Two‐Dimensional Self‐Organization of an Ordered Au Silicide Nanowire Network on a Si(110)‐16 × 2 Surface

A well‐ordered two‐dimensional (2D) network consisting of two crossed Au silicide nanowire (NW) arrays is self‐organized on a Si(110)‐16 × 2 surface by the direct‐current heating of ≈1.5 monolayers of Au on the surface at 1100 K. Such a highly regular crossbar nanomesh exhibits both a perfect long‐range spatial order and a high integration density over a mesoscopic area, and these two self‐ordering crossed arrays of parallel‐aligned NWs have distinctly different sizes and conductivities. NWs are fabricated with widths and pitches as small as ≈2 and ≈5 nm, respectively. The difference in the conductivities of two crossed‐NW arrays opens up the possibility for their utilization in nanodevices of crossbar architecture. Scanning tunneling microscopy/spectroscopy studies show that the 2D self‐organization of this perfect Au silicide nanomesh can be achieved through two different directional electromigrations of Au silicide NWs along different orientations of two nonorthogonal 16 × 2 domains, which are driven by the electrical field of direct‐current heating. Prospects for this Au silicide nanomesh are also discussed.     

Artificial neural network application of modeling failure rate for Boeing 737 tires

This paper presents an application of artificial neural network (ANN) technique for conducting the reliability analysis of Boeing 737 tires. For this purpose, an ANN model utilizing the feed‐forward back‐propagation algorithm as a learning rule is developed. The inputs to the neural network are the flight operational time and the number of landings as independent variables and the output is the failure rate of the tires. Two years of data are used for failure rate prediction model and validation. Model validation, which reflects the suitability of the model for future predictions, is performed by comparing the predictions of the model with that of Weibull regression model. The results show that the failure rate predicted by the ANN is closer in agreement with the actual data than the failure rate predicted by the Weibull model. The present work also identifies some of the common tire failures and presents representative results based on the established model for the most frequently occurring tire failure. Copyright © 2010 John Wiley & Sons, Ltd.     

Super‐resolution of the undersampled and subpixel shifted image sequence by a neural network

Numerous approaches to super‐resolution (SR) of sequentially observed images (image sequence) of low resolution (LR) have been presented in the past two decades. However, neural network methods are almost ignored for solving SR problems. This is because the SR problem traditionally has been regarded as the optimization of an ill‐posed large set of linear equations. A designed neural network based on this has a large number of neurons, thereby requiring a long learning time. Also, the deduced cost function is overly complex. These defects limit applications of a neural network to an SR problem. We think that the underlying meaning of the SR problem should refer to super‐resolving an imaging system by image sequence observation, instead of merely improving the image sequence itself. SR can be regarded as a pattern mapping from LR to SR images. The parameters of the pattern mapping can be learned from the imaging process of the image sequence. This article presents a neural network for SR based on learning from the imaging process of the image sequence. In order to speed up the convergence, we employ vector mapping to train the neural network. A mapping vector is composed of some neighbor subpixels. Such a well‐trained neural network has powerful generalization ability so that it can be used directly to estimate the SR image of the other image sequences without learning again. Our simulations show the effectiveness of the proposed neural network. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 8–15, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20001     

Water‐Rich Biomimetic Composites with Abiotic Self‐Organizing Nanofiber Network

Load‐bearing soft tissues, e.g., cartilage, ligaments, and blood vessels, are made predominantly from water (65–90%) which is essential for nutrient transport to cells. Yet, they display amazing stiffness, toughness, strength, and deformability attributed to the reconfigurable 3D network from stiff collagen nanofibers and flexible proteoglycans. Existing hydrogels and composites partially achieve some of the mechanical properties of natural soft tissues, but at the expense of water content. Concurrently, water‐rich biomedical polymers are elastic but weak. Here, biomimetic composites from aramid nanofibers interlaced with poly(vinyl alcohol), with water contents of as high as 70–92%, are reported. With tensile moduli of ≈9.1 MPa, ultimate tensile strains of ≈325%, compressive strengths of ≈26 MPa, and fracture toughness of as high as ≈9200 J m^?2, their mechanical properties match or exceed those of prototype tissues, e.g., cartilage. Furthermore, with reconfigurable, noncovalent interactions at nanomaterial interfaces, the composite nanofiber network can adapt itself under stress, enabling abiotic soft tissue with multiscale self‐organization for effective load bearing and energy dissipation.     

基于粗糙集和神经网络的联合模式识别系统

提出了一种划分属性离散区间的新方法．针对这种划分，提出一种约简和去噪的方法．随后，建立了粗糙集和LVQ神经网络的联合模式识别系统．最后，比较了用该系统和仅用神经网络进行识别的效果，证明了该方法的有效性．     

Compromise ascent directions for multiple‐response applications

A process or system under study often requires the measurement of multiple responses. The optimization of multiple response variables has received considerable attention in the literature with the majority focusing on locating optimal operating conditions within the current experimental region and thus often occurs in the later stages of experimentation. This article focuses instead on the initial experiment and the location of additional experimental runs if the region of interest shifts. Considerable trade‐off is often required in the multiple response context. In order to account for uncertainty in the model parameters and correlations among the responses, we propose the computation of Bayesian reliabilities to determine optimal factor settings for future experimental runs. The approach will be described in detail for two common design follow‐up strategies: steepest ascent (descent) and shifting factor levels. Illustrative examples are provided for each application. Copyright © 2011 John Wiley & Sons, Ltd.     

Neural network constitutive modelling for non‐linear characterization of anisotropic materials

This paper presents a new technique of neural network constitutive modelling for non‐linear characterization of anisotropic materials. The proposed technique, based on a recently developed energy‐based characterization framework, derives the variations of the external work applied to and the strain energy induced in a specimen. The error between the variations of the energies is subsequently applied to correct the neural network properties by using a modified backpropagation algorithm. Unlike the conventional techniques for neural network constitutive modelling, the proposed technique develops models by quantifying the deformation of the specimen on a continuum basis. This allows the neural network constitutive models to be constructed from a single load test of one specimen. Numerical examples first examine the effect of specimen geometries and loading conditions. The effect of noise in the experimental measurements is subsequently investigated while having the applicability for non‐linear constitutive behaviour shown thereafter. The application for anisotropic materials is finally demonstrated by modelling a unidirectional lamina based on the measurements of a biaxial load test on a balanced laminate. Copyright © 2010 John Wiley & Sons, Ltd.     

A new neural network‐based model for hysteretic behavior of materials

Cyclic behavior of materials is complex and difficult to model. A combination of hardening rules in classical plasticity is one possibility for modeling this complex material behavior. Neural network (NN) constitutive models have been shown in the past to have the capability of modeling complex material behavior directly from the results of material tests. In this paper, we propose a novel approach for NN‐based modeling of the cyclic behavior of materials. The proposed NN material model uses new internal variables that facilitate the learning of the hysteretic behavior of materials. The same approach can also be used in modeling of the hysteretic behavior of structural systems or structural components under cyclic loadings. The proposed model is shown to be superior to the earlier versions of NN material models. Although the earlier versions of the NN material models were effective in capturing the multi‐axial material behavior, they were only tested under cyclic uni‐axial state of stress. The proposed NN material model is capable of learning the hysteretic behavior of materials under even non‐uniform stress state in multi‐dimensional stress space. The performance of the proposed model is demonstrated through a series of examples using actual experimental data and simulated testing data. Copyright © 2007 John Wiley & Sons, Ltd.     

Quasi‐static crack propagation in plane and plate structures using set‐valued traction‐separation laws

We introduce a numerical technique to model set‐valued traction‐separation laws in plate bending and also plane crack propagation problems. By using of recent developments in thin (Kirchhoff–Love) shell models and the extended finite element method, a complete and accurate algorithm for the cohesive law is presented and is used to determine the crack path. The cohesive law includes softening and unloading to origin, adhesion and contact. Pure debonding and contact are obtained as particular (degenerate) cases. A smooth root‐finding algorithm (based on the trust‐region method) is adopted. A step‐driven algorithm is described with a smoothed law which can be made arbitrarily close to the exact non‐smooth law. In the examples shown the results were found to be step‐size insensitive and accurate. In addition, the method provides the crack advance law, extracted from the cohesive law and the absence of stress singularity at the tip. Copyright © 2007 John Wiley & Sons, Ltd.     

A neural network graph partitioning procedure for grid‐based domain decomposition

This paper describes a neural network graph partitioning algorithm which partitions unstructured finite element/volume meshes as a precursor to a parallel domain decomposition solution method. The algorithm works by first constructing a coarse graph approximation using an automatic graph coarsening method. The coarse graph is partitioned and the results are interpolated onto the original graph to initialize an optimization of the graph partition problem. In practice, a hierarchy of (usually more than two) graphs are used to help obtain the final graph partition. A mean field theorem neural network is used to perform all partition optimization. The partitioning method is applied to graphs derived from unstructured finite element meshes and in this context it can be viewed as a multi‐grid partitioning method. Copyright © 1999 John Wiley & Sons, Ltd.     

Content‐based gastric image retrieval using convolutional neural networks

The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content‐based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet‐18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet‐18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006 , outperforming other state‐of‐art conventional methods. Furthermore, we constructed a Gastric‐Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.     

Multifunctional POSS‐Based Nano‐Photo‐Initiator for Overcoming the Oxygen Inhibition of Photo‐Polymerization and for Creating Self‐Wrinkled Patterns

Oxygen inhibition remains a challenge in photo‐curing technology despite the expenditure of considerable effort in developing a convenient, efficient, and low‐cost prevention method. Here, a novel strategy to prevent oxygen inhibition is presented; it is based on the self‐assembly of multifunctional nano‐photo‐initiators (F₂‐POSS‐(SH)₄‐TX/EDB) at the interface of air and the liquid monomer. These nano‐photo‐initiators consist of a thiol‐containing polyhedral oligomeric silsesquioxane (POSS) skeleton onto which fluorocarbon chains and thioxanthone and dimethylaminobenzoate (TX/EDB) photo‐initiator moieties are grafted. Real‐time Fourier‐transform infrared spectroscopy (FT‐IR) is used to investigate the photo‐polymerization of various acrylate monomers that are initiated by F₂‐POSS‐(SH)₄‐TX/EDB and its model analogues in air and in N₂. FT‐IR results show that F₂‐POSS‐(SH)₄‐TX/EDB decreases the effects of oxygen inhibition. X‐ray photo‐electron spectroscopy and atomic force microscopy reveal that the self‐assembly of F₂‐POSS‐(SH)₄‐TX/EDB at the air/(liquid monomer) interface forms a cross‐linked top layer via thiol–ene polymerization; this layer acts as a physical barrier against the diffusion of oxygen from the surface into the bulk layer. A mismatch in the shrinkage between the top and bulk layers arise as a result of the different types of photo‐cross‐linking reactions. Subsequently, the surface develops a wrinkled pattern with a low surface energy. This strategy exhibits considerable potential for preventing oxygen inhibition, and the wrinkled pattern may prove very useful in photo‐curing technology.     

Carbon Nanolights in Piezopolymers are Self‐Organizing Toward Color Tunable Luminous Hybrids for Kinetic Energy Harvesting

Herein, an all‐solid‐state sequential self‐organization and self‐assembly process is reported for the in situ construction of a color tunable luminous inorganic/polymer hybrid with high direct piezoresponse. The primary inorganic self‐organization in solid polymer and the subsequent polymer self‐assembly are achieved at high pressure with the first utilization of piezo‐copolymer (PVDF‐TrFE) as the host matrix of guest carbon quantum dots (CQDs). This process induces the spontaneous formation of a highly ordered, microscale, polygonal, and hierarchically structured CQDs/PVDF‐TrFE hybrid with multicolor photoluminescence, consisting of very thermodynamic stable polar crystalline nanowire arrays. The electrical polarization‐free CQDs/PVDF‐TrFE hybrids can efficiently harvest the environmental available kinetic mechanical energy with a new large‐scale group‐cooperation mechanism. The open‐circuit voltage and short‐circuit current outputs reach up to 29.6 V cm^?2 and 550 nA cm^?2, respectively. The CQDs/PVDF‐TrFE–based hybrid nanogenerator demonstrates drastically improved durable and reliable features during the real‐time demonstration of powering commercial light emitting diodes. No attenuation/fluctuation of the electrical signals is observed for ≈10 000 continuous working cycles. This study may offer a new design concept for progressively but spontaneously constructing novel multiple self‐adaptive complex inorganic/polymer hybrids that promise applications in the next generation of self‐powered autonomous optoelectronic devices.     

A hyperstable neural network for the modelling and control of nonlinear systems

A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.     

Microsphere‐Assisted Robust Epidermal Strain Gauge for Static and Dynamic Gesture Recognition

A novel and robust epidermal strain gauge by using 3D microsphere arrays to immobilize, connect, and protect a multiwalled carbon nanotubes (MWNTs) pathway is presented. During the solvent deposition process, MWNTs sedimentate, self‐assemble, and wrap onto surface of polystyrene (PS) microspheres to construct conductive networks, which further obtain excellent stretchability of 100% by combining with commercially used elastomer. Benefiting from its 3D conductive pathway defined by microspheres, immobilized MWNT (I‐MWNT) network can be directly used in practical occasions without further packaging and is proved by tape tests to be capable of defend mechanical damage effectively from external environment. By parameter optimization, the strain sensor with 3 µm PS spheres obtains stable resistive responses for more than 1000 times, and maintains its gauge factor (GF) of 1.35. This thin‐film conductive membrane built by this effective construction method can be easily attached onto fingers of both robot and human, and is demonstrated in sensitive epidermal strain sensing and recognizing different hand gestures effectively, in static and dynamic modes, respectively.