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Weisheng Hu

Publications and source records attributed to Weisheng Hu.

At least 37 records · Page 2Linked to original sources
arXiv2023

Understanding the power-law nature of participation in community sports organizations

The improvement of living standards and awareness of chronic diseases have increased the importance of community sports organizations in promoting the physical activity levels of the public. However, limited understanding of human behavior in this context often leads to suboptimal resource utilization. In this study, we analyzed the participation behavior of 2,956 members with a time span of 6 years in a community sports organization. Our study reveals that, at the population level, the participation frequency in activities adheres to a power-law distribution. To understand the underlying mechanisms driving crowd participation, we introduce a novel behavioral model called HFBI (Habit-Formation and Behavioral Inertia), demonstrating a robust fit to the observed power-law distribution. The habit formation mechanism indicates that individuals who are more engaged are more likely to maintain participation, while the behavioral inertia mechanism suggests that individuals' willingness to participate in activities diminishes with their absences from activities. At the individual level, our analysis reveals a burst-quiet participation pattern, with bursts often commencing with incentive activities. We also find a power-law distribution in the intervals between individual participations. Our research offers valuable insights into the complex dynamics of human participation in community sports activity and provides a theoretical foundation to inform intervention design. Furthermore, the flexibility of our model enables its application to other data exhibiting power-law properties, broadening its potential impact beyond the realm of community sports.

cs.SI↗
arXiv2022

Four-dimensional direct detection with Jones space optical full-field recovery

Data centers, the engines of the global Internet, are supported by massive high-speed optical interconnects. In optical fiber communication, the classic direct detection obtains only the intensity of the optical field, while the coherent detection counterpart utilizes both phase and polarization diversities at the expense of beating with a narrow-linewidth and high-stable local oscillator (LO). Herein, we propose and demonstrate a four-dimensional Jones space optical field recovery (4-D JSFR) scheme without LO. The information encoded on the intensity and phase of both polarizations can be captured by the polarization-diversity full-field receiver structure and subsequently extracted through deep neural network-aided field recovery. It achieves similar electrical spectral efficiency as standard intradyne coherent detection. The fully recovered optical field can extend the transmission distance beyond the power fading limitation induced by fiber chromatic dispersion. Furthermore, the LO-free advantage makes 4-D JSFR suitable for monolithic photonic integration, offering a spectrally efficient and cost-effective candidate for large-scale data center applications. Our results could motivate a fundamental paradigm shift in the optical field recovery theory and future optical transceiver design.

physics.optics↗
arXiv2022

Fast predicting the complex nonlinear dynamics of mode-locked fiber laser by a recurrent neural network with prior information feeding

As an imperative method of investigating the internal mechanism of femtosecond lasers, traditional femtosecond laser modeling relies on the split-step Fourier method (SSFM) to iteratively resolve the nonlinear Schrodinger equation suffering from the large computation complexity. To realize inverse design and optimization of femtosecond lasers, numerous simulations of mode-locked fiber lasers with different cavity settings are required further highlighting the time-consuming problem induced by the large computation complexity. Here, a recurrent neural network is proposed to realize fast and accurate femtosecond mode-locked fiber laser modeling for the first time. The generalization over different cavity settings is achieved via our proposed prior information feeding method. With the acceleration of GPU, the mean time of the artificial intelligence (AI) model inferring 500 roundtrips is less than 0.1 s. Even on an identical CPU-based hardware platform, the AI model is still 6 times faster than the SSFM method. The proposed AI-enabled method is promising to become a standard approach to femtosecond laser modeling.

physics.optics↗
arXiv2022

Optical Field Recovery in Jones Space

Optical full-field recovery makes it possible to compensate for fiber impairments such as chromatic dispersion and polarization mode dispersion (PMD) in the digital signal processing. For cost-sensitive short-reach optical networks, some advanced single-polarization (SP) optical field recovery schemes are recently proposed to avoid chromatic dispersion-induced power fading effect, and improve the spectral efficiency for larger potential capacity. Polarization division multiplexing (PDM) can further double both the spectral efficiency and the system capacity of these SP carrier-assisted direct detection (DD) schemes. However, the so-called polarization fading phenomenon induced by random polarization rotation is a fundamental obstacle which prevents SP carrier-assisted DD systems from polarization diversity. In this paper, we propose a receiver of Jones-space field recovery (JSFR) to realize polarization diversity with SP carrier-assisted DD schemes in Jones space. Different receiver structures and simplified recovery procedures for JSFR are explored theoretically. The proposed JSFR pushes the SP DD schemes towards PDM without extra optical signal-to-noise ratio (OSNR) penalty. In addition, the JSFR shows good tolerance to PMD since the optical field recovery is conducted before polarization recovery. In the concept-of-proof experiment, we demonstrate 448-Gb/s reception over 80-km single-mode fiber using the proposed JSFR based on 22 couplers. Furthermore, we qualitatively compare the optical field recovery in Jones space and Stokes space from the perspective of the modulation dimension. Qualitatively, we compare the optical field recovery in the Jones space and Stokes space from the perspective of the modulation dimension.

eess.SP↗
arXiv2022

A Grey-box Launch-profile Aware Model for C+L Band Raman Amplification

Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression. Higher accuracy, less data requirements and lower computational complexity are demonstrated through simulations compared with the pure NN-based method.

cs.LG↗
arXiv2022

Physics-informed EDFA Gain Model Based on Active Learning

We propose a physics-informed EDFA gain model based on the active learning method. Experimental results show that the proposed modelling method can reach a higher optimal accuracy and reduce ~90% training data to achieve the same performance compared with the conventional method.

eess.SP↗
arXiv2022

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schrödinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.

physics.optics↗
arXiv2022

Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme

The modeling of optical wave propagation in optical fiber is a task of fast and accurate solving the nonlinear Schrödinger equation (NLSE), and can enable the optical system design, digital signal processing verification and fast waveform calculation. Traditional waveform modeling of full-time and full-frequency information is the split-step Fourier method (SSFM), which has long been regarded as challenging in long-haul wavelength division multiplexing (WDM) optical fiber communication systems because it is extremely time-consuming. Here we propose a linear-nonlinear feature decoupling distributed (FDD) waveform modeling scheme to model long-haul WDM fiber channel, where the channel linear effects are modelled by the NLSE-derived model-driven methods and the nonlinear effects are modelled by the data-driven deep learning methods. Meanwhile, the proposed scheme only focuses on one-span fiber distance fitting, and then recursively transmits the model to achieve the required transmission distance. The proposed modeling scheme is demonstrated to have high accuracy, high computing speeds, and robust generalization abilities for different optical launch powers, modulation formats, channel numbers and transmission distances. The total running time of FDD waveform modeling scheme for 41-channel 1040-km fiber transmission is only 3 minutes versus more than 2 hours using SSFM for each input condition, which achieves a 98% reduction in computing time. Considering the multi-round optimization by adjusting system parameters, the complexity reduction is significant. The results represent a remarkable improvement in nonlinear fiber modeling and open up novel perspectives for solution of NLSE-like partial differential equations and optical fiber physics problems.

eess.SP↗
arXiv2021

An Interpretable Mapping from a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization

In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.

eess.SP↗
arXiv2020

A Data-Fusion-Assisted Telemetry Layer for Autonomous Optical Networks

For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, collecting, storing and processing a large size of data are challenging tasks. Moreover, strong correlations and similarities between data from different sources and regions are not properly considered, which may limit function extension and accuracy improvement. To address abovementioned issues, a data-fusion-assisted telemetry layer between the physical layer and control layer is proposed in this paper. The data fusion methodologies are elaborated on three different levels: Source Level, Space Level and Model Level. For each level, various data fusion algorithms are introduced and relevant works are reviewed. In addition, proof-of-concept use cases for each level are provided through simulations, where the benefits of the data-fusion-assisted telemetry layer are shown.

eess.SP↗
arXiv2020

Experimental demonstration of multimode microresonator sensing by machine learning

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wideband transmission spectra that consist of multiple resonant modes. It is different from the previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range.

physics.app-ph↗
arXiv2019

Application of Machine Learning in Fiber Nonlinearity Modeling and Monitoring for Elastic Optical Networks

Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and investigated separately. Moreover, the accuracy of the previously proposed methods still needs to be improved for heterogenous dynamic optical networks. In this paper, we present the application of machine learning (ML) in NLI modeling and monitoring. In particular, we first propose to use ML approaches to calibrate the errors of current fiber nonlinearity models. The Gaussian-noise (GN) model is used as an illustrative example, and significant improvement is demonstrated with the aid of an artificial neural network (ANN). Further, we propose to use ML to combine the modeling and monitoring schemes for a better estimation of NLI variance. The following contents are the listed errors as mentioned in the comments for reasons of withdrawal. (1) The works, as mentioned as the title, should be addressed is about the elastic optical networks(EON), however, the simulation setup and the results section are focused on the conventional wavelength division multiplexing(WDM) networks. This error may confuse some researcher, getting the misleading decision for the researches about the elastic optical networks. (2) There exists some errors in the results rection, such as, Fig.9(b) and (c) with the wrong captions may result in misleading decision. (3) The split-step-Fourier-method(SSFM) presents good accuracy if the sufficiently small steps are adopted in the calculation, however this paper has not necessary contents and efforts to optimise the step-length of SSFM. This error may confuse the accuracy of simulation results. Therefore, we decide to withdraw this paper from arXiv. The correct and complete paper with the same title was published in journal of lightwave technology with doi: 10.1109/JLT.2019.2910143.

eess.SP↗
arXiv2019

First insight into the regime transition dynamics of mode-locked fibre lasers

Mode-locked lasers exhibit complex nonlinear dynamics. Precise observation of these dynamics will aid in understanding of the underlying physics and provide new insights for laser design and applications. The starting dynamics, from initial noise fluctuations to the mode-locking regime, have previously been observed directly by time-stretched transform-based real-time spectroscopy. However, the regime transition dynamics, which are essential processes in mode-locked lasers, have not yet been resolved because regime transition process tracking is very challenging. Here we demonstrate the first insight into the regime transition dynamics enabled by our design of a real-time programmable mode-locked fibre laser, in which different operating regimes can be achieved and switched automatically. The regime transition dynamics among initial noise fluctuations, Q-switching, fundamental mode-locking and harmonic mode-locking regimes have been observed and thoroughly analysed by both temporal and spectral means. These findings will enrich our understanding of the complex dynamics inside mode-locked lasers.

physics.optics↗
arXiv2018

Real-time programmable mode-locked fiber laser with a human-like algorithm

Nonlinear polarized evolution-based passively mode-locked fiber lasers with ultrafast and high peak power pulses are a powerful tool for engineering applications and research. However, their sensitivity to polarization limits their widespread application. To address this, automatic mode-locking immune to environmental disturbances is attracting more concerns. An experimental demonstration of the first real-time programmable mode-locked fiber laser is presented, which is enabled by our proposed human-like algorithm. It combines human logic with a machine's speed, computing capability, and precision. The laser automatically locks onto multiple operation regimes such as fundamental mode-locking, harmonic mode-locking, Q-switching, and even Q-switched mode-locking without physically altering its structure. The average initial mode-locking times (with randomly-assigned original polarization states and recover time from detachment under 10 successive experiments) are only 3.1 s and 58.9 ms. We believe this intelligent laser's superior performance can find practical applications in engineering and scientific research.

physics.ins-det↗