On processing development for fabrication of fiber reinforced composite, part 2

Fiber-reinforced composite laminates are used in many aerospace and automobile applications. The magnitudes and durations of the cure temperature and the cure pressure applied during the curing process have significant consequences for the performance of the finished product. The objective of this study is to exploit the potential of applying the optimization technique to the cure cycle design. Using the compression molding of a filled polyester sheet molding compound (SMC) as an example, a unified Computer Aided Design (CAD) methodology, consisting of three uncoupled modules, (i.e., optimization, analysis and sensitivity calculations), is developed to systematically generate optimal cure cycle designs. Various optimization formulations for the cure cycle design are investigated. The uniformities in the distributions of the temperature and the degree with those resulting from conventional isothermal processing conditions with pre-warmed platens. Recommendations with regards to further research in the computerization of the cure cycle design are also addressed

A momentum-space representation of Feynman propagator in Riemann-Cartan spacetime

We first construct generalized Riemann-normal coordinates by using autoparallels, instead of geodesics, in an arbitrary Riemann-Cartan spacetime. With the aid of generalized Riemann-normal coordinates and their associated orthonormal frames, we obtain a momentum-space representation of the Feynman propagator for scalar fields, which is a direct generalization of Bunch and Parker's works to curved spacetime with torsion. We further derive the proper-time representation in $n$ dimensional Riemann-Cartan spacetime from the momentum-space representation. It leads us to obtain the renormalization of one-loop effective Lagrangians of free scalar fields by using dimensional regularization. When torsion tensor vanishes, our resulting momentum-space representation returns to the standard Riemannian results.Comment: 12 page

Rare kaon decays in SUSY with non-universal A terms

We study the rare kaon decays in the framework of general SUSY models. Unlike the results in the literature, we find the contributions from the gluino exchange to the branching ratio of $K^+\to \pi^+ \nu \bar{\nu}$ can reach the central value ($\sim 1.5 \times 10^{-10}$) of the new E787 data while the predicted value of standard model is less than $10^{-10}$. We also find that the same effects also enhance the decays of $K_{L}\to \pi^0 \nu \bar{\nu}$, $K_L\to\pi^0e^{+} e^{-}$ and $K_L\to\mu^+ \mu^-$.Comment: 9 pages, references added, revised version to appear in J. Phys.

Entropy production and equilibration in Yang-Mills quantum mechanics

The Husimi distribution provides for a coarse grained representation of the phase space distribution of a quantum system, which may be used to track the growth of entropy of the system. We present a general and systematic method of solving the Husimi equation of motion for an isolated quantum system, and we construct a coarse grained Hamiltonian whose expectation value is exactly conserved. As an application, we numerically solve the Husimi equation of motion for two-dimensional Yang-Mills quantum mechanics (the x-y model) and calculate the time evolution of the coarse grained entropy of a highly excited state. We show that the coarse grained entropy saturates to a value that coincides with the microcanonical entropy corresponding to the energy of the system.Comment: 23 pages, 23 figure

A CHF detection method based on deep learning with RR intervals

© 2017 IEEE. There are extensive studies investigating congestive heart failure (CHF) detection based on heart rate variability. Although a high level of accuracy has been achieved, its robustness under different conditions is not guaranteed. To improve the robustness, we applied sparse auto-encoder-based deep learning algorithm in CHF detection with RR intervals. A total data size of 30,592 (5-min RR interval) was obtained from 72 healthy persons and 44 CHF patients. The deep learning algorithm first extracts unsupervised features using a sparse auto-encoder from raw RR intervals, then constructs a deep neural network model with various hidden nodes combinations. Results showed that the model achieved 72.41% accuracy. This demonstrated that RR intervals have potential in CHF detection but cannot fully reflect dynamic change in 24-h

On the medication distribution system for home health care through convenience stores, lockers, and home delivery.

Medication distribution service can be delivered based on a combination of home delivery and customer pickup. That is, medications are delivered either to customers' homes directly or to the pickup facilities (e.g. lockers) close to customers' homes. In Taiwan, there are more than 11,000 convenience stores that provide a 24-h service for customers to pick up the ordered items from e-commerce, which is unique to the world. In the medication distribution system, convenience stores can provide a unique opportunity for customers to more conveniently collect medications at stores, and also can reduce the operating cost for a logistics company providing the medication delivery service. Therefore, this work proposes a medication distribution system through convenience stores, lockers, and home delivery. Under this system, this work investigates how to simultaneously determine employment of convenience store chains, the convenience store locations to be visited, locations of lockers, vehicle routes for convenience stores and lockers, and vehicle routes for customers' homes, so that the total operating cost is minimized. This work further proposes a genetic algorithm to solve the medication distribution problem. Through simulation, the experimental results show that the proposed algorithm is able to solve the problem efficiently

Further analytical study of hybrid rocket combustion

Analytical studies of the transient and steady-state combustion processes in a hybrid rocket system are discussed. The particular system chosen consists of a gaseous oxidizer flowing within a tube of solid fuel, resulting in a heterogeneous combustion. Finite rate chemical kinetics with appropriate reaction mechanisms were incorporated in the model. A temperature dependent Arrhenius type fuel surface regression rate equation was chosen for the current study. The governing mathematical equations employed for the reacting gas phase and for the solid phase are the general, two-dimensional, time-dependent conservation equations in a cylindrical coordinate system. Keeping the simplifying assumptions to a minimum, these basic equations were programmed for numerical computation, using two implicit finite-difference schemes, the Lax-Wendroff scheme for the gas phase, and, the Crank-Nicolson scheme for the solid phase

Kinetics of TiSi2 formation by thin Ti films on Si

Silicide formation with Ti deposited on single crystal Si and Ti deposited on amorphous Si layers sequentially without breaking the vacuum was investigated using backscattering spectrometry and glancing-angle x-ray diffraction. For Ti deposited on amorphous Si, TiSi2 was formed with a rate proportional to (time)^1/2 and an activation energy of 1.8±0.1 eV. For Ti deposited on single crystal Si, the reaction rate was slower and the silicide layer was nonuniform in thickness. We attribute the difference in behavior to the presences of interfacial impurities in the case where Ti was deposited on single crystal Si

Fastener-Based Computational Models with Application to Cold-Formed Steel Shear Walls

The objective of this paper is to validate a tool that design engineers could employ to develop mechanics-based predictions of the lateral response of wood-sheathed cold-formed steel (CFS) framed shear walls applicable in a wide variety of situations. Wood framed shear walls enjoy a variety of tools, most notably SAPWood and its predecessor CASHEW, that provide a means to predict the complete hysteretic behavior of a shear wall based on the nail fastener schedule and board selection. The existence of these tools helps engineers in unique design situations, encourages innovation in shear wall design particularly for Type I shear walls, and provides enabling modeling details critical for seismic performance-based design. Recently, as part of the CFS-NEES effort, the cyclic performance of CFS stud-to-sheathing connections has been characterized. In addition, the cyclic performance of full CFS shear walls, utilizing the same connections, has also been characterized. This paper explores an engineering model implemented in OpenSees that directly employs the fastener-based characterization as the essential nonlinearity in a CFS framed shear wall. CFS shear wall framing is modeled with beam elements, hold downs are modeled with linear springs, sheathing is modeled as a rigid diaphragm, and the stud-to-sheathing connections as zero-length springs utilizing the Pinching04 material model in OpenSees. Production, analysis, and post-processing of the model are automated with custom Matlab scripts that form the basis for a future engineering tool. The model is validated against monotonic and cyclic shear wall tests, and is shown to have good agreement. In addition to providing a mechanical means to assess shear walls, high fidelity shell finite element models are completed in ABAQUS to shed additional light on the mechanics-based OpenSees model. The long-term goal of the modelling is to provide a reliable means to predict the lateral response of any CFS framed system that relies on connection deformations, such as gravity walls or wood-sheathed floor diaphragms in addition to shear walls

On the design and implementation of a high definition multi-view intelligent video surveillance system

This paper proposes a distributed architecture for high definition (HD) multi-view video surveillance system. It adopts a modular design where multiple intelligent Internet Protocol (IP)-based video surveillance cameras are connected to a local video server. Each server is equipped with storage and optional graphics processing units (GPUs) for supporting high-level video analytics and processing algorithms such as real-time decoding and tracking for the video captured. The servers are connected to the IP network for supporting distributed processing and remote data access. The DSP-based surveillance camera is equipped with realtime algorithms for streaming compressed videos to the server and performing simple video analytics functions. We also developed video analytics algorithms for security monitoring. Both publicly available data set and real video data that are captured under indoor and outdoor scenarios are used to validate our algorithms. Experimental results show that our distributed system can support real-time video applications with high definition resolution.published_or_final_versio