arXiv subjects

Pengfei Wu

Publications and source records attributed to Pengfei Wu.

25 records · Page 2Linked to original sources

arXiv2023

Underwater Sound Speed Profile Construction: A Review

Real--time and accurate construction of regional sound speed profiles (SSP) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affect the signal propagation modes such as trajectory. In this paper, we summarizes and analyzes the current research status in the field of underwater SSP construction, and the mainstream methods include direct SSP measurement and SSP inversion. In the direct measurement method, we compare the performance of popular international commercial temperature, conductivity, and depth profilers (CTD). While for the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) for constructing SSP are introduced, and their advantages and disadvantages are compared. The traditional direct measurement method has good accuracy performance, but it usually takes a long time. The proposal of SSP inversion method greatly improves the convenience and real--time performance, but the accuracy is not as good as the direct measurement method. Currently, the SSP inversion relies on sonar observation data, making it difficult to apply to areas that couldn't be covered by underwater observation systems, and these methods are unable to predict the distribution of sound velocity at future times. How to comprehensively utilize multi-source data and provide elastic sound velocity distribution estimation services with different accuracy and real-time requirements for underwater users without sonar observation data is the mainstream trend in future research on SSP construction.

eess.SP↗

arXiv2023

A Safe DRL Method for Fast Solution of Real-Time Optimal Power Flow

High-level penetration of intermittent renewable energy sources (RESs) has introduced significant uncertainties into modern power systems. In order to rapidly and economically respond to the fluctuations of power system operating state, this paper proposes a safe deep reinforcement learning (SDRL) based method for fast solution of real-time optimal power flow (RT-OPF) problems. The proposed method considers the volatility of RESs and temporal constraints, and formulates the RT-OPF as a Constrained Markov Decision Process (CMDP). In the training process, the proposed method hybridizes the proximal policy optimization (PPO) and the primal-dual method. Instead of integrating the constraint violation penalty with the reward function, its actor gradients are estimated by a Lagrange advantage function which is derived from two critic systems based on economic reward and violation cost. The decoupling of reward and cost alleviates reward sparsity while improving critic approximation accuracy. Moreover, the introduction of Lagrange multipliers enables the agent to comprehend the trade-off between optimality and feasibility. Numerical tests are carried out and compared with penalty-based DRL methods on the IEEE 9-bus, 30-bus, and 118-bus test systems. The results show that the well-trained SDRL agent can significantly improve the computation efficiency while satisfying the security constraints and optimality requirements.

eess.SY↗

arXiv2023

Atmospheric turbulence does not change the degree of polarization of vector beams

We propose a novel theoretical framework to demonstrate vector beams whose degree of polarization does not change on atmospheric propagation. Inspired by the Fresnel equations, we derive the reflective and refractive field of vector beams propagating through a phase screen by employing the continuity of electromagnetic field. We generalize the conventional split-step beam propagation method by considering the vectorial properties in the vacuum diffraction and the refractive properties of a single phase screen. Based on this vectorial propagation model, we extensively calculate the change of degree of polarization (DOP) of vector beams under different beam parameters and turbulence parameters both in free-space and satellite-mediated links. Our result is that whatever in the free-space or satellite-mediated regime, the change of DOP mainly fluctuates around the order of $10^{-13}$ to $10^{-6}$, which is almost negligible.

physics.optics↗

arXiv2022

FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining

Tables store rich numerical data, but numerical reasoning over tables is still a challenge. In this paper, we find that the spreadsheet formula, which performs calculations on numerical values in tables, is naturally a strong supervision of numerical reasoning. More importantly, large amounts of spreadsheets with expert-made formulae are available on the web and can be obtained easily. FORTAP is the first method for numerical-reasoning-aware table pretraining by leveraging large corpus of spreadsheet formulae. We design two formula pretraining tasks to explicitly guide FORTAP to learn numerical reference and calculation in semi-structured tables. FORTAP achieves state-of-the-art results on two representative downstream tasks, cell type classification and formula prediction, showing great potential of numerical-reasoning-aware pretraining.

cs.IR↗

arXiv2021

Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech

In expressive speech synthesis, there are high requirements for emotion interpretation. However, it is time-consuming to acquire emotional audio corpus for arbitrary speakers due to their deduction ability. In response to this problem, this paper proposes a cross-speaker emotion transfer method that can realize the transfer of emotions from source speaker to target speaker. A set of emotion tokens is firstly defined to represent various categories of emotions. They are trained to be highly correlated with corresponding emotions for controllable synthesis by cross-entropy loss and semi-supervised training strategy. Meanwhile, to eliminate the down-gradation to the timbre similarity from cross-speaker emotion transfer, speaker condition layer normalization is implemented to model speaker characteristics. Experimental results show that the proposed method outperforms the multi-reference based baseline in terms of timbre similarity, stability and emotion perceive evaluations.

eess.AS↗

arXiv2019

Lightning-Fast and Privacy-Preserving Outsourced Computation in the Cloud

In this paper, we propose a framework for lightning-fast privacy-preserving outsourced computation framework in the cloud, which we refer to as LightCom. Using LightCom, a user can securely achieve the outsource data storage and fast secure data processing in a single cloud server different from the existing multi-server outsourced computation model. Specifically, we first present a general secure computation framework for LightCom under the cloud server equipped with multiple Trusted Processing Units (TPUs) which face the side-channel attack. Under the LightCom, we design two specified fast processing toolkits which allow the user to achieve the commonly-used secure integer computation and secure floating-point computation against the side-channel information leakage of TPUs, respectively. Furthermore, our LightCom can also guarantee access pattern protection during the data processing and achieve user private information retrieve after the computation. We prove that the proposed LightCom can successfully achieve the goal of single cloud outsourced data processing to avoid the extra computation server and trusted computation server, and demonstrate the utility and the efficiency of LightCom using simulations.

cs.CR↗

arXiv2019

Learning Deep Representations by Mutual Information for Person Re-identification

Most existing person re-identification (ReID) methods have good feature representations to distinguish pedestrians with deep convolutional neural network (CNN) and metric learning methods. However, these works concentrate on the similarity between encoder output and ground-truth, ignoring the correlation between input and encoder output, which affects the performance of identifying different pedestrians. To address this limitation, We design a Deep InfoMax (DIM) network to maximize the mutual information (MI) between the input image and encoder output, which doesn't need any auxiliary labels. To evaluate the effectiveness of the DIM network, we propose end-to-end Global-DIM and Local-DIM models. Additionally, the DIM network provides a new solution for cross-dataset unsupervised ReID issue as it needs no extra labels. The experiments prove the superiority of MI theory on the ReID issue, which achieves the state-of-the-art results.

cs.CV↗