, so as to realize accurate color separation.

Dynamic Modeling and Control of a Padder Roller System

Kuo, Chung-Feng Jeffrey,Chen, Jia-Siang The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.5

This is the first time in the literature dealing with the dynamic modeling and control of a rotating padder roller system. It is intended to design a control system with effective scheme and robustness to stabilize all vibration modes of a rotating padder roller system by using one set of sensor and actuator. The controller design depends on the specific pole-zero patterns. In practice, the pole-zero patterns remain the same, no matter how the physical system parameters are different. By properly placing the actuator and sensor, a realizable controller and sensor is designed to stabilize all the vibration modes and make the closed loop system absolutely stable. This will suppress the vibration without suffering from spillover and can eliminate an infinite number of vibration modes. The performance of this controller has been successfully implemented by computer simulation.

Optimization of the Processing Conditions and Prediction of the Quality for Dyeing Nylon and Lycra Blended Fabrics

Kuo Chung-Feng Jeffrey,Fang Chien-Chou The Korean Fiber Society 2006 Fibers and polymers Vol.7 No.4

This paper is intended to determine the optimal processing parameters applied to the dyeing procedure so that the desired color strength of a raw fabric can be achieved. Moreover, the processing parameters are also used for constructing a system to predict the fabric quality. The fabric selected is the nylon and Lycra blend. The dyestuff used for dyeing is acid dyestuff and the dyeing method is one-bath-two-section. The Taguchi quality method is applied for parameter design. The analysis of variance (ANOVA) is applied to arrange the optimal condition, significant factors and the percentage contributions. In the experiment, according to the target value, a confirmation experiment is conducted to evaluate the reliability. Furthermore, the genetic algorithm (GA) is combined with the back propagation neural network (BPNN) in order to establish the forecasting system for searching the best connecting weights of BPNN. It can be shown that this combination not only enhances the efficiency of the learning algorithm, but also decreases the dependency of the initial condition during the network training. Most of all, the robustness of the learning algorithm will be increased and the quality characteristic of fabric will be precisely predicted.

Self-organizing Map Network for Automatically Recognizing Color Texture Fabric Nature

Kuo, Chung-Feng Jeffrey,Kao, Chih-Yuan The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.2

The method of recognizing color texture brought forth in the present study is to employ unsupervised learning network to automatically recognize the fabric type and the main texture types. Firstly, the color scanner is adopted to extract fabric image which is afterwards saved as the digital image. Secondly, CIE-Lab color model is taken to obtain the feature value and wavelet transform is utilized to display the texture of the fabric image. Thirdly, co-occurrence matrix is employed to figure out the feature values of the texture structure such as angular second moment, entropy, homogeneity, contrast. Finally, self-organizing map (SOM) network is used as the classifier. The experiment result shows that the study can automatically and accurately classify the fabric types (including shuttle-woven fabric, jersey fabric and non-woven fabric) and main texture type of the fabric (such as plain weave, twill weave, satin weave, single jersey, double jersey and non-woven fabric).

Analysis and Construction of a Quality Prediction System for Needle-Punched Non-woven Fabrics

Kuo Chung-Feng Jeffrey,Su Te-Li,Chiu Chin-Hsun,Tsai Cheng-Ping The Korean Fiber Society 2007 Fibers and polymers Vol.8 No.1

In this study, polyester and polypropylene staple fibers were selected as the raw material, and then processed through roller-carder, cross-lapper and needle-punching machine to produce needle-punched non-woven fabrics. First, the experiment was planned using the Taguchi method to select processing parameters that affect the quality of the needle-punched non-woven fabric to act as the control factors for this experiment. The quality characteristics were the longitudinal and transverse tensile strength of the non-woven fabric as well as longitudinal and transverse tear strength. The $L_{18}(2^1{\times}3^7)$ orthogonal array was selected for the experiment as it offered an improvement on the traditional method that wastes a lot of time, effort and cost. By using the analysis of variance(ANOVA) technique at the same time, the effect of significant factors on the production process of needle-punched non-woven fabrics could be determined. Finally, the processing parameters were set as the input parameters of a back-propagation neural network(BPNN). The BPNN consists of an input layer, a hidden layer and an output layer where the longitudinal/transverse tensile and tear strength of the non-woven fabric were set as the output parameters. This was used to construct a quality prediction system for needle-punched non-woven fabrics. The experimental results indicated that the prediction system implemented in this study provided accurate predictions.