arXiv subjects

Fuli Zhang

Publications and source records attributed to Fuli Zhang.

15 recordsLinked to original sources

arXiv2025

Research on Core Loss of Direct-drive 75kW Tidal Current Generator Using Machine Learning and Multi-objective Optimization Algorithms

This paper presents a classification of generator excitation waveforms using principal component analysis (PCA) and machine learning models, including logistic regression, random forest, and gradient boosting decision trees (GBDT). Building upon the traditional Steinmetz equation, a temperature correction term is introduced. Through nonlinear regression and least squares parameter fitting, a novel temperature correction equation is proposed, which significantly reduces the prediction error for core losses under high-temperature conditions. The average relative error is decreased to 16.03%, thereby markedly enhancing the accuracy. Using GBDT and random forest regression models, the independent and combined effects of temperature, excitation waveforms, and magnetic materials on core loss are analyzed. The results indicate that the excitation waveform has the most significant impact, followed by temperature, while the magnetic core material exhibits the least influence. The optimal combination for minimizing core loss is identified, achieving a core loss value of 35,310.9988 under the specified conditions. A data-driven predictive model for core loss is developed, demonstrating excellent performance with an R*R value of 0.9703 through machine learning regression analysis, indicating high prediction accuracy and broad applicability. Furthermore, a multi-objective optimization model considering both core loss and transmission magnetic energy is proposed. Genetic algorithms are employed for optimization, resulting in an optimal design scheme that minimizes core loss and maximizes transmission magnetic energy. Based on this model, the magnetic core compensation structure of a direct-drive 75kW tidal energy generator is optimized in practical applications, yielding satisfactory results.

physics.app-ph↗

arXiv2024

Space-Time-Modulated Wideband Radiation-Type Programmable Metasurface for Low Sidelobe Beamforming

Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to the capability of elaborate modulation over electromagnetic (EM) waves. However, they are in either reflective or transmissive mode, and usually possess a relatively high profile as a result of the external feed source. Besides, it is difficult to conduct multibit phase shift in metasurfaces, when comparing with conventional phased arrays. Here, we propose a strategy of space-time modulated wideband radiation-type programmable metasurface for low side-lobe beamforming. The wideband programmable metasurface avoids the space-feed external source required by its traditional counterpart, thus achieving a significant reduction of profile through integration of a highefficiency microwave-fed excitation network and metasurface. Furthermore, through introducing space-time-modulated strategy, the high-accuracy amplitude-phase weight algorithm can also be synchronously carried out on the first harmonic component for low side-lobe beam-scanning. Most importantly, adaptive beamforming and generation of interference null can further be created after analyzing the harmonic component characteristics of received signals.

eess.SP↗

arXiv2024

Experimental Demonstration of Controllable PT and anti-PT Coupling in a non-Hermitian Metamaterial

Non-Hermiticity has recently emerged as a rapidly developing field due to its exotic characteristics related to open systems, where the dissipation plays a critical role. In the presence of balanced energy gain and loss with environment, the system exhibits parity-time (PT) symmetry, meanwhile as the conjugate counterpart, anti-PT symmetry can be achieved with dissipative coupling within the system. Here, we demonstrate the coherence of complex dissipative coupling can control the transition between PT and anti-PT symmetry in an electromagnetic metamaterial. Notably, the achievement of the anti-PT symmetric phase is independent of variations in dissipation. Furthermore, we observe phase transitions as the system crosses exceptional points in both anti-PT and PT symmetric metamaterial configurations, achieved by manipulating the frequency and dissipation of resonators. This work provides a promising metamaterial design for broader exploration of non-Hermitian physics and practical application with controllable Hamiltonian.

physics.optics↗

arXiv2022

An Industrial Experience Report on Retro-inspection

To reinforce the quality of code delivery, especially to improve future coding quality, one global Information and Communication Technology (ICT) enterprise has institutionalized a retrospective style inspection (namely retro-inspection), which is similar to Fagan inspection but differs in terms of stage, participants, etc. This paper reports an industrial case study that aims to investigate the experiences and lessons from this software practice. To this end, we collected and analyzed various empirical evidence for data triangulation. The results reflect that retro-inspection distinguishes itself from peer code review by identifying more complicated and underlying defects, providing more indicative and suggestive comments. Many experienced inspectors indicate defects together with their rationale behind and offer suggestions for correction and prevention. As a result, retro-inspection can benefit not only quality assurance (like Fagan inspection), but also internal audit, inter-division communication, and competence promotion. On the other side, we identify several lessons of retro-inspection at this stage, e.g., developers' acceptance and organizers' predicament, for next-step improvement of this practice. To be specific, some recommendations are discussed for retro-inspection, e.g., more adequate preparation and more careful publicity. This study concludes that most of the expected benefits of retro-inspection can be empirically confirmed in this enterprise and its value on the progress to continuous maturity can be recognized organization-wide. The experiences on executing this altered practice in a large enterprise provide reference value on code quality assurance to other software organizations.

cs.SE↗

arXiv2022

Modeling Review History for Reviewer Recommendation:A Hypergraph Approach

Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender's performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one Pull-Request (PR) (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one PR with multiple reviewers herein) and developed a new recommender, namely HGRec, which is evaluated by 12 OSS projects with more than 87K PRs, 680K comments in terms of accuracy and recommendation distribution. The results indicate that HGRec outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders, HGRec is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers' workload congestion issue. Moreover, since HGRec is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.

cs.SE↗

arXiv2020

Quantifying the Impact of Scholarly Papers Based on Higher-Order Weighted Citations

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical.

cs.DL↗

arXiv2020

An Overview on Evaluating and Predicting Scholarly Article Impact

Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The~review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars.

cs.DL↗

arXiv2020

Measure the Impact of Institution and Paper via Institution-citation Network

This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.

cs.DL↗

arXiv2020

Author Impact: Evaluations, Predictions, and Challenges

Author impact evaluation and prediction play a key role in determining rewards, funding, and promotion. In this paper, we first introduce the background of author impact evaluation and prediction. Then, we review recent developments of author impact evaluation, including data collection, data pre-processing, data analysis, feature selection, algorithm design, and algorithm evaluation. Thirdly, we provide an in-depth literature review on author impact predictive models and common evaluation metrics. Finally, we look into the representative research issues, including author impact inflation, unified evaluation standards, academic success gene, identification of the origins of hot streaks, and higher-order academic networks analysis. This paper should help the researchers obtain a broader understanding in author impact evaluation and prediction, and provides future research directions.

cs.DL↗

arXiv2020

Predicting the Citations of Scholarly Paper

Citation prediction of scholarly papers is of great significance in guiding funding allocations, recruitment decisions, and rewards. However, little is known about how citation patterns evolve over time. By exploring the inherent involution property in scholarly paper citation, we introduce the Paper Potential Index (PPI) model based on four factors: inherent quality of scholarly paper, scholarly paper impact decaying over time, early citations, and early citers' impact. In addition, by analyzing factors that drive citation growth, we propose a multi-feature model for impact prediction. Experimental results demonstrate that the two models improve the accuracy in predicting scholarly paper citations. Compared to the multi-feature model, the PPI model yields superior predictive performance in terms of range-normalized RMSE. The PPI model better interprets the changes in citation, without the need to adjust parameters. Compared to the PPI model, the multi-feature model performs better prediction in terms of Mean Absolute Percentage Error and Accuracy; however, their predictive performance is more dependent on the parameter adjustment.

cs.DL↗

arXiv2020

Implicit Multi-feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.

cs.SI↗

arXiv2017

Controlling the toroidal excitations in metamaterials for high-Q response

The excitation of toroidal multipoles in metamaterials was investigated for high-Q response at a subwavelength scale. In this study, we explored the optimization of toroidal excitations in a planar metamaterial comprised of asymmetric split ring resonators (ASRRs). It was found that the scattering power of toroidal dipole can be remarkably strengthened by adjusting the characteristic parameter of ASRRs: asymmetric factor. Interestingly, the improvement in toroidal excitation accompanies increment on the Q-factor of the toroidal metamaterial; it is shown that both the scattering power of toroidal dipole and the Q-factor were increased more than one order by changing the asymmetric factor of ASRRs. The optimization in excitation of toroidal multipole provide opportunity to further increase the Q-factor of metamaterial and boost light-matter interactions at the subwavelength scale for potential applications in low-power nonlinear processing, and sensitive photonic applications.

physics.optics↗

arXiv2016

A metamaterial modulator based on electrically controllable electromagnetically induced transparency

Electromagnetically induced transparency (EIT) is a promising technology for the enhancement of light-matter interactions, and recent demonstrations of the quantum EIT realized in artificial micro-structured medium have remarkably reduced the extreme requirement for experimental observation of EIT spectrum. In this paper, we propose to electrically control the EIT spectrum in a metamaterial for an electromagnetic modulator. A diode acting as a tunable resistor is loaded in the gap of two paired wires to inductively tune the magnetic resonance, which induces remarkable modulation on the EIT spectrum through the metamaterial sample. The experimental measurements confirmed that the prediction of electromagnetic modulation in three narrow bands on the EIT spectrum, and a modulation contrast of up to 31 dB was achieved on the transmission through the metamaterial. Our results may facilitate the study on active/dynamical technology in translational metamaterials, which connect extraordinary manipulations on the flow of light in metamaterials, e.g., the exotic EIT, and practical applications in industry.

physics.optics↗

arXiv2015

Tunable mid-infrared coherent perfect absorption in a graphene meta-surface

We exploited graphene nanoribbons based meta-surface to realize coherent perfect absorption (CPA) in the mid-infrared regime. It was shown that quasi-CPA frequencies, at which CPA can be demonstrated with proper phase modulations, exist for the graphene meta-surface with strong resonant behaviors. The CPA can be tuned substantially by merging the geometric design of the meta-surface and the electrical tunability of graphene. Furthermore, we found that the graphene nanoribbon meta-surface based CPA is realizable with experimental graphene data. The findings of CPA with graphene meta-surface can be generalized for potential applications in optical detections and signal processing with two-dimensional optoelectronic materials.

physics.optics↗

arXiv2014

Tunable terahertz coherent perfect absorption in a monolayer graphene

Coherent perfect absorber (CPA) was proposed as the time-reversed counterpart to laser: a resonator containing lossy medium instead of gain medium can absorb the coherent optical fields completely. Here, we exploit a monolayer graphene to realize the CPA in a non-resonant manner. It is found that quasi-CPA point exists in the terahertz regime for suspending monolayer graphene, and the CPA can be implemented with the assistant of proper phase modulation among two incident beams at the quasi-CPA frequencies. The graphene based CPA is found of broadband angular selectivity: CPA point splits into two frequency bands for the orthogonal $s$ and $p$ polarizations at oblique incidence, and the two bands cover a wide frequency range starting from zero frequency. Furthermore, the coherent absorption can be tuned substantially by varying the gate-controlled Fermi energy. The findings of CPA with non-resonant graphene sheet can be generalized for potential applications in terahertz/infrared detections and signal processing with two-dimensional optoelectronic materials.

physics.optics↗