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Yuan An

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EAAI Journal 2026 Journal Article

Solving parameter estimation problem in inverse radiation analysis by deep learning: A case study of two-dimensional temperature field reconstruction in furnace

Chun Lou
Chong Zhang
Yuan An
Shipeng Ren
Nimeti Kalaycı

The simultaneous reconstruction of temperature fields and radiative properties in industrial furnaces is a significant ill-posed inverse problem, characterized by strong parameter coupling and high computational cost. Although traditional decoupled reconstruction methods are accurate, their intensive iterative processes make them impractical for online monitoring. This paper proposes a deep learning-based method for the simultaneous reconstruction of two-dimensional temperature fields and radiative properties. A radiative imaging model was first developed to relate the internal temperature field, radiative properties, and boundary radiative temperatures. A large dataset was generated based on radiative transfer forward problem. A multi-task deep neural network was then developed and trained on the dataset. In the simulation study, the reconstructed temperature field and radiative properties showed average relative errors of less than 1 % and 6 %, respectively. Comparative analysis with the decoupled reconstruction method demonstrates that the model achieves similar accuracy while reducing computation time from 2 s to 8 μs, enabling real-time capability. Furthermore, the model maintains accurate reconstruction under various noise conditions, demonstrating robustness. Experimental validation on a 350 MW (MW) furnace, using transfer learning with limited annotated data, confirmed effectiveness of the method. As furnace load increased from 260 to 320 MW, reconstructed temperature fields increased from 1424 to 1627 K (K), while absorption and scattering coefficients gradually increased. The average relative error of inversely calculated boundary radiative temperature under different loads was less than 1. 5 %. These indicates that the proposed method has promising potential for the study of flames.

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EAAI Journal 2025 Journal Article

A streaming variable neural speech codec

Huaifeng Zhang
Pengfei Wu
Guigeng Li
Yuan An
Hao Zhang

This paper presents a variable bit rate streaming neural speech codec designed for ultra-low bit rate scenarios, based on the SoundStream network framework. The codec employs the vector quantized variational auto-encoder (VQ-VAE) algorithm to capture the temporal structure and spectral characteristics of the speech signal, and constructs a latent space codebook to facilitate the effective mapping of feature vectors to discrete vectors. Based on the harmonic characteristics of speech signals and the inherent defects of single-scale discriminators, we introduce multi-period discriminators and multi-scale discriminators. The training process uses a balanced training strategy to ensure the balance between codebook utilization and training weights, and utilizes the Short-Time Fourier Transform (STFT) spectrum that can provide more accurate time–frequency resolution to compute the reconstruction loss. We introduce codebook loss to improve the utilization rate of the codebook and accelerate the convergence of the model. In the inference process, we use a quantizer selection strategy to achieve adaptive adjustment of variable bitrate. Objective and subjective experiments demonstrate that our proposed new neural speech codec outperforms traditional classical speech codecs and existing neural speech codecs in terms of reconstructed speech naturalness and quality while maintaining the low latency characteristic of neural speech codecs. With a multi-stimulus test with hidden reference and anchor (MUSHRA) score of 87, it is highly suitable for ultra-low bit rate speech compression applications such as satellite speech communication and narrowband instant messaging. The demo has been publicly released at https: //svcodec. github. io/.

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AAAI Conference 2006 Conference Paper

Building Semantic Mappings from Databases to Ontologies

Yuan An

A recent special issue of AI Magazine (AAAI 2005) was dedicated to the topic of semantic integration — the problem of sharing data across disparate sources. At the core of the solution lies the discovery the “semantics” of different data sources. Ideally, the semantics of data are captured by a formal ontology of the domain together with a semantic mapping connecting the schema describing the data to the ontology. However, establishing the semantic mapping from a database schema to a formal ontology in terms of formal logic expressions is inherently difficult to automate, so the task was left to humans. In this paper, we report on our study (An, Borgida, & Mylopoulos 2005a; 2005b) of a semi-automatic tool, called MAPONTO, that assists users to discover plausible semantic relationships between a database schema (relational or XML) and an ontology, expressing them as logical formulas/rules.

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