Method of forming a multiple layer dielectric and a hot film sensor therewith

The invention is a method of forming a multiple layer dielectric for use in a hot-film laminar separation sensor. The multiple layer dielectric substrate is formed by depositing a first layer of a thermoelastic polymer such as on an electrically conductive substrate such as the metal surface of a model to be tested under cryogenic conditions and high Reynolds numbers. Next, a second dielectric layer of fused silica is formed on the first dielectric layer of thermoplastic polymer. A resistive metal film is deposited on selected areas of the multiple layer dielectric substrate to form one or more hot-film sensor elements to which aluminum electrical circuits deposited upon the multiple layered dielectric substrate are connected

Method of Forming a Composite Coating with Particle Materials that are Readily Dispersed in a Sprayable Polyimide Solution

A method for creating a composite form of coating from a sprayable solution of soluble polyimides and particle materials that are uniformly dispersed within the solution is described. The coating is formed by adding a soluble polyimide to a solvent, then stirring particle materials into the solution. The composite solution is sprayed onto a substrate and heated in an oven for a period of time in order to partially remove the solvent. The process may be repeated until the desired thickness or characteristic of the coating is obtained. The polyimide is then heated to at least 495 F, so that it is no longer soluble

Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications

Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need an illumination control on color, intensity, and luminance, etc., which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise non-trivial challenges both in modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on Applications of Artificial Intelligence in Wireless Communication

Rateless codes-based secure communication employing transmit antenna selection and harvest-to-jam under joint effect of interference and hardware impairments

In this paper, we propose a rateless codes-based communication protocol to provide security for wireless systems. In the proposed protocol, a source uses the transmit antenna selection (TAS) technique to transmit Fountain-encoded packets to a destination in presence of an eavesdropper. Moreover, a cooperative jammer node harvests energy from radio frequency (RF) signals of the source and the interference sources to generate jamming noises on the eavesdropper. The data transmission terminates as soon as the destination can receive a sufficient number of the encoded packets for decoding the original data of the source. To obtain secure communication, the destination must receive sufficient encoded packets before the eavesdropper. The combination of the TAS and harvest-to-jam techniques obtains the security and efficient energy via reducing the number of the data transmission, increasing the quality of the data channel, decreasing the quality of the eavesdropping channel, and supporting the energy for the jammer. The main contribution of this paper is to derive exact closed-form expressions of outage probability (OP), probability of successful and secure communication (SS), intercept probability (IP) and average number of time slots used by the source over Rayleigh fading channel under the joint impact of co-channel interference and hardware impairments. Then, Monte Carlo simulations are presented to verify the theoretical results.Web of Science217art. no. 70

Experiment and Simulation Effects of Cyclic Pitch Control on Performance of Horizontal Axis Wind Turbine

Offshore wind is generally stronger and more consistent than wind on land. A large part of the offshore wind resource is however located in deep water, where floating wind turbines can harvest more energy. This paper describes a systematic experiment and a simulation analysis (FAST code) about the cyclic pitch control of blades. This work was performed to investigate performance fluctuation of a floating wind turbine utilizing cyclic pitch control. The experiment was carried out in an open wind tunnel with mainstream wind velocity of 10 m/s with the front inflow wind and the oblique inflow wind conditions. A model wind turbine is two-bladed downwind wind turbine with diameter of 1.6 m. Moment and force acts on the model wind turbine were measured by a six-component balance. Fluctuation of power coefficient and thrust coefficient was investigated in the cyclic pitch control. The model wind turbine and the experimental conditions were simulated by FAST code. The comparison of the experimental data and the simulation results of FAST code show that the power coefficient and thrust coefficient are in good agreement. Keywords: Floating Offshore Wind Turbine, Aerodynamic Forces, Cyclic Pitch Control, FAST Code, Wind Tunnel ExperimentArticle History: Received February 11st 2017; Received in revised form April 29th 2017; Accepted June 2nd 2017; Available onlineHow to Cite This Article: Sang, L.Q., Maeda, T., Kamada, Y. and Li, Q. (2017) Experiment and simulation effect of cyclic pitch control on performance of horizontal axis wind turbine to International Journal of Renewable Energy Development, 6(2), 119-125.https://doi.org/10.14710/ijred.6.2.119-12

Characterization of Electromagnetic Properties for Durability Performance and Saturation in Hardened Cement Mortar

Electromagnetic (EM) properties—dielectric constant and conductivity are changed with porosity and saturation in cement-based materials. In this paper, dielectric constant and conductivity are measured in cement mortar with 5 different mixture conditions considering saturation. For the same mixture proportions, durability tests including porosity, chloride diffusion, air permeability, sorptivity, and water diffusion are performed. Among the continuously measured EM properties within 5–20 GHz of frequency range for different saturation, results under 60% of saturation which shows stable results are selected and averaged as one value. The averaged measurements utilizing results under 60% of saturation are compared with those from durability tests. Through the normalization using the results of W/C 40% which shows best durability performances, changing ratios of durability characteristics are evaluated with normalized dielectric constant and conductivity. The behaviors of EM properties with different saturation and their relationships with durability performances are studied

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the nonconvexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.Comment: to appear in IEEE J. Sel. Areas Commu

Learning Autonomy in Management of Wireless Random Networks

This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. Individual nodes decide their optimal states with distributed coordination among other nodes through randomly varying backhaul links. This poses a technical challenge in distributed universal optimization policy robust to a random topology of the wireless network, which has not been properly addressed by conventional deep neural networks (DNNs) with rigid structural configurations. We develop a flexible DNN formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology. A key enabler of this approach is an iterative message-sharing strategy through arbitrarily connected backhaul links. The DMPNN provides a convergent solution for iterative coordination by learning numerous random backhaul interactions. The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.Comment: to appear in IEEE TW

Contributions to peptidomimetic design: predictive computational studies and syntheses of linker molecules

In an effort to partially mimic the complex interaction between nerve growth factor (NGF) and its membrane-bound tyrosine kinase A receptor (TrkA), several small organic molecules with functionalities similar to the side-chains of the amino acid residues of NGF critical to binding were devised. These molecules were studied computationally using the program Affinity. Each molecule was individually docked onto one of the binding sites on TrkA as determined by mutagenesis studies and the x-ray crystal structure obtained from the Protein Data Bank. One of the strategies to enhance binding of active peptidomimetics to their target proteins is to link them together to form either homodimers or heterodimers. However, these dimers have low solubility in water and mimic only residues that are close together on the protein. Triethylene oxide- and hexaethylene oxide-based linker molecules were designed to circumvent these limitations. The increased polarity will improve the watersolubility and the added lengths, which can be controlled and varied by simple chemical manipulations, will allow for mimicking critical residues that are farther apart on the protein

Statistical Analysis of Fragility Curves

This paper presents a statistical analysis of structural fragility curves. Both empirical and analytical fragility curves are considered. The empirical fragility curves are developed utilizing bridge damage data obtained from the 1995 Hyogo-ken Nanbu (Kobe) earthquake. The analytical fragility curves are constructed on the basis of the nonlinear dynamic analysis. Two-parameter lognormal distribution functions are used to represent the fragility curves with the parameters estimated by the maximum likelihood method. This paper also presents methods of testing the goodness of fit of the fragility curves and estimating the confidence intervals of the two parameters (median and log-standard deviation) of the distribution. An analytical interpretation of randomness and uncertainty associated with the median is provided