arXiv subjects

Jingxin Zhang

Publications and source records attributed to Jingxin Zhang.

At least 19 recordsLinked to original sources

arXiv2025

ExoAtom: A Database of Atomic Spectra in ExoMol Format

We present the ExoAtom database, www.exomol.com/exoatom, an extension of the ExoMol database to provide atomic line lists in the ExoMol format. ExoAtom is designed for detailed astrophysical, planetary, and laboratory applications. ExoAtom currently includes atomic data for 80 neutral atoms and 74 singly charged ions. These data are extracted from both the NIST and Kurucz databases, with 79/71 atoms/ions sourced from NIST and 38/37 atoms/ions sourced from Kurucz. ExoAtom uses the file types .all, .def, .states, .trans and .pf as fundamental components for structuring atomic data in a consistent hierarchy. The .states file contains quantum numbers, uncertainties, lifetimes, etc. The .trans file specifies Einstein A coefficients and their associated wavenumbers. The .pf file provides partition functions over a wide grid of temperatures. Post-processing of the ExoAtom data is provided by the program PyExoCross. Future development of ExoAtom will include additional ionization stages.

physics.atom-ph↗

arXiv2025

ExoMolHR: A Relational Database of Empirical High-Resolution Molecular Spectra

ExoMolHR is an empirical, high-resolution molecular spectrum calculator for the high-temperature molecular line lists available from the ExoMol molecular database. Uncertainties, where available, in recommended ExoMol datasets are used to select highly accurate spectral lines. These lines largely rely on empirical energy levels generated through the MARVEL (measured active rotation vibration energy levels) procedure, which is being systematically used to improve the energy and transition data provided by the ExoMol database. The freely accessible ExoMolHR database provides line positions with calculated intensities for a user-specified wavenumber/wavelength range and temperature. Spectra can be plotted on the ExoMolHR website (https://www.exomol.com/exomolhr/) or downloaded as a CSV file. Cross sections can be calculated using the Python program PyExoCross. The ExoMolHR database currently provides 24307135 spectral lines for 33 molecules and 58 isotopologues; these numbers will increase as the ExoMol database is updated.

astro-ph.EP↗

arXiv2024

Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark

Anomaly detection in complex industrial processes plays a pivotal role in ensuring efficient, stable, and secure operation. Existing anomaly detection methods primarily focus on analyzing dominant anomalies using the process variables (such as arc current) or constructing neural networks based on abnormal visual features, while overlooking the intrinsic correlation of cross-modal information. This paper proposes a cross-modal Transformer (dubbed FmFormer), designed to facilitate anomaly detection by exploring the correlation between visual features (video) and process variables (current) in the context of the fused magnesium smelting process. Our approach introduces a novel tokenization paradigm to effectively bridge the substantial dimensionality gap between the 3D video modality and the 1D current modality in a multiscale manner, enabling a hierarchical reconstruction of pixel-level anomaly detection. Subsequently, the FmFormer leverages self-attention to learn internal features within each modality and bidirectional cross-attention to capture correlations across modalities. By decoding the bidirectional correlation features, we obtain the final detection result and even locate the specific anomaly region. To validate the effectiveness of the proposed method, we also present a pioneering cross-modal benchmark of the fused magnesium smelting process, featuring synchronously acquired video and current data for over 2.2 million samples. Leveraging cross-modal learning, the proposed FmFormer achieves state-of-the-art performance in detecting anomalies, particularly under extreme interferences such as current fluctuations and visual occlusion caused by heavy water mist. The presented methodology and benchmark may be applicable to other industrial applications with some amendments. The benchmark will be released at https://github.com/GaochangWu/FMF-Benchmark.

cs.CV↗

arXiv2024

ExoMol line lists -- LX. Molecular line list for the ammonia isotopologue $^{15}$NH$_3$

A theoretical line list for $^{15}$NH$_3$ CoYuTe-15 is presented based on the empirical potential energy and ab initio dipole moments surfaces developed and used for the production of the ExoMol line list CoYuTe for $^{14}$NH$_3$. The ro-vibrational energy levels and wavefunctions are computed using the variational program TROVE. The line list ranges up to 10000 cm$^{-1}$ ($λ\geq 1$ $μ$m) and contains 929 795 249 transitions between 1 269 961 states with $J\le 30$. The line list should be applicable for temperatures up to $\sim$1000 K. To improve the accuracy of the line positions, a set of experimentally-derived energy levels of $^{15}$NH$_3$ is produced using the MARVEL procedure. To this end, 37 experimental sources of the line positions of $^{15}$NH$_3$ available in the literature are collected, combined and systematised to produce a self-consistent spectroscopic network of 21095 $^{15}$NH$_3$ transitions covering 40 vibrational bands ranging up to 6818 cm$^{-1}$ and resulting in 2777 energy term values. These MARVEL energies are then used to replace the theoretical values in the CoYuTe-15 line list and also complemented by pseudo-MARVEL energies obtained by an isotopologue extrapolation using the previously reported MARVEL energies of the $^{14}$NH$_3$ parent isotopologue of ammonia. A list of 53856 high resolution transitions between MARVEL states and theoretical intensities is provided in the HITRAN format. Comparison with the recent experimental spectra of $^{15}$NH$_3$ illustrate the potential of the line list for detections and as an efficient assistant in spectroscopic assignments. The line list is available from www.exomol.com.

astro-ph.EP↗

arXiv2024

The 2024 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres

The ExoMol database (www.exomol.com) provides molecular data for spectroscopic studies of hot atmospheres. These data are widely used to model atmospheres of exoplanets, cool stars and other astronomical objects, as well as a variety of terrestrial applications. The 2024 data release reports the current status of the database which contains recommended line lists for 91 molecules and 224 isotopologues giving a total of almost 10$^{12}$ individual transitions. New features of the database include extensive "MARVELization" of line lists to allow them to be used for high resolutions studies, extension of several line lists to ultraviolet wavelengths, provision of photodissociation cross sections and extended provision of broadening parameters. Some of the in-house data specifications have been rewritten in JSON and moved to conformity with other international standards. Data products, including specific heats, a database of lifetimes for plasma studies, and the ExoMolHR web app which allows exclusively high resolution data to be extracted, are discussed.

astro-ph.GA↗

arXiv2024

PyExoCross: a Python program for generating spectra and cross-sections from molecular line lists

PyExoCross is a Python adaptation of the ExoCross Fortran application, PyExoCross is designed for postprocessing the huge molecular line lists generated by the ExoMol project and other similar initiatives such as the HITRAN and HITEMP databases. PyExoCross generates absorption and emission stick spectra, cross-sections, and other properties (partition functions, specific heats, cooling functions, lifetimes, and oscillator strengths) based on molecular line lists. PyExoCross calculates cross-sections with four line profiles: Doppler, Gaussian, Lorentzian, and Voigt profiles in both sampling and binned methods; a number of options are available for computing Voigt profiles which we test for speed and accuracy. PyExoCross supports importing and exporting line lists in the ExoMol and HITRAN/HITEMP formats. PyExoCross also provides conversion between the ExoMol and HITRAN data formats. In addition, PyExoCross has extra code for users to automate the batch download of line list files from the ExoMol database.

astro-ph.IM↗

arXiv2024

IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict

The conflict between Israel and Palestinians significantly escalated after the October 7, 2023 Hamas attack, capturing global attention. To understand the public discourse on this conflict, we present a meticulously compiled dataset-IsamasRed-comprising nearly 400,000 conversations and over 8 million comments from Reddit, spanning from August 2023 to November 2023. We introduce an innovative keyword extraction framework leveraging a large language model to effectively identify pertinent keywords, ensuring a comprehensive data collection. Our initial analysis on the dataset, examining topics, controversy, emotional and moral language trends over time, highlights the emotionally charged and complex nature of the discourse. This dataset aims to enrich the understanding of online discussions, shedding light on the complex interplay between ideology, sentiment, and community engagement in digital spaces.

cs.SI↗

arXiv2024

Maximal electric power generation from varying ocean waves with LC-tuned reactive PTO force

The reactive Power Take Off (PTO) force is the key to maximizing mechanical power absorption and electric power generation of Wave Energy Converters (WECs) from ocean waves with variable frequency, but its study is limited due to its difficulty in physical realization. This paper presents a simple yet effective $LC$-tuned WEC that generates a tunable reactive PTO force from tunable inductor $L$ and capacitor $C$ in the WEC. A complete closed loop system model of the WEC is derived first, then three quantitative rules are obtained from analyzing the model. These rules are used to tune the $LC$ network, and hence the reactive PTO force that drives the WEC, to resonate with the input wave force and generate maximal electric power over a range of wave frequencies. Mathematical analysis of the WEC and tuning rules reveals the analytical and quantitative descriptions of the WEC's mechanical power absorption, active and reactive electric power generation and power factor, optimal electric resistance load, and the generator and $LC$ capacity requirements. Simulation results show the effectiveness and advantages of the proposed WEC and verify the analysis results.

eess.SY↗

arXiv2023

Experiment-based deep learning approach for power allocation with a programmable metasurface

Deep learning, as a highly efficient method for metasurface inverse design, commonly use simulation data to train deep neural networks (DNNs) that can map desired functionalities to proper metasurface designs. However, the assumptions and simplifications made in the simulation model may not reflect the actual behavior of a complex system, leading to suboptimal performance of the DNNs in practical scenarios. To address this issue, we propose an experiment-based deep learning approach for metasurface inverse design and demonstrate its effectiveness for power allocation in complex environments with obstacles. Enabled by the tunability of a programmable metasurface, large sets of experimental data in various configurations can be collected for DNN training. The DNN trained by experimental data can inherently incorporate complex factors and can adapt to changed environments through its on-site data-collecting and fast-retraining capability. The proposed experiment-based DNN holds the potential for intelligent and energy-efficient wireless communication in complex indoor environments.

eess.SP↗

arXiv2023

Data structures for photoabsorption within the ExoMol project

The ExoMol database currently provides comprehensive line lists for modelling the spectroscopic properties of molecules in hot atmospheres. Extending the spectral range of the data provided to ultraviolet (UV) wavelengths brings into play three processes not currently accounted for in the ExoMol data structure, namely photodissociation, which is an important chemical process in its own right,the opacity contribution due to continuum absorption and predissociation which can lead to significant and observable line broadening effects. Data structures are proposed which will allow these processes to be correctly captured and the (strong) temperature-dependent effects predicted for UV molecular photoabsorption in general and photodissociation in particular to be represented.

astro-ph.EP↗

arXiv2023

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become popular in the field of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a large number of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To address the aforementioned issues, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. The proposed SCCAM method is tested on a continuous stirred tank heater and the Tennessee Eastman industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification and root cause analysis.

cs.LG↗

arXiv2022

Generation of superhelical time-varying OAM beam with space-time-coding digital metasurface

The recently proposed extreme-ultraviolet (EUV) beams with time-varying orbital angular momentum (OAM) realized by high-harmonic generation (HHG) provides extraordinary tools for quantum excitation control and particle manipulation. However, such an approach is not easily scalable to other frequency regimes. Here, we design a space-time-coding digital metasurface operating in microwave frequencies to experimentally generate different modes of time-varying OAM. We also introduce a concept of superhelicity of time-varying OAM with a higher-order twist of the wavefront structure, which can be further realized by reflection phase profiles nonlinear in time through the metasurface. By developing a two-probe measurement technique, we dynamically map the OAM field pattern varying in time. The proposed superhelical time-varying OAM beam paves a way for particle manipulations and OAM multiplexing communications.

physics.app-ph↗

arXiv2022

Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring

In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode dynamic process monitoring. When a new mode arrives, a set of data should be collected so that this mode can be identified by PSFA and prior knowledge. Then, a regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is denoted as PSFA-EWC, which is updated continually and capable of achieving excellent performance for successive modes. Different from traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and forward transfer ability. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. Compared with several known methods, the effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.

cs.LG↗

arXiv2021

Convergence Rate of Accelerated Average Consensus with Local Node Memory: Optimization and Analytic Solutions

Previous researches have shown that adding local memory can accelerate the consensus. It is natural to ask questions like what is the fastest rate achievable by the $M$-tap memory acceleration, and what are the corresponding control parameters. This paper introduces a set of effective and previously unused techniques to analyze the convergence rate of accelerated consensus with $M$-tap memory of local nodes and to design the control protocols. These effective techniques, including the Kharitonov stability theorem, the Routh stability criterion and the robust stability margin, have led to the following new results: 1) the direct link between the convergence rate and the control parameters; 2) explicit formulas of the optimal convergence rate and the corresponding optimal control parameters for $M \leq 2$ on a given graph; 3) the optimal worst-case convergence rate and the corresponding optimal control parameters for the memory $M \geq 1$ on a set of uncertain graphs. We show that the acceleration with the memory $M = 1$ provides the optimal convergence rate in the sense of worst-case performance. Several numerical examples are given to demonstrate the validity and performance of the theoretical results.

math.OC↗

arXiv2021

Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series

In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction algorithms as usual consist of the selection of kernel structure parameters and the kernel weight vector updating. For structure parameters, the kernel dictionary is selected by some sparsification techniques with online selective modeling criteria, and moreover the kernel covariance matrix is intermittently optimized in the light of the covariance matrix adaptation evolution strategy (CMA-ES). Optimizing the real symmetric covariance matrix can not only improve the kernel structure's flexibility by the cross relatedness of the input variables, but also partly alleviate the prediction uncertainty caused by the kernel dictionary selection for nonstationary time series. In order to sufficiently capture the underlying dynamic characteristics in prediction-error time series, a generalized optimization strategy is designed to construct the kernel dictionary sequentially in multiple kernel connection modes. The generalized optimization strategy provides a more self-contained way to construct the entire kernel connections, which enhances the ability to adaptively track the changing dynamic characteristics. Numerical simulations have demonstrated that the proposed approach has superior prediction performance for nonstationary time series.

eess.SP↗

arXiv2021

Self-learning sparse PCA for multimode process monitoring

This paper proposes a novel sparse principal component analysis algorithm with self-learning ability for successive modes, where synaptic intelligence is employed to measure the importance of variables and a regularization term is added to preserve the learned knowledge of previous modes. Different from traditional multimode monitoring methods, the monitoring model is updated based on the current model and new data when a new mode arrives, thus delivering prominent performance for sequential modes. Besides, the computation and storage resources are saved in the long run, because it is not necessary to retrain the model from scratch frequently and store data from previous modes. More importantly, the model furnishes excellent interpretability owing to the sparsity of parameters. Finally, a numerical case and a practical pulverizing system are adopted to illustrate the effectiveness of the proposed algorithm.

cs.LG↗

arXiv2021

Monitoring multimode processes: a modified PCA algorithm with continual learning ability

For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically forgotten when a monitoring model for the current mode is built. It would result in an abrupt performance decrease. It could be an effective manner to make local monitoring model remember the features of previous modes. Choosing the principal component analysis (PCA) as a basic monitoring model, we try to resolve this problem. A modified PCA algorithm is built with continual learning ability for monitoring multimode processes, which adopts elastic weight consolidation (EWC) to overcome catastrophic forgetting of PCA for successive modes. It is called PCA-EWC, where the significant features of previous modes are preserved when a PCA model is established for the current mode. The optimal parameters are acquired by differences of convex functions. Moreover, the proposed PCA-EWC is extended to general multimode processes and the procedure is presented. The computational complexity and key parameters are discussed to further understand the relationship between PCA and the proposed algorithm. Potential limitations and relevant solutions are pointed to understand the algorithm further. Numerical case study and a practical industrial system in China are employed to illustrate the effectiveness of the proposed algorithm.

stat.ML↗

arXiv2021

PET Image Reconstruction with Multiple Kernels and Multiple Kernel Space Regularizers

Kernelized maximum-likelihood (ML) expectation maximization (EM) methods have recently gained prominence in PET image reconstruction, outperforming many previous state-of-the-art methods. But they are not immune to the problems of non-kernelized MLEM methods in potentially large reconstruction error and high sensitivity to iteration number. This paper demonstrates these problems by theoretical reasoning and experiment results, and provides a novel solution to solve these problems. The solution is a regularized kernelized MLEM with multiple kernel matrices and multiple kernel space regularizers that can be tailored for different applications. To reduce the reconstruction error and the sensitivity to iteration number, we present a general class of multi-kernel matrices and two regularizers consisting of kernel image dictionary and kernel image Laplacian quatradic, and use them to derive the single-kernel regularized EM and multi-kernel regularized EM algorithms for PET image reconstruction. These new algorithms are derived using the technical tools of multi-kernel combination in machine learning, image dictionary learning in sparse coding, and graph Laplcian quadratic in graph signal processing. Extensive tests and comparisons on the simulated and in vivo data are presented to validate and evaluate the new algorithms, and demonstrate their superior performance and advantages over the kernelized MLEM and other conventional methods.

eess.IV↗