arXiv subjects

Na Li

Publications and source records attributed to Na Li.

At least 289 records · Page 16Linked to original sources

arXiv2017

An automatic water detection approach based on Dempster-Shafer theory for multi spectral images

Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification. However, due to the similarity of the spectra of water bodies, built-up areas, approaches based on high-resolution satellites sometimes confuse these features. A popular direction to detect water is spectral index, often requiring the ground truth to find appropriate thresholds manually. As for traditional machine learning methods, they identify water merely via differences of spectra of various land covers, without taking specific properties of spectral reflection into account. In this paper, we propose an automatic approach to detect water bodies based on Dempster-Shafer theory, combining supervised learning with specific property of water in spectral band in a fully unsupervised context. The benefits of our approach are twofold. On the one hand, it performs well in mapping principle water bodies, including little streams and branches. On the other hand, it labels all objects usually confused with water as `ignorance', including half-dry watery areas, built-up areas and semi-transparent clouds and shadows. `Ignorance' indicates not only limitations of the spectral properties of water and supervised learning itself but insufficiency of information from multi-spectral bands as well, providing valuable information for further land cover classification.

cs.AI↗

arXiv2017

Distributed Regularized Primal-Dual Method: Convergence Analysis and Trade-offs

We study deterministic and stochastic primal-dual sub-gradient algorithms for distributed optimization of a separable objective function with global inequality constraints. In both algorithms, the norm of the Lagrangian multipliers are controlled by augmenting the corresponding Lagrangian function with a quadratic regularization term. Specifically, we show that when the stepsize of each algorithm satisfies a certain restriction, the norm of the Lagrangian multipliers is upper bounded by an expression that is inversely proportional to the parameter of the regularization. We use this result to compute upper bounds on the sub-gradients of the Lagrangian function. For the deterministic algorithm, we prove a convergence rate for attaining the optimal objective value. In the stochastic optimization case, we similarly prove convergence rates both in the expectation and with a high probability, using the method of bounded martingale difference. For both algorithms, we demonstrate a trade-off between the convergence rate and the decay rate of the constraint violation, in the sense that improving the convergence rate slows the decay rate of the constraint violation and vice versa. We demonstrate the convergence of our proposed algorithms numerically for distributed regression with the hinge and logistic loss functions over different graph structures.

math.OC↗

arXiv2017

Harnessing Smoothness to Accelerate Distributed Optimization

There has been a growing effort in studying the distributed optimization problem over a network. The objective is to optimize a global function formed by a sum of local functions, using only local computation and communication. Literature has developed consensus-based distributed (sub)gradient descent (DGD) methods and has shown that they have the same convergence rate $O(\frac{\log t}{\sqrt{t}})$ as the centralized (sub)gradient methods (CGD) when the function is convex but possibly nonsmooth. However, when the function is convex and smooth, under the framework of DGD, it is unclear how to harness the smoothness to obtain a faster convergence rate comparable to CGD's convergence rate. In this paper, we propose a distributed algorithm that, despite using the same amount of communication per iteration as DGD, can effectively harnesses the function smoothness and converge to the optimum with a rate of $O(\frac{1}{t})$. If the objective function is further strongly convex, our algorithm has a linear convergence rate. Both rates match the convergence rate of CGD. The key step in our algorithm is a novel gradient estimation scheme that uses history information to achieve fast and accurate estimation of the average gradient. To motivate the necessity of history information, we also show that it is impossible for a class of distributed algorithms like DGD to achieve a linear convergence rate without using history information even if the objective function is strongly convex and smooth.

math.OC↗

arXiv2017

Groups of Repairmen and Repair-based Load Balancing in Supermarket Models with Repairable Servers

Supermarket models are a class of interesting parallel queueing networks with dynamic randomized load balancing and real-time resource management. When the parallel servers are subject to breakdowns and repairs, analysis of such a supermarket model becomes more difficult and challenging. In this paper, we apply the mean-field theory to studying four interrelated supermarket models with repairable servers, and numerically indicate impact of the different repairman groups on performance of the systems. First, we set up the systems of mean-field equations for the supermarket models with repairable servers. Then we prove the asymptotic independence of the supermarket models through the operator semi-group and the mean-field limit. Furthermore, we show that the fixed points of the supermarket models satisfy the systems of nonlinear equations. Finally, we use the fixed points to give numerical computation for performer analysis, and provide valuable observations on model improvement. Therefore, this paper provides a new and effective method in the study of complex supermarket models.

cs.PF↗

arXiv2017

Stochastic Primal-Dual Method on Riemannian Manifolds with Bounded Sectional Curvature

We study a stochastic primal-dual method for constrained optimization over Riemannian manifolds with bounded sectional curvature. We prove non-asymptotic convergence to the optimal objective value. More precisely, for the class of hyperbolic manifolds, we establish a convergence rate that is related to the sectional curvature lower bound. To prove a convergence rate in terms of sectional curvature for the elliptic manifolds, we leverage Toponogov's comparison theorem. In addition, we provide convergence analysis for the asymptotically elliptic manifolds, where the sectional curvature at each given point on manifold is locally bounded from below by the distance function. We demonstrate the performance of the primal-dual algorithm on the sphere for the non-negative principle component analysis (PCA). In particular, under the non-negativity constraint on the principle component and for the symmetric spiked covariance model, we empirically show that the primal-dual approach outperforms the spectral method. We also examine the performance of the primal-dual method for the anchored synchronization from partial noisy measurements of relative rotations on the Lie group SO(3). Lastly, we show that the primal-dual algorithm can be applied to the weighted MAX-CUT problem under constraints on the admissible cut. Specifically, we propose different approximation algorithms for the weighted MAX-CUT problem based on optimizing a function on the manifold of direct products of the unit spheres as well as the manifold of direct products of the rotation groups.

math.OC↗

arXiv2017

Distributed Temperature Control via Geothermal Heat Pump Systems in Energy Efficient Buildings

Geothermal Heat Pump (GHP) systems are heating and cooling systems that use the ground as the temperature exchange medium. GHP systems are becoming more and more popular in recent years due to their high efficiency. Conventional control schemes of GHP systems are mainly designed for buildings with a single thermal zone. For large buildings with multiple thermal zones, those control schemes either lose efficiency or become costly to implement requiring a lot of real-time measurement, communication and computation. In this paper, we focus on developing energy efficient control schemes for GHP systems in buildings with multiple zones. We present a thermal dynamic model of a building equipped with a GHP system for floor heating/cooling and formulate the GHP system control problem as a resource allocation problem with the objective to maximize user comfort in different zones and to minimize the building energy consumption. We then propose real-time distributed algorithms to solve the control problem. Our distributed multi-zone control algorithms are scalable and do not need to measure or predict any exogenous disturbances such as the outdoor temperature and indoor heat gains. Thus, it is easy to implement them in practice. Simulation results demonstrate the effectiveness of the proposed control schemes.

eess.SY↗

arXiv2017

Decentralized and Distributed Temperature Control via HVAC Systems in Energy Efficient Buildings

In this paper, we design real-time decentralized and distributed control schemes for Heating Ventilation and Air Conditioning (HVAC) systems in energy efficient buildings. The control schemes balance user comfort and energy saving, and are implemented without measuring or predicting exogenous disturbances. Firstly, we introduce a thermal dynamic model of building systems and formulate a steady-state resource allocation problem, which aims to minimize the aggregate deviation between zone temperatures and their set points, as well as the building energy consumption, subject to practical operating constraints, by adjusting zone flow rates. Because this problem is nonconvex, we propose two methods to (approximately) solve it and to design the real-time control. In the first method, we present a convex relaxation approach to solve an approximate version of the steady-state optimization problem, where the heat transfer between neighboring zones is ignored. We prove the tightness of the relaxation and develop a real-time decentralized algorithm to regulate the zone flow rate. In the second method, we introduce a mild assumption under which the original optimization problem becomes convex, and then a real-time distributed algorithm is developed to regulate the zone flow rate. In both cases, the thermal dynamics can be driven to equilibria which are optimal solutions to those associated steady-state optimization problems. Finally, numerical examples are provided to illustrate the effectiveness of the designed control schemes.

eess.SY↗

arXiv2017

An Integrated Design of Optimization and Physical Dynamics for Energy Efficient Buildings: A Passivity Approach

In this paper, we address energy management for heating, ventilation, and air-conditioning (HVAC) systems in buildings, and present a novel combined optimization and control approach. We first formulate a thermal dynamics and an associated optimization problem. An optimization dynamics is then designed based on a standard primal-dual algorithm, and its strict passivity is proved. We then design a local controller and prove that the physical dynamics with the controller is ensured to be passivity-short. Based on these passivity results, we interconnect the optimization and physical dynamics, and prove convergence of the room temperatures to the optimal ones defined for unmeasurable disturbances. Finally, we demonstrate the present algorithms through simulation.

eess.SY↗

arXiv2016

Passivity-Based Distributed Optimization with Communication Delays Using PI Consensus Algorithm

In this paper, we address a class of distributed optimization problems in the presence of inter-agent communication delays based on passivity. We first focus on unconstrained distributed optimization and provide a passivity-based perspective for distributed optimization algorithms. This perspective allows us to handle communication delays while using scattering transformation. Moreover, we extend the results to constrained distributed optimization, where it is shown that the problem is solved by just adding one more feedback loop of a passive system to the solution of the unconstrained ones. We also show that delays can be incorporated in the same way as the unconstrained problems. Finally, the algorithm is applied to a visual human localization problem using a pedestrian detection algorithm.

eess.SY↗

arXiv2016

Revealing plasma oscillation in THz spectrum from laser plasma of molecular jet

Contribution of plasma oscillation to the broadband terahertz emission is revealed by interacting two-color laser pulses with a supersonic jet of nitrogen molecules. Temporal and spectral shifts of THz waves are observed as the plasma density varies. The former owes to the changing refractive index of the THz waves, and the latter correlates to the varying plasma frequency. Simulation of considering photocurrents, plasma oscillation and decaying plasma density explains the broadband THz spectrum and the varying THz spectrum. Plasma oscillation only contributes to THz waves at low plasma density owing to negligible plasma absorption. At the longer medium or higher density, the combining effects of plasma oscillation and absorption results in the observed low-frequency broadband THz spectra.

physics.optics↗

arXiv2016

Real-time Decentralized and Robust Voltage Control in Distribution Networks

Voltage control plays an important role in the operation of electricity distribution networks, especially when there is a large penetration of renewable energy resources. In this paper, we focus on voltage control through reactive power compensation and study how different information structures affect the control performance. In particular, we first show that using only voltage measurements to determine reactive power compensation is insufficient to maintain voltage in the acceptable range. Then we propose two fully decentralized and robust algorithms by adding additional information, which can stabilize the voltage in the acceptable range. The one with higher complexity can further minimize a cost of reactive power compensation in a particular form. Both of the two algorithms use only local measurements and local variables and require no communication. In addition, the two algorithms are robust against heterogeneous update rates and delays.

math.OC↗

arXiv2016

Dynamical Decentralized Voltage Control of Multi-Terminal HVDC Grids

High-voltage direct current (HVDC) is a commonly used technology for long-distance electric power transmission, mainly due to its low resistive losses. When connecting multiple HVDC lines into a multi-terminal HVDC (MTDC) system, several challenges arise. To ensure safe and efficient operation of MTDC systems, the voltage of all terminals need to be steered to within an operational range. In this paper we study the commonly used decentralized voltage droop controller, and show that it in general does not steer the voltages to within the operational range. We propose a decentralized PI controller with deadband, and show that it always steers the voltages to within the operational range regardless of the loads. Additionally we show that the proposed controller inherits the property of proportional power sharing from the droop controller, provided that both the loads and the line resistances are sufficiently low. The results are validated through simulation in MATLAB.

math.OC↗

arXiv2016

Electron motion enhanced high harmonic generation in xenon clusters

Atomic clusters presents an isolated system that models the bulk materials whose mechanism of HHG remains uncertain, and a promising medium to produce HHG beyond the limited conversion efficiency for gaseous atoms. Here we reveal that the oscillation of collective electron motion within clusters develops after the interaction of intense laser fields, and it significantly enhances the harmonic dipole and increases the quantum phase of the harmonics. Experimentally, the phase matching conditions of HHG from nanometer xenon clusters and atoms are distinguished, which confirms the enhanced internal field that was proposed theoretically a decade ago. The separation of HHG from atoms and clusters allows the determination of the amplitude of the HHG for clusters to be 5 orders higher, corresponding to 4 times higher conversion efficiency for atomic response. The finding provides an insight on the HHG mechanism of bulk materials and a means by which an efficient coherent X-ray source can be developed.

physics.optics↗

arXiv2016

Reward Processes and Performance Simulation in Supermarket Models with Different Servers

Supermarket models with different servers become a key in modeling resource management of stochastic networks, such as, computer networks, manufacturing systems and transportation networks. While these different servers always make analysis of such a supermarket model more interesting, difficult and challenging. This paper provides a new novel method for analyzing the supermarket model with different servers through a multi-dimensional continuous-time Markov reward processes. Firstly, the utility functions are constructed for expressing a routine selection mechanism that depends on queue lengths, on service rates, and on some probabilities of individual preference. Then applying the continuous-time Markov reward processes, some segmented stochastic integrals of the random reward function are established by means of an event-driven technique. Based on this, the mean of the random reward function in a finite time period is effectively computed by means of the state jump points of the Markov reward process, and also the mean of the discounted random reward function in an infinite time period can be calculated through the same event-driven technique. Finally, some simulation experiments are given to indicate how the expected queue length of each server depends on the main parameters of this supermarket model.

cs.PF↗

arXiv2016

Distributed Resource Allocation Using One-Way Communication with Applications to Power Networks

Typical coordination schemes for future power grids require two-way communications. Since the number of end power-consuming devices is large, the bandwidth requirements for such two-way communication schemes may be prohibitive. Motivated by this observation, we study distributed coordination schemes that require only one-way limited communications. In particular, we investigate how dual descent distributed optimization algorithm can be employed in power networks using one-way communication. In this iterative algorithm, system coordinators broadcast coordinating (or pricing) signals to the users/devices who update power consumption based on the received signal. Then system coordinators update the coordinating signals based on the physical measurement of the aggregate power usage. We provide conditions to guarantee the feasibility of the aggregated power usage at each iteration so as to avoid blackout. Furthermore, we prove the convergence of algorithms under these conditions, and establish its rate of convergence. We illustrate the performance of our algorithms using numerical simulations. These results show that one-way limited communication may be viable for coordinating/operating the future smart grids.

eess.SY↗

arXiv2016

Robustness Analysis for an Online Decentralized Descent Power allocation algorithm

As independent service providers shift from conventional energy to renewable energy sources, the power distribution system will likely experience increasingly significant fluctuation in supply, given the uncertain and intermittent nature of renewable sources like wind and solar energy. These fluctuations in power generation, coupled with time-varying consumer demands of electricity and the massive scale of power distribution networks present the need to not only design real-time decentralized power allocation algorithms, but also characterize how effective they are given fast-changing consumer demands and power generation capacities. In this paper, we present an Online Decentralized Dual Descent (OD3) power allocation algorithm and determine (in the worst case) how much of observed social welfare and price volatility can be explained by fluctuations in generation capacity and consumer demand. Convergence properties and performance guarantees of the OD3 algorithm are analyzed by characterizing the difference between the online decision and the optimal decision. The theoretical results in the paper are validated and illustrated by numerical experiments using real data.

eess.SY↗

arXiv2015

Spin Manipulation by Creation of Single-Molecule Radical Cations

All-trans-retinoic acid (ReA), a closed-shell organic molecule comprising only C, H, and O atoms, is investigated on a Au(111) substrate using scanning tunneling microscopy and spectroscopy. In dense arrays single ReA molecules are switched to a number of states, three of which carry a localized spin as evidenced by conductance spectroscopy in high magnetic fields. The spin of a single molecule may be reversibly switched on and off without affecting its neighbors. We suggest that ReA on Au is readily converted to a radical by the abstraction of an electron.

cond-mat.mtrl-sci↗

arXiv2015

A Class of Linear-Quadratic-Gaussian (LQG) Mean-Field Game (MFG) of Stochastic Delay Systems

This paper investigates the linear-quadratic-Gaussian (LQG) mean-field game (MFG) for a class of stochastic delay systems. We consider a large population system in which the dynamics of each player satisfies some forward stochastic differential delay equation (SDDE). The consistency condition or Nash certainty equivalence (NCE) principle is established through an auxiliary mean-field system of anticipated forward-backward stochastic differential equation with delay (AFBSDDE). The wellposedness of such consistency condition system can be further established by some continuation method instead the classical fixed-point analysis. Thus, the consistency condition maybe given on arbitrary time horizon. The decentralized strategies are derived which are shown to satisfy the $ε$-Nash equilibrium property. Two special cases of our MFG for delayed system are further investigated.

math.OC↗