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Dan Wu

Publications and source records attributed to Dan Wu.

At least 73 records · Page 4Linked to original sources

Direct methods for pseudo-relativistic Schrödinger operators

In this paper, we establish various maximal principles and develop the direct moving planes and sliding methods for equations involving the physically interesting (nonlocal) pseudo-relativistic Schrödinger operators $(-Δ+m^{2})^{s}$ with $s\in(0,1)$ and mass $m>0$. As a consequence, we also derive multiple applications of these direct methods. For instance, we prove monotonicity, symmetry and uniqueness results for solutions to various equations involving the operators $(-Δ+m^{2})^{s}$ in bounded domains, epigraph or $\mathbb{R}^{N}$, including pseudo-relativistic Schrödinger equations, 3D boson star equations and the equations with De Giorgi type nonlinearities.

math.AP↗

Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. In this work, we show that the complex mechanism of cascading failures can be well captured by training a graph convolutional network (GCN) offline. Subsequently, the search of cascading failures can be significantly accelerated with the aid of the trained GCN model. We link the power network topology with the structure of the GCN, yielding a smaller parameter space to learn the complex mechanism. We further enable the interpretability of the GCN model by a layer-wise relevance propagation (LRP) algorithm. The proposed method is tested on both the IEEE RTS-79 test system and China's Henan Province power system. The results show that the GCN guided method can not only accelerate the search of cascading failures, but also reveal the reasons for predicting the potential cascading failures.

eess.SP↗

On Distribution Patterns of Power Flow Solutions

A fundamental challenge in computer analysis of power flow is the rigorous understanding of the impact of different loading levels on the solutions of the power flow equation. This letter presents a comprehensive study of possible numerical solutions that may arise as the loading level varies. In particular, a type of "false" load flow solutions is reported for the first time as a legitimate numerical solutions but with no engineering justification. The existence and the mechanism of why this category of solutions exist are rigorously analyzed. The probability mass function of voltage solution is shown to be less dependent on loading levels. Furthermore, numbers of all actual solutions and those solutions within engineering limits are summarized. This letter presents a first attempt to compute such huge numbers of solutions to the tested systems, and analyze their distribution patterns. All solution sets investigated in this letter are posted online associated with this letter to support any further research purposes.

eess.SP↗

FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications.

eess.SP↗

Influence of Load Models on Equilibria, Stability and Algebraic Manifolds of Power System Differential-Algebraic System

Load models have a great impact on voltage behaviors as well as power system transient dynamics. Extensive work has been done on this topic, proposing appropriate load models and capturing better load behaviors during transient. This paper presents a comprehensive study to investigate the geometric and topological changes induced by different load models for the traditional power system differential-algebraic equations. Specifically, we attempt to reveal the deformation of equilibria, stability regions, and algebraic manifolds during a continuous evolution of load model. Several findings are presented in the paper, some of which countering traditional recognitions and intuitions. A major discovery is that the load model with a large proportion of constant impedance and a small proportion of constant power exhibits much more complex features than the load model with the reversed proportions of impedance and power. The increase of complexity is thoroughly recorded and investigated by the changes of geometric properties and mutations of topological invariants in the sense of equilibria, stability regions, and algebraic manifolds for the DAE system. However, most of the changes seem to occur on unstable components of algebraic manifolds or near the singular boundary surfaces, suggesting a limited variation of dynamical behaviors on the stable component.

eess.SY↗

Optimal Decision Making Model of Battery Energy Storage-Assisted Electric Vehicle Charging Station Considering Incentive Demand Response

Considering large scale implementation of electric vehicles (EVs), public EV charging stations are served as fuel tanks for EVs to meet the need of longer travelling distance and overcome the shortage of private charging piles. The allocation of local battery energy storage (BES) can enhance the flexibility of the EV charging station. This paper proposes an optimal decision making model of the BES-assisted EV charging station considering the incentive demand response. Firstly, the detailed models of the BES-assisted EV charging station are presented. Secondly, as a representative incentive demand response, the emergency demand response (EDR) model is introduced. Thirdly, based on the charging load forecast data, an optimal decision making model of the BES-assisted EV charging station considering the EDR to maximize the charging station's operating profit is established. Finally, the feasibility of the proposed method is verified through case studies. The conclusions of this paper are as follows: 1) Through the optimal decision making model, correct and profitable EDR participation decision can be determined for the BES-assisted EV charging station effectively. 2) Local BES in the EV charging station can improve the charging station's ability to participate in the EDR.

eess.SY↗

Fast Skill Learning for Variable Compliance Robotic Assembly

The robotic assembly represents a group of benchmark problems for reinforcement learning and variable compliance control that features sophisticated contact manipulation. One of the key challenges in applying reinforcement learning to physical robot is the sample complexity, the requirement of large amounts of experience for learning. We mitigate this sample complexity problem by incorporating an iteratively refitted model into the learning process through model-guided exploration. Yet, fitting a local model of the physical environment is of major difficulties. In this work, a Kalman filter is used to combine the adaptive linear dynamics with a coarse prior model from analytical description, and proves to give more accurate predictions than the existing method. Experimental results show that the proposed model fitting strategy can be incorporated into a model predictive controller to generate good exploration behaviors for learning acceleration, while preserving the benefits of model-free reinforcement learning for uncertain environments. In addition to the sample complexity, the inevitable robot overloaded during operation also tends to limit the learning efficiency. To address this problem, we present a method to restrict the largest possible potential energy in the compliance control system and therefore keep the contact force within the legitimate range.

cs.RO↗

A Holomorphic Embedding Based Continuation Method for Identifying Multiple Power Flow Solutions

In this paper, we propose an efficient continuation method for locating multiple power flow solutions. We adopt the holomorphic embedding technique to represent solution curves as holomorphic functions in the complex plane. The holomorphicity, which provides global information of the curve at any regular point, enables large step sizes in the path-following procedure such that non-singular curve segments can be traversed with very few steps. When approaching singular points, we switch to the traditional predictor-corrector routine to pass through them and switch back afterward to the holomorphic embedding routine. We also propose a warm starter when switching to the predictor-corrector routine, i.e. a large initial step size based on the poles of the Padé approximation of the derived holomorphic function, since these poles reveal the locations of singularities on the curve. Numerical analysis and experiments on many standard IEEE test cases are presented, along with the comparison to the full predictor-corrector routine, confirming the efficiency of the method.

eess.SY↗

A Telecom Perspective on the Internet of Drones: From LTE-Advanced to 5G

Drones are driving numerous and evolving use cases, and creating transformative socio-economic benefits. Drone operation needs wireless connectivity for communication between drones and ground control systems, among drones, and between drones and air traffic management systems. Mobile networks are well positioned to identify, track, and control the growing fleet of drones. The wide-area, quality, and secure connectivity provided by mobile networks can enhance the efficiency and effectiveness of drone operations beyond visual line-of-sight range. In this article, we elaborate how the drone ecosystem can benefit from mobile technologies, summarize key capabilities required by drone applications, and analyze the service requirements on mobile networks. We present field trial results collected in LTE-Advanced networks to gain insights into the capabilities of the current 4G+ networks for connected drones and share our vision on how 5G networks can further support diversified drone applications.

cs.NI↗

Preliminary result on stochastic system control theory for aperiod sample-data systems

In this paper, we obtain some preliminary results on stochastic control theory for time-varying linear systems both continuous and discrete, and further apply to aperiod sample-data linear systems. The Ito's lemma is utilized in this proposed theory, and deduced that the stability of a linear time-varying system is determined by the eigenvalues expectation of system matrix, which coincidences with the stable conditions for time-invariant system, i.e. Hurwitz for continuous systems or inside the unit circle for discrete systems. The control method for aperiod time-invariant sample-data system is also derived. It is shown that the stable condition is determined by the expectation of the sample-interval but the up-bound and the aperiod interval can be arbitrarily large even infinity. To verify the efficiency of our theory, serval experiments are demonstrated in the final of the paper.

eess.SY↗

Exceed Improved Efficient Perovskite Solar Cells Under Dual-Irradiation System

In general, perovskite solar cells (PSC) with a sensitized or thin film architecture absorb light from a single illumination. This paper reported a PSC architecture with a semitransparent Au/ITO counter electrode, which allows light to pass it partially. When the device was illuminated simultaneously from both the FTO and Au/ITO sides, the PSC has achieved an overall power conversion efficiency (PCE) as high as 20.1% under high light intensity (1.4 sun), which is much higher than that of the single-irradiation system.

physics.app-ph↗

MFDL: A Multicarrier Fresnel Penetration Model based Device-Free Localization System leveraging Commodity Wi-Fi Cards

Device-free localization plays an important role in many ubiquitous applications. Among the different technologies proposed, Wi-Fi based technology using commercial devices has attracted much attention due to its low cost, ease of deployment, and high potential for accurate localization. Existing solutions use either fingerprints that require labor-intensive radio-map survey and updates, or models constructed from empirical studies with dense deployment of Wi-Fi transceivers. In this work, we explore the Fresnel Zone Theory in physics and propose a generic Fresnel Penetration Model (FPM), which reveals the linear relationship between specific Fresnel zones and multicarrier Fresnel phase difference, along with the Fresnel phase offset caused by static multipath environments. We validate FPM in both outdoor and complex indoor environments. Furthermore, we design a multicarrier FPM based device-free localization system (MFDL), which overcomes a number of practical challenges, particularly the Fresnel phase difference estimation and phase offset calibration in multipath-rich indoor environments. Extensive experimental results show that compared with the state-of-the-art work (LiFS), our MFDL system achieves better localization accuracy with much fewer number of Wi-Fi transceivers. Specifically, using only three transceivers, the median localization error of MFDL is as low as 45$cm$ in an outdoor environment of 36$m^2$, and 55$cm$ in indoor settings of 25$m^2$. Increasing the number of transceivers to four allows us to achieve 75$cm$ median localization error in a 72$m^2$ indoor area, compared with the 1.1$m$ median localization error achieved by LiFS using 11 transceivers in a 70$m^2$ area.

cs.NI↗

Small-Signal Analysis of the Microgrid Secondary Control Considering a Communication Time Delay

This paper presents a small-signal analysis of an islanded microgrid composed of two or more voltage source inverters connected in parallel. The primary control of each inverter is integrated through internal current and voltage loops using PR compensators, a virtual impedance, and an external power controller based on frequency and voltage droops. The frequency restoration function is implemented at the secondary control level, which executes a consensus algorithm that consists of a load-frequency control and a single time delay communication network. The consensus network consists of a time-invariant directed graph and the output power of each inverter is the information shared among the units, which is affected by the time delay. The proposed small-signal model is validated through simulation results and experimental results. A root locus analysis is presented that shows the behavior of the system considering control parameters and time delay variation.

eess.SY↗

Kernel principal component analysis network for image classification

In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed. First, mapping the data into higher space with kernel principal component analysis to make the data linearly separable. Then building a two-layer KPCANet to obtain the principal components of image. Finally, classifying the principal components with linearly classifier. Experimental results show that the proposed KPCANet is effective in face recognition, object recognition and hand-writing digits recognition, it also outperforms principal component analysis network (PCANet) generally as well. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.

cs.LG↗

Bilingual Terminology Extraction Using Multi-level Termhood

Purpose: Terminology is the set of technical words or expressions used in specific contexts, which denotes the core concept in a formal discipline and is usually applied in the fields of machine translation, information retrieval, information extraction and text categorization, etc. Bilingual terminology extraction plays an important role in the application of bilingual dictionary compilation, bilingual Ontology construction, machine translation and cross-language information retrieval etc. This paper addresses the issues of monolingual terminology extraction and bilingual term alignment based on multi-level termhood. Design/methodology/approach: A method based on multi-level termhood is proposed. The new method computes the termhood of the terminology candidate as well as the sentence that includes the terminology by the comparison of the corpus. Since terminologies and general words usually have differently distribution in the corpus, termhood can also be used to constrain and enhance the performance of term alignment when aligning bilingual terms on the parallel corpus. In this paper, bilingual term alignment based on termhood constraints is presented. Findings: Experiment results show multi-level termhood can get better performance than existing method for terminology extraction. If termhood is used as constrain factor, the performance of bilingual term alignment can be improved.

cs.CL↗

Existence and stability of standing waves for nonlinear fractional Schrödinger equations with Hartree type nonlinearity

In this paper, we consider the nonlinear fractional Schrödinger equations with Hartree type nonlinearity. We obtain the existence of standing waves by studying the related constrained minimization problems by applying the concentration-compactness principle. Via symmetric decreasing rearrangements, we also show that the standing waves, up to a translations and phases, are positive symmetric nonincreasing functions. Moreover, we prove that the set of minimizers is a stable set for the initial value problem of the equations, that is, a solution whose initial data is near the set will remain near it for all time.

math.AP↗

Search for quantum electronic dipoles in the dimerized κ-(BEDT-TTF)2Cu[N(CN)2]Cl salt

The Mott insulator κ-(BEDT-TTF)2Cu[N(CN)2]Cl consists of molecular dimers arranged on an anisotropic triangular lattice and develops a canted antiferromagnetic ground state. It has recently been suggested that this system features purely electronic ferroelectricity which requires an electric dipole moment. Optical spectroscopy clearly rules out charge imbalance in this system, which excludes the existence of quantum electric dipoles on the dimers and subsequently a dipolar spin coupling. We suggest that the prominent in-plane dielectric response in κ-(BEDT-TTF)2Cu[N(CN)2]Cl is due to short-range discommensurations of the antiferromagnetic phase in the temperature range 30 < T < 50 K, and domain wall relaxations at lower temperatures.

cond-mat.str-el↗