arXiv subjects

Ning Zhang

Publications and source records attributed to Ning Zhang.

At least 325 records · Page 18Linked to original sources

arXiv2018

Understanding Specimen- and Grain-Size Effects on Nanoscale Plastic Deformation Mechanisms and Mechanical Properties of Polycrystalline Yttria-Stabilized Tetragonal Zirconia Nanopillars

Specimen- and grain-size effects on nanoscale plastic deformation mechanisms and mechanical properties of polycrystalline yttria-stabilized tetragonal zirconia (YSTZ) nanopillars are studied by molecular dynamics simulations. Through uniaxial compression of YSTZ columnar nanopillars, intergranular and transgranular deformation mechanisms are investigated. Cooperative intergranular deformations including grain boundary sliding and migration, grain rotation, and amorphous phase formation at grain boundaries are revealed. Results also reveal formation of partial dislocations, which act as splitters of large grains and play a significant role in facilitating the rotation of grains, and consequently promote amorphous-to-crystalline phase transition in-between neighboring grains. An increase in free surface-to-volume ratio is found to be responsible for specimen size-induced softening phenomenon, where a decrease in Young modulus and strength is observed when the specimen width decreases from 30 nm to 10 nm. Also, a decrease in Young modulus and strength is revealed with the decrease of average grain size from 15 nm to 5 nm. Grain boundary density is identified to be responsible for the observed grain size-induced softening behavior in polycrystalline YSTZ nanopillars. A transition in dominant deformation mechanism is observed from amorphous phase formation at grain boundaries to a competition between intergranular grain boundary sliding and transgranular phase transformation. Furthermore, an inverse Hall-Petch relationship is revealed describing the correlation between grain size and strength for polycrystalline YSTZ nanopillars with grain sizes below 15 nm.

cond-mat.mtrl-sci↗

arXiv2018

The initial-boundary value problems for the coupled derivative nonlinear Schrödinger equations on the half-line

The unified transform method is used to analyze the initial-boundary value problem for the coupled derivative nonlinear Schrödinger(CDNLS) equations on the half-line. In this paper, we assume that the solution $u(x,t)$ and $v(x,t)$ of CDNLS equations are exists, and we show that it can be expressed in terms of the unique solution of a matrix Riemann-Hilbert problem formulated in the plane of the complex spectral parameter $λ$.

nlin.SI↗

arXiv2018

Planning Low-Carbon Campus Energy Hubs

Multi-energy systems can provide a constant level of service to end-use energy demands, while deriving delivered energy from a variety of primary/secondary energy sources. This fuel-switching capability can be used to reduce operating expenses, reduce environmental impacts, improve flexibility to accommodate renewable energy, and improve reliability. This paper presents four frameworks for incentivizing energy hub equipment investments for low-carbon operation targets. These frameworks vary in the measures taken to achieve low-carbon operation (explicit constraint vs. carbon pricing) and in the relationship between the hub builder and operator (cooperative vs. uncoordinated). The underlying energy hub model upon which these frameworks are built is an enhanced greenfield model, introducing 'energy buses' to reduce dimensionality. A case study is conducted for a campus being designed in Beijing, and results from each framework are compared to illustrate their relative costs. When the operator cannot be trusted to cooperate in controlling emissions, the system must be 'overbuilt' with more expensive equipment to ensure emissions target are met. A taxation-based approach increases overall costs at moderate emissions targets, but this effect decreases at aggressive targets. This paper also compares the cost of less efficient institutional frameworks with the most efficient approach, i.e. cooperation between builder and operator with constraints on emissions.

math.OC↗

arXiv2018

The Novel Symmetry Constraint and Binary Nonlinearization of the Super Generalized Broer-Kaup Hierarchy with Self-consistent Sources and Conservation Laws

The super generalized Broer-Kaup(gBK) hierarchy and its super Hamiltonian structure are established based on a loop super Lie algebra and super-trace identity. Then the self-consistent sources, the conservation laws, the novel symmetry constraint and the binary nonlinearization of the super gBK hierarchy are generated, respectively. In addition, the integrals of motion required for Liouville integrability are explicitly given.

math.AP↗

arXiv2018

Riemann-Hilbert approach for a mixed coupled nonlinear Schrödinger system and its soliton solutions

In this work, we examine the integrable mixed coupled nonlinear Schrödinger (mCNLS) system, which describe the propagation of an optical pulse in a birefringent optical fiber. By the Riemann-Hilbert(RH) approach, the N-soliton solutions of the mCNLS system can be expressed explicitly when the jump matrix of a specific RH problem is a $3\times3$ unit matrix. As a special example, the expression of one- and two-soliton are displayed explicitly. More generally, as a promotion, an integrable generalized multi-component NLS system with its linear spectral problem be discussed. It is hoped that our results can help enrich the nonlinear dynamical behaviors of the mCNLS.

math.AP↗

arXiv2018

Riemann-Hilbert method and soliton solutions in the system of two-component Hirota equations

In this letter we examine the two-component Hirota (TH) equations which describes the pulse propagation in a coupled fiber with higher-order dispersion and self-steepening. As the TH equations is a complete integrable system, which admits a $3\times 3$ Ablowitz-Kaup-Newell-Segu(AKNS)-type Lax pair, we obtain the general N-soliton solutions of the TH equations via the Riemann-Hilbert(RH) method when the jump matrix of a specific RH problem is a $3\times3$ unit matrix. As an example, the expression of one- and two-soliton are displayed explicitly.

math.AP↗

arXiv2018

Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty}$ filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.

cs.NI↗

arXiv2018

Screening of Fungi for the Application of Self-Healing Concrete

Concrete is susceptible to cracking owing to drying shrinkage, freeze-thaw cycles, delayed ettringite formation, reinforcement corrosion, creep and fatigue, etc. Since maintenance and inspection of concrete infrastructure require onerous labor and high costs, self-healing of harmful cracks without human interference or intervention could be of great attraction. The goal of this study is to explore a new self-healing approach in which fungi are used as a self-healing agent to promote calcium carbonate precipitation to fill the cracks in concrete structures. Recent research results in the field of geomycology have shown that many species of fungi could play an important role in promoting calcium carbonate mineralization, but their application in self-healing concrete has not been reported. Therefore, a screening of different species of fungi has been conducted in this study. Our results showed that, despite the drastic pH increase owing to the leaching of calcium hydroxide from concrete, Aspergillus nidulans (MAD1445), a pH regulatory mutant, could grow on concrete plates and promote calcium carbonate precipitation.

q-bio.OT↗

arXiv2018

Superior Structural, Elastic and Electronic Properties of 2D Titanium Nitride MXenes Over Carbide MXenes: A Comprehensive First Principles Study

The structural, elastic and electronic properties of two-dimensional (2D) titanium carbide/nitride based pristine (Tin+1Cn/Tin+1Nn) and functionalized MXenes (Tin+1CnT2/Tin+1NnT2, T stands for the terminal groups: -F, -O and -OH, n = 1, 2, 3) are investigated by density functional theory calculations. Carbide-based MXenes possess larger lattice constants and monolayer thicknesses than nitride-based MXenes. The in-plane Young's moduli of Tin+1Nn are larger than those of Tin+1Cn, whereas in both systems they decrease with the increase of the monolayer thickness. Cohesive energy calculations indicate that MXenes with a larger monolayer thickness have a better structural stability. Adsorption energy calculations imply that Tin+1Nn have stronger preference to adhere to the terminal groups, which suggests more active surfaces for nitride-based MXenes. More importantly, nearly free electron states are observed to exist outside the surfaces of -OH functionalized carbide/nitride based MXenes, especially in Tin+1Nn(OH)2, which provide almost perfect transmission channels without nuclear scattering for electron transport. The overall electrical conductivity of nitride-based MXenes is determined to be higher than that of carbide-based MXenes. The exceptional properties of titanium nitride-based MXenes, including strong surface adsorption, high elastic constant and Young's modulus, and good metallic conductivity, make them promising materials for catalysis and energy storage applications.

cond-mat.mtrl-sci↗

arXiv2018

Overlapping Sliced Inverse Regression for Dimension Reduction

Sliced inverse regression (SIR) is a pioneer tool for supervised dimension reduction. It identifies the effective dimension reduction space, the subspace of significant factors with intrinsic lower dimensionality. In this paper, we propose to refine the SIR algorithm through an overlapping slicing scheme. The new algorithm, called overlapping sliced inverse regression (OSIR), is able to estimate the effective dimension reduction space and determine the number of effective factors more accurately. We show that such overlapping procedure has the potential to identify the information contained in the derivatives of the inverse regression curve, which helps to explain the superiority of OSIR. We also prove that OSIR algorithm is $\sqrt n $-consistent and verify its effectiveness by simulations and real applications.

stat.ML↗

arXiv2018

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

Recently, there emerged revived interests of designing automatic programs (e.g., using genetic/evolutionary algorithms) to optimize the structure of Convolutional Neural Networks (CNNs) for a specific task. The challenge in designing such programs lies in how to balance between large search space of the network structures and high computational costs. Existing works either impose strong restrictions on the search space or use enormous computing resources. In this paper, we study how to design a genetic programming approach for optimizing the structure of a CNN for a given task under limited computational resources yet without imposing strong restrictions on the search space. To reduce the computational costs, we propose two general strategies that are observed to be helpful: (i) aggressively selecting strongest individuals for survival and reproduction, and killing weaker individuals at a very early age; (ii) increasing mutation frequency to encourage diversity and faster evolution. The combined strategy with additional optimization techniques allows us to explore a large search space but with affordable computational costs. Our results on standard benchmark datasets (MNIST, SVHN, CIFAR-10, CIFAR-100) are competitive to similar approaches with significantly reduced computational costs.

cs.NE↗

arXiv2018

ECD: An Edge Content Delivery and Update Framework in Mobile Edge Computing

This article proposes an edge content delivery framework (ECD) based on mobile edge computing in the era of Internet of Things (IOT), to alleviate the load of the core network and improve the quality of experience (QoE) of mobile users. Considering mobile devices become both the content consumers and providers, and majority of the contents are unnecessary to be uploaded to the cloud datacenter, at the network edge, we deploy a content server to store the raw contents generated from the mobile users, and a cache pool to store the contents that are frequently requested by mobile users in the ECD. The cache pools are ranked and high ranked cache pools will store contents with higher popularity. Furthermore, we propose edge content delivery scheme and edge content update scheme, based on content popularity and cache pool ranking. The content delivery scheme is to efficiently deliver contents to mobile users, while the edge content update scheme is to mitigate the content generated by users to appropriate cache pools based on its request frequently and cache poor ranking. The edge content delivery is completely different from the content delivery network and can further reduce the load on the core network. In addition, because the top ranking cache pools are prioritized for higher priority contents and the cache pools are prioritized for higher priority contents and the cache pools are in proximity to the mobile users, the immediately interactive response between mobile users and cache pools can be achieved. A representative case study of ECD is provided and open research issues are discussed.

cs.NI↗

arXiv2018

Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning

Separating audio mixtures into individual instrument tracks has been a long standing challenging task. We introduce a novel weakly supervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution distance between the separated sources and the real sources for each individual source. Moreover, a global regularization term is added to fulfill the spectrum energy preservation property regardless separation. Unlike state-of-the-art weakly supervised models which often involve deliberately devised constraints or careful model selection, our approach need little prior model specification on the data, and can be straightforwardly learned in an end-to-end fashion. We show that the proposed method performs competitively on public benchmark against state-of-the-art weakly supervised methods.

cs.SD↗

arXiv2018

Combining Probabilistic Load Forecasts

Probabilistic load forecasts provide comprehensive information about future load uncertainties. In recent years, many methodologies and techniques have been proposed for probabilistic load forecasting. Forecast combination, a widely recognized best practice in point forecasting literature, has never been formally adopted to combine probabilistic load forecasts. This paper proposes a constrained quantile regression averaging (CQRA) method to create an improved ensemble from several individual probabilistic forecasts. We formulate the CQRA parameter estimation problem as a linear program with the objective of minimizing the pinball loss, with the constraints that the parameters are nonnegative and summing up to one. We demonstrate the effectiveness of the proposed method using two publicly available datasets, the ISO New England data and Irish smart meter data. Comparing with the best individual probabilistic forecast, the ensemble can reduce the pinball score by 4.39% on average. The proposed ensemble also demonstrates superior performance over nine other benchmark ensembles.

stat.AP↗

arXiv2018

A Linearly Convergent Majorized ADMM with Indefinite Proximal Terms for Convex Composite Programming and Its Applications

This paper aims to study a majorized alternating direction method of multipliers with indefinite proximal terms (iPADMM) for convex composite optimization problems. We show that the majorized iPADMM for 2-block convex optimization problems converges globally under weaker conditions than those used in the literature and exhibits a linear convergence rate under a local error bound condition. Based on these, we establish the linear rate convergence results for a symmetric Gaussian-Seidel based majorized iPADMM, which is designed for multi-block composite convex optimization problems. Moreover, we apply the majorized iPADMM to solve different types of regularized logistic regression problems. The numerical results on both synthetic and real datasets demonstrate the efficiency of the majorized iPADMM and also illustrate the effectiveness of the introduced indefinite proximal terms.

math.OC↗

arXiv2017

Interactions of Fungi with Concrete: Significant Importance for Bio-Based Self-Healing Concrete

The goal of this study is to explore a new self-healing concept in which fungi are used as a self-healing agent to promote calcium mineral precipitation to fill the cracks in concrete. An initial screening of different species of fungi has been conducted. Fungal growth medium was overlaid onto cured concrete plate. Mycelial discs were aseptically deposited at the plate center. The results showed that, due to the dissolving of Ca(OH)2 from concrete, the pH of the growth medium increased from its original value of 6.5 to 13.0. Despite the drastic pH increase, Trichoderma reesei (ATCC13631) spores germinated into hyphal mycelium and grew equally well with or without concrete. X-ray diffraction (XRD) and scanning electron microscope (SEM) confirmed that the crystals precipitated on the fungal hyphae were composed of calcite. These results indicate that T. reesei has great potential to be used in bio-based self-healing concrete for sustainable infrastructure.

q-bio.OT↗

arXiv2017

An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems

The sparse group Lasso is a widely used statistical model which encourages the sparsity both on a group and within the group level. In this paper, we develop an efficient augmented Lagrangian method for large-scale non-overlapping sparse group Lasso problems with each subproblem being solved by a superlinearly convergent inexact semismooth Newton method. Theoretically, we prove that, if the penalty parameter is chosen sufficiently large, the augmented Lagrangian method converges globally at an arbitrarily fast linear rate for the primal iterative sequence, the dual infeasibility, and the duality gap of the primal and dual objective functions. Computationally, we derive explicitly the generalized Jacobian of the proximal mapping associated with the sparse group Lasso regularizer and exploit fully the underlying second order sparsity through the semismooth Newton method. The efficiency and robustness of our proposed algorithm are demonstrated by numerical experiments on both the synthetic and real data sets.

math.OC↗

arXiv2017

An Efficient Multigrid Method for Ground State Solution of Bose-Einstein Condensates

An efficient multigrid method is proposed to compute the ground state solution of Bose-Einstein condensations by the finite element method based on the combination of the multigrid method for nonlinear eigenvalue problem and an efficient implementation for the nonlinear iteration. The proposed numerical method not only has the optimal convergence rate, but also has the asymptotically optimal computational work which is independent from the nonlinearity of the problem. The independence from the nonlinearity means that the asymptotic estimate of the computational work can reach almost the same as that of solving the corresponding linear boundary value problem by the multigrid method. Some numerical experiments are provided to validate the efficiency of the proposed method.

math.NA↗