Robust sliding mode design for uncertain stochastic systems based on H∞ control method

The official published version can be found at the link below.In this paper, the design problem of sliding mode control (SMC) is addressed for uncertain stochastic systems modeled by Itô differential equations. There exist the parameter uncertainties in both the state and input matrices, as well as the unmatched external disturbance. The key feature of this work is the integration of SMC method with H∞ technique such that the robust stochastic stability with a prescribed disturbance attenuation level can be achieved. A sufficient condition for the existence of the desired sliding mode controller is obtained via linear matrix inequalities. The reachability of the specified sliding surface is proven. Finally, a numerical simulation example is presented to illustrate the proposed method.This work was funded by The Royal Society of the U.K.;NNSF of China. Grant Numbers: 60674015, 60674089;The Technology Innovation Key Foundation of Shanghai Municipal Education Commission. Grant Number: 09ZZ60;Shanghai Leading Academic Discipline Project. Grant Number: B50

Orientation-dependent deformation mechanisms of bcc niobium nanoparticles

Nanoparticles usually exhibit pronounced anisotropic properties, and a close insight into the atomic-scale deformation mechanisms is of great interest. In present study, atomic simulations are conducted to analyze the compression of bcc nanoparticles, and orientation-dependent features are addressed. It is revealed that surface morphology under indenter predominantly governs the initial elastic response. The loading curve follows the flat punch contact model in [110] compression, while it obeys the Hertzian contact model in [111] and [001] compressions. In plastic deformation regime, full dislocation gliding is dominated in [110] compression, while deformation twinning is prominent in [111] compression, and these two mechanisms coexist in [001] compression. Such deformation mechanisms are distinct from those in bulk crystals under nanoindentation and nanopillars under compression, and the major differences are also illuminated. Our results provide an atomic perspective on the mechanical behaviors of bcc nanoparticles and are helpful for the design of nanoparticle-based components and systems.Comment: 21 pages, 11 figure

A model to simulate the spreading of oil and gas in underwater oil spills

The rapid growth of offshore oil production and undersea oil delivery pipelines increases the risk of\ud underwater oil spill. In this study, a model based on the Lagrangian particle tracking method is developed to simulate\ud the spreading of oil and gas in an underwater oil spill, which is helpful to estimate the environmental impact and to find\ud effective measures for preventing the spreading of oil. The oil droplets and gas bubbles released from the leakage point\ud are modeled by a large number of representative particles, which are divided into several groups to simulate different\ud components of oil and gas leaked from the underwater blowout. The movement of each particle in one time step\ud includes two components, a mean movement and a random walk. The mean movement is computed by combining the\ud effect of surrounding marine hydrodynamic, the buoyant jet flow near the leakage point and the rise velocity of\ud representative oil droplets or gas bubbles.The random walk method is used to simulate the turbulent diffusion. The\ud compressibility and dissolution of gas are also considered, which play an important role in deepwater. Comparing with\ud the previous models for underwater oil spill based on the integral Lagrangian control volume method, the present model\ud is more flexible in simulating the crude oil which has complex components. The model is validated by several\ud experiment cases and successfully applied to simulate the DeepSpill field expreiment, and good agreement between the\ud calculation and the observation is obtained. The fractionation of different gas bubbles or oil droplets is considered and\ud significant differences in the underwater distribution of oil droplets and gas bubbles with different sizes are clearly\ud shown in the simulated results

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending on whether training data is available, prior research has proposed both unsupervised distance-based schemes and supervised deep learning schemes for sentence matching. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interactions between them. In this paper, we propose Hierarchical Sentence Factorization---a technique to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a "predicate-argument" form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page

Electron Depletion Due to Bias of a T-Shaped Field-Effect Transistor

A T-shaped field-effect transistor, made out of a pair of two-dimensional electron gases, is modeled and studied. A simple numerical model is developed to study the electron distribution vs. applied gate voltage for different gate lengths. The model is then improved to account for depletion and the width of the two-dimensional electron gases. The results are then compared to the experimental ones and to some approximate analytical calculations and are found to be in good agreement with them.Comment: 16 pages, LaTex (RevTex), 8 fig