arXiv subjects

Lei Yang

Publications and source records attributed to Lei Yang.

At least 451 records · Page 25Linked to original sources

arXiv2019

Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning

In this paper, a biologically-inspired adaptive intelligent secondary controller is developed for microgrids to tackle system dynamics uncertainties, faults, and/or disturbances. The developed adaptive biologically-inspired controller adopts a novel computational model of emotional learning in mammalian limbic system. The learning capability of the proposed biologically-inspired intelligent controller makes it a promising approach to deal with the power system non-linear and volatile dynamics without increasing the controller complexity, and maintain the voltage and frequency stabilities by using an efficient reference tracking mechanism. The performance of the proposed intelligent secondary controller is validated in terms of the voltage and frequency absolute errors in the simulated microgrid. Simulation results highlight the efficiency and robustness of the proposed intelligent controller under the fault conditions and different system uncertainties compared to other benchmark controllers.

eess.SY↗

arXiv2019

Divergent trajectories under diagonal geodesic flow and splitting of discrete subgroups of $\mathrm{SO}(n,1) \times \mathrm{SO}(n,1)$

Let $H = \mathrm{SO}(n,1)$ and $A = \{a(t): t \in \mathbb{R}\}$ be a maximal $\mathbb{R}$-split Cartan subgroup of $H$. Let $Γ\subset H \times H$ be a nonuniform lattice in $H \times H$ and $X_Γ : = H \times H/ Γ$. Let $A_2 : = \{ a_2(t):=a(t) \times a(t) : t \in \mathbb{R}\} \subset A\times A$ on $X_Γ$ and $\mathcal{D}_Γ\subset X_Γ$ denote the collection of points $x \in X_Γ$ such that $a_2(t)x$ diverges as $t \rightarrow +\infty$. In this note, we will show that if the Hausdorff dimension of $\mathcal{D}_Γ$ is greater than $\dim (H\times H) - 2(n-1)$, then $Γ$ is essentially split, namely, it contains a subgroup of finite index of form $Γ_1 \times Γ_2 $, where $Γ_1$ and $Γ_2$ are both lattices in $H$.

math.DS↗

arXiv2019

Badly approximable points on manifolds and unipotent orbits in homogeneous spaces

In this paper, we study the weighted $n$-dimensional badly approximable points on manifolds. Given a $C^n$ differentiable non-degenerate submanifold $\mathcal{U} \subset \mathbb{R}^n$, we will show that any countable intersection of the sets of the weighted badly approximable points on $\mathcal{U}$ has full Hausdorff dimension. This strengthens a result of Beresnevich by removing the condition on weights and weakening the smoothness condition on manifolds. Compared to the work of Beresnevich, our approach relies on homogeneous dynamics. It turns out that in order to solve this problem, it is crucial to study the distribution of long pieces of unipotent orbits in homogeneous spaces. The proof relies on the linearization technique and representations of $\mathrm{SL}(n+1,\mathbb{R})$.

math.NT↗

arXiv2019

On the Performance Gain of NOMA over OMA in Uplink Communication Systems

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by $γ$ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to $M$-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of $M$ for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, $M$, and the number of users, $K$, the ESG of NOMA over OMA increases linearly with both $M$ and $K$. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

cs.IT↗

arXiv2019

Using the Modified Nearest Neighbor Method to Correct Fiber-collision Effects on Galaxy Clustering

Fiber collision is a persistent problem faced by modern spectroscopic galaxy surveys. In this work, we propose a new method to correct for this undesired effect, focusing on the clustering from the fiber-collision scale up to $\lesssim 10 \rm Mpc\,h^{-1}$. We assume that the fiber-collided galaxies are in association with their nearest three angular neighbors. Compared with the conventional nearest-neighbor method, we have properly accounted for the foreground (background) galaxies that are associated with the foreground (background) cosmic webs relative to the nearest neighbor. We have tested the new method with mock catalogs of the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7). The test demonstrates that our new method can recover the projected two-point correlation functions at an accuracy better than 1\% on small (below the fiber-collision scale) to intermediate (i.e., $10 \rm Mpc\,h^{-1}$) scales, where the fiber collision takes effect and the SDSS main sample can probe. The new method also gives a better recovery of the redshift-space correlation functions almost on all scales that we are interested in.

astro-ph.CO↗

arXiv2019

On the Performance Gain of NOMA over OMA in Uplink Single-cell Systems

In this paper, we investigate the performance gain of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink single-cell systems. In both single-antenna and multi-antenna scenarios, the performance gain of NOMA over OMA in terms of asymptotic ergodic sum-rate is analyzed for a sufficiently large number of users. In particular, in single-antenna systems, we identify two types of near-far gains brought by NOMA: 1) the large-scale near-far gain via exploiting the large-scale fading increases with the cell size; 2) the small-scale near-far gain via exploiting the small-scale fading is a constant given by $γ= 0.57721$ nat/s/Hz in Rayleigh fading channels. Furthermore, we have analyzed that the performance gain achieved by single-antenna NOMA can be amplified via increasing the number of antennas equipped at the base station due to the extra spatial degrees of freedom. The numerical results confirm the accuracy of the derived analyses and unveil the performance gains of NOMA over OMA in different scenarios.

cs.IT↗

arXiv2019

Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search

A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific dataset? Recently, several Neural Architecture Search (NAS) frameworks have been developed that use reinforcement learning and evolutionary algorithm to search for the solution. However, most of them take a long time to find the optimal architecture due to the huge search space and the lengthy training process needed to evaluate each candidate. In addition, most of them aim at accuracy only and do not take into consideration the hardware that will be used to implement the architecture. This will potentially lead to excessive latencies beyond specifications, rendering the resulting architectures useless. To address both issues, in this paper we use Field Programmable Gate Arrays (FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely FNAS, which will provide an optimal neural architecture with latency guaranteed to meet the specification. In addition, with a performance abstraction model to analyze the latency of neural architectures without training, our framework can quickly prune architectures that do not satisfy the specification, leading to higher efficiency. Experimental results on common data set such as ImageNet show that in the cases where the state-of-the-art generates architectures with latencies 7.81x longer than the specification, those from FNAS can meet the specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to 11.13x speedup for the search process. To the best of the authors' knowledge, this is the very first hardware aware NAS.

cs.DC↗

arXiv2019

Enhanced Quasi-Maximum Likelihood Decoding of Short LDPC Codes based on Saturation

In this paper, we propose an enhanced quasi-maximum likelihood (EQML) decoder for LDPC codes with short block lengths. After the failure of the conventional belief propagation (BP) decoding, the proposed EQML decoder selects unreliable variable nodes (VNs) and saturates their associated channel output values to generate a list of decoder input sequences. Each decoder input sequence in the list is then decoded by the conventional BP decoder to obtain the most likely codeword. To improve the accuracy of selecting unreliable VNs, we propose an edge-wise selection method based on the sign fluctuation of VNs' extrinsic messages. A partial pruning stopping (PPS) rule is also presented to reduce the decoding latency. Simulation results show that the proposed EQML decoder outperforms the conventional BP decoder and the augmented BP decoder for short LDPC codes. It even approaches the performance of ML decoding within 0.3 dB in terms of frame error rate. In addition, the proposed PPS rule achieves a lower decoding latency compared to the list decoding stopping rule.

cs.IT↗

arXiv2019

RPC: A Large-Scale Retail Product Checkout Dataset

Over recent years, emerging interest has occurred in integrating computer vision technology into the retail industry. Automatic checkout (ACO) is one of the critical problems in this area which aims to automatically generate the shopping list from the images of the products to purchase. The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products. Despite its significant practical and research value, this problem is not extensively studied in the computer vision community, largely due to the lack of a high-quality dataset. To fill this gap, in this work we propose a new dataset to facilitate relevant research. Our dataset enjoys the following characteristics: (1) It is by far the largest dataset in terms of both product image quantity and product categories. (2) It includes single-product images taken in a controlled environment and multi-product images taken by the checkout system. (3) It provides different levels of annotations for the check-out images. Comparing with the existing datasets, ours is closer to the realistic setting and can derive a variety of research problems. Besides the dataset, we also benchmark the performance on this dataset with various approaches. The dataset and related resources can be found at \url{https://rpc-dataset.github.io/}.

cs.CV↗

arXiv2018

Rebooting Ultrasonic Positioning Systems for Ultrasound-incapable Smart Devices

An ultrasonic Positioning System (UPS) has outperformed RF-based systems in terms of its accuracy for years. However, few of the developed solutions have been deployed in practice to satisfy the localization demand of today's smart devices, which lack ultrasonic sensors and were considered as being `deaf' to ultrasound. A recent finding demonstrates that ultrasound may be audible to the smart devices under certain conditions due to their microphone's nonlinearity. Inspired by this insight, this work revisits the ultrasonic positioning technique and builds a practical UPS, called UPS+ for ultrasound-incapable smart devices. The core concept is to deploy two types of indoor beacon devices, which will advertise ultrasonic beacons at two different ultrasonic frequencies respectively. Their superimposed beacons are shifted to a low-frequency by virtue of the nonlinearity effect at the receiver's microphone. This underlying property functions as an implicit ultrasonic downconverter without throwing harm to the hearing system of humans. We demonstrate UPS+, a fully functional UPS prototype, with centimeter-level localization accuracy using custom-made beacon hardware and well-designed algorithms.

cs.NI↗

arXiv2018

A Comparison of CP-OFDM, PCC-OFDM and UFMC for 5G Uplink Communications

Polynomial-cancellation-coded orthogonal frequency division multiplexing (PCC-OFDM) is a form of OFDM that has waveforms which are very well localized in both the time and frequency domains and so it is ideally suited for use in the 5G network. This paper analyzes the performance of PCC-OFDM in the uplink of a multiuser system using orthogonal frequency division multiple access (OFDMA) and compares it with conventional cyclic prefix OFDM (CP-OFDM), and universal filtered multicarrier (UFMC). PCC-OFDM is shown to be much less sensitive than either CP-OFDM or UFMC to time and frequency offsets. For a given constellation size, PCC-OFDM in additive white Gaussian noise (AWGN) requires 3dB lower signal-to-noise ratio (SNR) for a given bit-error-rate, and the SNR advantage of PCC-OFDM increases rapidly when there are timing and/or frequency offsets. For PCC-OFDM no frequency guard band is required between different OFDMA users. PCC-OFDM is completely compatible with CP-OFDM and adds negligible complexity and latency, as it uses a simple mapping of data onto pairs of subcarriers at the transmitter, and a simple weighting-and-adding of pairs of subcarriers at the receiver. The weighting and adding step, which has been omitted in some of the literature, is shown to contribute substantially to the SNR advantage of PCC-OFDM. A disadvantage of PCC-OFDM (without overlapping) is the potential reduction in spectral efficiency because subcarriers are modulated in pairs, but this reduction is more than regained because no guard band or cyclic prefix is required and because, for a given channel, larger constellations can be used.

eess.SP↗

arXiv2018

Joint Trajectory and Resource Allocation Design for UAV Communication Systems

In this paper, we investigate resource allocation design for unmanned aerial vehicle (UAV)-enabled communication systems, where a UAV is dispatched to provide communications to multiple user nodes. Our objective is to maximize the communication system throughput by jointly optimizing the subcarrier allocation policy and the trajectory of the UAV, while taking into account the minimum required data rate for each user node, no-fly zones (NFZs), the maximum UAV cruising speed, and initial/final UAV locations. The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a locally optimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm approaches closely to that of the system without NFZ. In addition, the proposed algorithm can achieve a significant throughput gain compared to various benchmark schemes.

cs.IT↗

arXiv2018

TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation

For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions. Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task. Although many deep neural network structures and techniques have been applied to improve the accuracy, few have paid attention to better differentiating the easily confused classes. In this paper, we propose TreeSegNet which adopts an adaptive network to increase the classification rate at the pixelwise level. Specifically, based on the infrastructure of DeepUNet, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively according to the confusion matrix and the proposed TreeCutting algorithm. By transporting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS 2D semantic labeling Potsdam dataset, the results obtained by TreeSegNet are better than those of other published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.

cs.CV↗

arXiv2018

Feature Fusion through Multitask CNN for Large-scale Remote Sensing Image Segmentation

In recent years, Fully Convolutional Networks (FCN) has been widely used in various semantic segmentation tasks, including multi-modal remote sensing imagery. How to fuse multi-modal data to improve the segmentation performance has always been a research hotspot. In this paper, a novel end-toend fully convolutional neural network is proposed for semantic segmentation of natural color, infrared imagery and Digital Surface Models (DSM). It is based on a modified DeepUNet and perform the segmentation in a multi-task way. The channels are clustered into groups and processed on different task pipelines. After a series of segmentation and fusion, their shared features and private features are successfully merged together. Experiment results show that the feature fusion network is efficient. And our approach achieves good performance in ISPRS Semantic Labeling Contest (2D).

cs.CV↗

arXiv2018

Mapping the Real Space Distributions of Galaxies in SDSS DR7: II. Measuring the growth rate, clustering amplitude of matter and biases of galaxies at redshift $0.1$

We extend the real-space mapping method developed in Shi et at. (2016) so that it can be applied to flux-limited galaxy samples. We use an ensemble of mock catalogs to demonstrate the reliability of this extension, showing that it allows for an accurate recovery of the real-space correlation functions and galaxy biases. We also demonstrate that, using an iterative method applied to intermediate-scale clustering data, we can obtain an unbiased estimate of the growth rate of structure $fσ_8$, which is related to the clustering amplitude of matter, to an accuracy of $\sim 10\%$. Applying this method to the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7), we construct a real-space galaxy catalog spanning the redshift range $0.01 \leq z \leq 0.2$, which contains 584,473 galaxies in the north Galactic cap (NGC). Using this data, we infer $\fss$ at a median redshift $z=0.1$, which is consistent with the WMAP9 cosmology at the $1σ$ level. By combining this measurement with the real-space clustering of galaxies and with galaxy-galaxy weak lensing measurements for the same sets of galaxies, we are able to break the degeneracy between $f$, $σ_8$, and $b$. From the SDSS DR7 data alone, we obtain the following cosmological constraints at redshift $z=0.1$: $f=$$0.464^{+0.040}_{-0.040}$, $σ_8=0.769^{+0.121}_{-0.089}$, and $b=1.910^{+0.234}_{-0.268}$, $1.449^{+0.194}_{-0.196}$, $1.301^{+0.170}_{-0.177}$, and $1.196^{+0.159}_{-0.161}~$ for galaxies within different absolute magnitude bins $^{0.1}{\rm M}_r-5\log h=[-23,0, -22.0], [-22,0, -21.0], [-21.0, -20.0]$ and $[-20.0, -19.0]$, respectively.

astro-ph.CO↗

arXiv2018

A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems

In this paper we consider a general matrix factorization model which covers a large class of existing models with many applications in areas such as machine learning and imaging sciences. To solve this possibly nonconvex, nonsmooth and non-Lipschitz problem, we develop a non-monotone alternating updating method based on a potential function. Our method essentially updates two blocks of variables in turn by inexactly minimizing this potential function, and updates another auxiliary block of variables using an explicit formula. The special structure of our potential function allows us to take advantage of efficient computational strategies for non-negative matrix factorization to perform the alternating minimization over the two blocks of variables. A suitable line search criterion is also incorporated to improve the numerical performance. Under some mild conditions, we show that the line search criterion is well defined, and establish that the sequence generated is bounded and any cluster point of the sequence is a stationary point. Finally, we conduct some numerical experiments using real datasets to compare our method with some existing efficient methods for non-negative matrix factorization and matrix completion. The numerical results show that our method can outperform these methods for these specific applications.

math.OC↗

arXiv2018

Reliability-Based Windowed Decoding for Spatially-Coupled LDPC Codes

In this letter, we propose a reliability-based windowed decoding scheme for spatially-coupled (SC) low-density parity-check (LDPC) codes. To mitigate the error propagation along the sliding windowed decoder of the SC LDPC codes, a partial message reservation (PMR) method is proposed where only the reliable messages generated in the previous decoding window are reserved for the next decoding window. We also propose a partial syndrome check (PSC) stopping rule for each decoding window, in which only the complete VNs are checked. Simulation results show that our proposed scheme significantly improves the error floor performance compared to the sliding windowed decoder with the conventional weighted bit-flipping (WBF) algorithm.

cs.IT↗

arXiv2018

ELUCID V. Lighting dark matter halos with galaxies

In a recent study, using the distribution of galaxies in the north galactic pole of SDSS DR7 region enclosed in a 500$\mpch$ box, we carried out our ELUCID simulation (Wang et al. 2016, ELUCID III). Here we {\it light} the dark matter halos and subhalos in the reconstructed region in the simulation with galaxies in the SDSS observations using a novel {\it neighborhood} abundance matching method. Before we make use of thus established galaxy-subhalo connections in the ELUCID simulation to evaluate galaxy formation models, we set out to explore the reliability of such a link. For this purpose, we focus on the following a few aspects of galaxies: (1) the central-subhalo luminosity and mass relations; (2) the satellite fraction of galaxies; (3) the conditional luminosity function (CLF) and conditional stellar mass function (CSMF) of galaxies; and (4) the cross correlation functions between galaxies and the dark matter particles, most of which are measured separately for all, red and blue galaxy populations. We find that our neighborhood abundance matching method accurately reproduces the central-subhalo relations, satellite fraction, the CLFs and CSMFs and the biases of galaxies. These features ensure that thus established galaxy-subhalo connections will be very useful in constraining galaxy formation processes. And we provide some suggestions on the three levels of using the galaxy-subhalo pairs for galaxy formation constraints. The galaxy-subhalo links and the subhalo merger trees in the SDSS DR7 region extracted from our ELUCID simulation are available upon request.

astro-ph.GA↗