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Chen Wang

Publications and source records attributed to Chen Wang.

At least 757 records · Page 42Linked to original sources
arXiv2019

Voltage Security Analysis of VSC-HVDC Transmission Lines

Due to generation retirements and growth of renewable energy sources integration, there will be widespread changes in the real and reactive power flow in nowadays power systems. These changes also bring about challenges on system operation and stability. High voltage direct current (HVDC) technology seems to be a promising solution to these new challenges. A study on the impact of the replacement of 500KV AC transmission lines by VSC-HVDC transmission line on system voltage security is conducted. The analysis is based on reactive load margin method and implemented on one of the Dominion Energy planning models. Different control schemes of HVDC are considered. Using k-means clustering method, three representative zones within the network are selected. The results corresponding to them are demonstrated and discussed. It is shown that HVDC lines with P-V control remarkably improve system voltage security, while those with P-PF control scheme have negative effects.

eess.SY↗
arXiv2019

Multi-layer Attention Mechanism for Speech Keyword Recognition

As an important part of speech recognition technology, automatic speech keyword recognition has been intensively studied in recent years. Such technology becomes especially pivotal under situations with limited infrastructures and computational resources, such as voice command recognition in vehicles and robot interaction. At present, the mainstream methods in automatic speech keyword recognition are based on long short-term memory (LSTM) networks with attention mechanism. However, due to inevitable information losses for the LSTM layer caused during feature extraction, the calculated attention weights are biased. In this paper, a novel approach, namely Multi-layer Attention Mechanism, is proposed to handle the inaccurate attention weights problem. The key idea is that, in addition to the conventional attention mechanism, information of layers prior to feature extraction and LSTM are introduced into attention weights calculations. Therefore, the attention weights are more accurate because the overall model can have more precise and focused areas. We conduct a comprehensive comparison and analysis on the keyword spotting performances on convolution neural network, bi-directional LSTM cyclic neural network, and cyclic neural network with the proposed attention mechanism on Google Speech Command datasets V2 datasets. Experimental results indicate favorable results for the proposed method and demonstrate the validity of the proposed method. The proposed multi-layer attention methods can be useful for other researches related to object spotting.

cs.LG↗
arXiv2019

Robust Target-relative Localization with Ultra-Wideband Ranging and Communication

In this paper we propose a method to achieve relative positioning and tracking of a target by a quadcopter using Ultra-wideband (UWB) ranging sensors, which are strategically installed to help retrieve both relative position and bearing between the quadcopter and target. To achieve robust localization for autonomous flight even with uncertainty in the speed of the target, two main features are developed. First, an estimator based on Extended Kalman Filter (EKF) is developed to fuse UWB ranging measurements with data from onboard sensors including inertial measurement unit (IMU), altimeters and optical flow. Second, to properly handle the coupling of the target's orientation with the range measurements, UWB based communication capability is utilized to transfer the target's orientation to the quadcopter. Experiment results demonstrate the ability of the quadcopter to control its position relative to the target autonomously in both cases when the target is static and moving.

eess.SY↗
arXiv2019

Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition

Comprehensive quality-aware automated semantic web service composition is an NP-hard problem, where service composition workflows are unknown, and comprehensive quality, i.e., Quality of services (QoS) and Quality of semantic matchmaking (QoSM) are simultaneously optimized. The objective of this problem is to find a solution with optimized or near-optimized overall QoS and QoSM within polynomial time over a service request. In this paper, we proposed novel memetic EDA-based approaches to tackle this problem. The proposed method investigates the effectiveness of several neighborhood structures of composite services by proposing domain-dependent local search operators. Apart from that, a joint strategy of the local search procedure is proposed to integrate with a modified EDA to reduce the overall computation time of our memetic approach. To better demonstrate the effectiveness and scalability of our approach, we create a more challenging, augmented version of the service composition benchmark based on WSC-08 \cite{bansal2008wsc} and WSC-09 \cite{kona2009wsc}. Experimental results on this benchmark show that one of our proposed memetic EDA-based approach (i.e., MEEDA-LOP) significantly outperforms existing state-of-the-art algorithms.

cs.AI↗
arXiv2019

Optimal efficiency and power and their trade-off in three-terminal quantum thermoelectric engines with two output electric currents

We establish a theory of optimal efficiency and power for three-terminal thermoelectric engines which have two independent output electric currents and one input heat current. This set-up goes beyond the conventional heat engines with only one output electric current. For such a set-up, we derive the optimal efficiency and power and their trade-off for three-terminal heat engines with and without time-reversal symmetry. The formalism goes beyond the known optimal efficiency and power for systems with or without time-reversal symmetry, showing interesting features that have not been revealed before. A concrete example of quantum-dot heat engine is studied to show that the current set-up can have much improved efficiency and power compared with previous set-ups with only one output electric current. Our analytical results also apply for thermoelectric heat engines with multiple output electric currents, providing an alternative scheme toward future high-performance thermoelectric materials.

cond-mat.mes-hall↗
arXiv2019

Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and different definitions of imperceptible perturbations bring unique challenges and opportunities for the adversarial attacks and defenses for graph data. In this paper, we propose both attack and defense techniques. For attack, we show that the discreteness problem could easily be resolved by introducing integrated gradients which could accurately reflect the effect of perturbing certain features or edges while still benefiting from the parallel computations. For defense, we observe that the adversarially manipulated graph for the targeted attack differs from normal graphs statistically. Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations. Our experiments on a number of datasets show the effectiveness of the proposed methods.

cs.LG↗
arXiv2019

Quantum Zeno effect in the multimode quantum Rabi model

We study the quantum Zeno effect (QZE) and quantum anti-Zeno effect (QAZE) of the multimode quantum Rabi model(MQRM). We derive an analytic expression for the decay rate of the survival probability where cavity modes are initially prepared as thermal equilibrium states. A crossover from QZE to QAZE is observed due to the energy backflow induced by high frequency cavity modes. In addition, we apply a numerically exact method based on the thermofield dynamics(TFD) theory and the matrix product states(MPS) to study the effect of squeezing of the cavity modes on the QZE of the MQRM. The influence of the squeezing angle, squeezing strength and temperature on the decay rate of the survival probability are discussed.

quant-ph↗
arXiv2019

On some conjectural congruences

In this paper, we confirm some congruences conjectured by V.J.W. Guo and M.J. Schlosser recently. For example, we show that for primes $p>3$, $$ \sum_{k=0}^{p-1}(2pk-2k-1)\frac{\left(\frac{-1}{p-1}\right)_k^{2p-2}}{(k!)^{2p-2}}\equiv0\pmod{p^5}. $$

math.NT↗
arXiv2019

Constraining mixing in massive stars in the Small Magellanic Cloud

Context. The evolution of massive stars is strongly influenced by internal mixing processes such as semiconvection, convective core overshooting, and rotationally induced mixing. None of these is currently well constrained. Aims. We investigate models for massive stars in the Small Magellanic Cloud (SMC). We aim to constrain the various mixing efficiencies by comparing model results to observations. Methods. We use the stellar evolution code MESA to compute more than 60 grids of detailed evolutionary models for stars with initial masses of 9 to 100 Msol, in each grid assuming different combinations of mixing efficiencies. Results. We find that for most of the combinations of the mixing efficiencies, models in a wide mass range spend core-helium burning either only as blue supergiants, or only as red supergiants. The latter case corresponds to models that maintain a shallow slope of the hydrogen/helium (H/He) gradient separating the core and the envelope of the models. Only a small part of the mixing parameter space leads to models that produce a significant number of blue and red supergiants, which both exist abundantly in the SMC. Interestingly, these models contain steep H/He gradients, as is required to understand the hot, hydrogen-rich Wolf-Rayet stars in the SMC. We find that unless it is very fast, rotation has a limited effect on the H/He profiles in our models. Conclusions. While we use specific implementations of the considered mixing processes, they comprehensively probe the two first order structural parameters: the core mass and the H/He gradient in the core-envelope interface. Our results imply that, in massive stars, the H/He gradients above the helium cores become very steep. Our model grids can be tested with future observational surveys of the massive stars in the SMC, and thereby help to considerably reduce the uncertainties in models of massive star evolution.

astro-ph.SR↗
arXiv2019

Unifying quantum heat transfer and superradiant signature in a nonequilibrium collective-qubit system: a polaron-transformed Redfield approach

We investigate full counting statistics of quantum heat transfer in a collective-qubit system, constructed by multi-qubits interacting with two thermal baths. The nonequilibrium polaron-transformed Redfield approach embedded with an auxiliary counting field is applied to obtain the steady state heat current and fluctuations, which enables us to study the impact of the qubit-bath interaction in a wide regime. The heat current, current noise and skewness are all found to clearly unify the limiting results in the weak and strong couplings, respectively. Moreover, the superradiant heat transfer is clarified as a system-size-dependent effect, and large number of qubits dramatically suppresses the nonequilibrium superradiant signature.

quant-ph↗
arXiv2019

Thermal rectification and heat amplification in a nonequilibrium V-type three-level system

Thermal rectification and heat amplification are investigated in a nonequilibrium V-type three-level system with quantum interference. By applying the Redfield master equation combined with full counting statistics, we analyze the steady state heat transport. The noise-induced interference is found to be able to rectify the heat current, which paves a new way to design quantum thermal rectifier. Within the three-reservoir setup, the heat amplification is clearly identified far-from equilibrium, which is in absence of the negative differential thermal conductance.

quant-ph↗
arXiv2019

Strong system-bath coupling induces negative differential thermal conductance and heat amplification in nonequilibrium two-qubits systems

Quantum heat transfer is analyzed in nonequilibrium two-qubits systems by applying the nonequilibrium polaron-transformed Redfield equation combined with full counting statistics. Steady state heat currents with weak and strong qubit-bath couplings are clearly unified. Within the two-terminal setup, the negative differential thermal conductance is unraveled with strong qubit-bath coupling and finite qubit splitting energy. The partially strong spin-boson interaction is sufficient to show the negative differential thermal conductance. Based on the three-terminal setup, that two-qubits are asymmetrically coupled to three thermal baths, a giant heat amplification factor is observed with strong qubit-bath coupling. Moreover, the strong interaction of either the left or right spin-boson coupling is able to exhibit the apparent heat amplification effect.

quant-ph↗
arXiv2019

Touchdown on the Cloud: The impact of the Super Bowl on Cloud

The Super Bowl is the world's biggest televised sporting event. We examine the impact of the increasing online activities during the Super Bowl of year 2015-2017 on various types of Cloud systems, including the Cloud infrastructure service, the Cloud content delivery networks for live streaming, and popular web services such as on-demand video streaming service and social network applications. We probe these systems from agents deployed around the world to compare their load variations and performance changes during and after the game. Through our studies of three consecutive years, we find that Super Bowl events have impacts on the Cloud. However, the current Cloud system is still able to ensure capacity to cope with the challenges brought by traffic changes during such massive events.

cs.NI↗
arXiv2019

Evolutionary Multitasking for Semantic Web Service Composition

Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-optimized functional quality (i.e., Quality of Semantic Matchmaking) or non-functional quality (i.e., Quality of Service) or both due to the complexity of this problem. With a significant increase in service composition requests, many composition requests have similar input and output requirements but may vary due to different preferences from different user segments. This problem is often treated as a multi-objective service composition so as to cope with different preferences from different user segments simultaneously. Without taking a multi-objective approach that gives rise to a solution selection challenge, we perceive multiple similar service composition requests as jointly forming an evolutionary multi-tasking problem in this work. We propose an effective permutation-based evolutionary multi-tasking approach that can simultaneously generate a set of solutions, with one for each service request. We also introduce a neighborhood structure over multiple tasks to allow newly evolved solutions to be evaluated on related tasks. Our proposed method can perform better at the cost of only a fraction of time, compared to one state-of-art single-tasking EC-based method. We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.

cs.AI↗
arXiv2019

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to extract pixel-wise dense feature embedding, from which the pose is estimated. Furthermore, we integrate an end-to-end iterative pose refinement procedure that further improves the pose estimation while achieving near real-time inference. Our experiments show that our method outperforms state-of-the-art approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed method to a real robot to grasp and manipulate objects based on the estimated pose.

cs.CV↗
arXiv2019

Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition

The rigid registration of two 3D point sets is a fundamental problem in computer vision. The current trend is to solve this problem globally using the BnB optimization framework. However, the existing global methods are slow for two main reasons: the computational complexity of BnB is exponential to the problem dimensionality (which is six for 3D rigid registration), and the bound evaluation used in BnB is inefficient. In this paper, we propose two techniques to address these problems. First, we introduce the idea of translation invariant vectors, which allows us to decompose the search of a 6D rigid transformation into a search of 3D rotation followed by a search of 3D translation, each of which is solved by a separate BnB algorithm. This transformation decomposition reduces the problem dimensionality of BnB algorithms and substantially improves its efficiency. Then, we propose a new data structure, named 3D Integral Volume, to accelerate the bound evaluation in both BnB algorithms. By combining these two techniques, we implement an efficient algorithm for rigid registration of 3D point sets. Extensive experiments on both synthetic and real data show that the proposed algorithm is three orders of magnitude faster than the existing state-of-the-art global methods.

cs.CV↗
arXiv2018

Proof of a congruence concerning truncated hypergeometric series ${}_6F_5$

In this paper, we mainly prove the following congruence conjectured by J.-C. Liu: $$ {}_6F_5\bigg[\begin{matrix}\frac{5}{4}&\frac{1}{2}&\frac{1}{2}&\frac{1}{2}&\frac{1}{2}&\frac{1}{2}\\&\frac{1}{4}&1&1&1&1\end{matrix}\bigg|\ -1\bigg]_{\frac{p-1}{2}}\equiv-\frac{p^3}{16}Γ_p\left(\frac{1}{4}\right)^4\pmod{p^5}, $$ where $p\geq5$ are primes with $p\equiv3\pmod{4}$.

math.NT↗