On the oxidation kinetics and mechanisms of various SiC-coated carbon-carbon composites

SiC coatings on the surface of C-C were produced by either silicone resin impregnation/ pyrolysis or reaction sintering. Cycles of resin impregnation/pyrolysis produced an SiC coating, on the walls of fine open pores, which was effective in reducing the oxidation rate of C-C and in shifting the transition temperature to a higher value. Unless it is pre-coated with a pyrocarbon layer before sintering, plain reaction sintered SiC has oxidation behaviors similar to those of the above-mentioned SiC. The dense pyrocarbon film deposition on the surface of C-C could form a better SiC film than others. The carbon film homogenized the surface of C-C and a dense SiC film was established. The oxidation of the coated C-C can be modelled by a set of “oxidation resistors” in series and/or in parallel, with each resistor corresponding to an oxidation element. The controlling mechanism can be resolved from the activation energy. A combined resistant layer, consisting of resin impregnation, pyrocarbon film and reaction sintering SiC, showed the best oxidation resistance of any single-layer coated C-C composite.     

马氏体相变研究的进展和瞻望SCIEI

叙述马氏体相变研究的一些进展和瞻望,包括马氏体相变的定义,马氏体相变热力学,奥氏体状态对马氏体相变的影响,动力学,形核和长大,以及马氏体相变晶体学。     

Oxidation of Si-based ceramics in the presence of alkali halide vapors

Nonoxide silicon-based ceramics owe their oxidation resistance to the formation of a thin silica film that separates the ceramic from oxidizing environments. This silica film can be fluxed by alkali oxides to form low-melting alkali silicate corrosion products that do not provide protection. Alkali chlorides and other halides have sufficiently high vapor pressures that silica fluxing can occur without the formation of a condensed halide salt. The rates of oxidation of Si, SiC, and Si₃N₄ are influenced by the composition of gas mixtures containing these species.     

Task rescheduling optimization to minimize network resource consumption

An increasing number of big-data services are being deployed in a cloud computing environment, attracted by the on-demand service, rapid elasticity, and low maintenance costs. As a result, ensuring the quality of service has become an important research problem. Traditionally, task rescheduling is used to ensure a consistent quality of service in the event of failure of a virtual machine. However, the network resource consumption of different rescheduling methods varies. To address this problem, we propose a task rescheduling method that minimizes network resource consumption.The method includes three algorithms. The first obtains a set of good virtual machines from the large quantity of service-providing virtual machines using the skyline operation. A ranking algorithm then fuses the data size and the task emergency to identify significant tasks. Finally, we present an algorithm that automatically determines the optimal insertion point for each task. To verify the effectiveness of the proposed method, we extend the renowned simulator CloudSim and conduct a series of experiments. The results show that our method is more efficient than other methods in terms of network resource consumption.     

3D resistive RAM cell design for high-density storage class memory—a review

In this article, we comprehensively review recent progress in the ReRAM cell technology for 3D integration focusing on a material/device level. First we briefly mention pioneering work on high-density crossbar ReRAM arrays which paved the way to 3D integration. We discuss the two main proposed 3D integration schemes—3D horizontally stacked ReRAM vs 3D Vertical ReRAM and their respective advantages and disadvantages. We follow with the detailed memory cell design on important work in both areas, utilizing either filamentary or interface-limited switching mechanisms. We also discuss our own contributions on HfO₂-based filamentary 3D Vertical ReRAM as well as TaO_x/TiO₂ bilayer-based self-rectifying 3D Vertical ReRAM. Finally, we summarize the present status and provide an outlook for the nearterm future.     

Exploring the Individual's Behavior on Self-Disclosure Online

Even though the ubiquity of technology can make many aspects of life more convenient, it may simultaneously raise some concerns about the privacy issues related to personal information. This research focused on the individual's behavior toward self-disclosure online and his or her cognition on personal privacy that was affected by not only the personal psychological factors but also the individual's computer knowledge and associated skills. To study how technological and psychological factors can simultaneously affect one's privacy concern and restrain behavior on disclosure, this study applied both perceived privacy and privacy concern to assess such factors as personal subjective attitude, computer self-efficacy, and perceived behavioral control to investigate the technological influence. A hypothesized model was proposed to describe the relationships among these constructs. After structural equation modeling is utilized to analyze the proposed hypotheses, the research findings showed that, although the perceived behavioral control had a negative effect on self-disclosure online, all the other hypotheses indicated a positive casual effect.     

A study of optimal viewing distance in a parallax barrier 3D display

The optimal viewing distance was proposed as a parameter for designing a parallax barrier 3D display. It can be designed based on simple geometric method and by considering the pitches of image display pixels and parallax barrier, or even including the aperture ratios of the image display pixels and parallax barrier. It can be analyzed by using ray tracing method. By considering the optical refraction index of the cover glass, the angular behavior of the system becomes more realistic; however, the geometric method is difficult to be used. We propose a revised method for estimating the view distance and angular behavior. In this paper, we have demonstrated a designated eye position (DEP) for each view and shown that multiple DEPs make a circular curve around the center of the display. We prove the new concept by comparing the optical ray tracing calculations and optical measurement.     

Personalized web feeds based on ontology technologies

The rapid development of the social Web has resulted in diverse Web 2.0 applications for accessing various Web feeds such as Weblogs, news headlines, business products, real time information and Podcasts. Due to the masses of Web feeds available on the Internet, a major challenge is how to access them efficiently. Conventional methods of manually finding and matching keyword for Web feeds are time-consuming and inaccurate. This study addresses this issue by defining a Personalized Web Feeds Framework (PWFF) for integrating ontology technologies into Web feeds and user profiles. The proposed PWFF is used to develop an Ontology-based Personalized Web Feed Platform (OPWFP) that provides customized Web feeds for personnel needs. To demonstrate the feasibility of OPWFP, the experimental results illustrate the efficiency and effectiveness of the proposed approach.     

Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.     

Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems

Many published papers show that a TSK-type fuzzy system provides more powerful representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has a similar feature to the fuzzy system. As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters. Then, a supervisory adaptive dynamic RBF-based neural-fuzzy control (SADRNC) system which is composed of a DTRN controller and a supervisory compensator is proposed. The DTRN controller is designed to online estimate an ideal controller based on the gradient descent method, and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the DTRN controller upon the system stability in the Lyapunov sense. Finally, the proposed SADRNC system is applied to control a chaotic system and an inverted pendulum to illustrate its effectiveness. The stability of the proposed SADRNC scheme is proved analytically and its effectiveness has been shown through some simulations.