Author name cluster

Feng Jin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

EAAI Journal 2026 Journal Article

A causal generative model-based optimal scheduling method for blast furnace gas system considering unknown scenarios

Feng Jin
Xiaoxue Wang
Jun Zhao
Wei Wang

Blast furnace gas is a significant category of byproduct energy source produced during the ironmaking process, and its rational utilization is crucial to improving energy efficiency in steel plants. However, frequent operator interventions continually create new operating scenarios, which makes it difficult for traditional methods to maintain effective scheduling. Existing scheduling methods based on optimization and generative adversarial networks (GANs) rely excessively on historical scenarios and fail to capture the explicit causal relationships among the key factors, thus restricting their applicability for scheduling under unknown conditions. To tackle such an issue, an optimal scheduling method for BFG system based on an improved causal generative model, which is capable of generating diverse and physically consistent scenarios, is proposed in this study. Each scenario is characterized by three interpretable factors, i. e. gas tank level, generation-consumption flow difference, and consumption of adjustable units. A causal conditional Wasserstein GAN (Causal-CWGAN) is then constructed by embedding a process-informed adjacency matrix and a differentiable acyclicity constraint into the WGAN-GP framework. In addition, a correction model combined with mechanism-based rationality rules is adopted to further optimize the consumption and filters unreasonable scenarios. Subsequently, the tank-level prediction is performed to update the generated scenario set and derive practical adjustment suggestions. Experimental results on real data from a steel enterprise show that, compared with the WGAN and the MGAN methods, the proposed one yields smaller Wasserstein distances, generates more rational scenarios, and provides adjustment strategies that can stably keep the gas tank level within the safety operating range.

Details DOI

EAAI Journal 2025 Journal Article

A physics informed convolution neural network for spatiotemporal temperature analysis of concrete dams

Jiaqi Yang
Jinting Wang
Feng Jin
Jianwen Pan

Structural health monitoring is indispensable throughout the life cycle of dams, and the loading conditions determines the reliability of the assessment. Among them, temperature plays an important role on the behavior of arch dams, which are sparsely monitored in practice. How to use these sparsely measured data to obtain the accurate spatiotemporal temperature field becomes a critical problem. This study proposes a physics informed convolutional neural network for spatiotemporal temperature field of arch dams. A dual thread convolutional neural network considers the effects of spatiotemporal and temporal variables distinctively. The proposed model is validated using measured data from an existing arch dam. Compared with applied convolutional neural network, the proposed model improves the accuracy of temperature field reconstruction by 18 % and reduces reliance on measured data. Benefit of consideration of the continuity and heat transfer, the spatial distribution of the temperature field is more reasonable in continuity, and can retain accuracy even with limited monitoring data. The proposed model can provide the actual spatiotemporal non-uniform temperature field of the arch dam, providing basic data for the analysis and safety evaluation of arch dams throughout their life-cycle.

Details DOI

EAAI Journal 2025 Journal Article

Generalized deep neural network for seismic site response prediction with transfer learning

Lin Li
Feng Jin
Duruo Huang
Chunhui He
Fulong Ma

Accurate prediction of site-specific seismic responses plays a pivotal role in evaluating earthquake effects on infrastructure. Traditional physics-based methods suffer from inherent model assumptions, significant parameter uncertainty, and high computational costs. This study proposes a generalized deep neural network that integrates seismic motion data and site information to predict three-directional seismic responses across various site types. Trained on an extensive dataset of recorded data from Kiban Kyoshin Network in Japan, the model demonstrated excellent performance on the test set, with correlation coefficients reaching 97 % between the predicted and target results. Utilizing transfer learning techniques, it was adapted to seismic response prediction at new sites not included in the training set. Compared to the state-of-the-art finite element method, the retrained model significantly improved prediction accuracy, with an overall average error reduction of approximately 50 %. Additionally, the model effectively captured the nonlinear response characteristics of a site during strong seismic events without any strong motion data to retrain. The proposed model demonstrated superior prediction accuracy, higher computational efficiency, and stronger generalization capabilities compared to traditional physics-based models.

Details DOI

EAAI Journal 2023 Journal Article

Soil seismic response modeling of KiK-net downhole array sites with CNN and LSTM networks

Lin Li
Feng Jin
Duruo Huang
Gang Wang

Accurate prediction of soil seismic response is necessary for geotechnical engineering. The conventional physics-based models such as the finite element method (FEM) usually fail to obtain accurate predictions due to the model assumption and parameter uncertainties. And the physics-based models are computationally expensive. This study proposes deep learning models to develop data-driven surrogate models for the prediction of soil seismic response based on the recorded ground motions from KiK-net downhole array sites. Two kinds of advanced neural networks, convolution neural network (CNN) and long short-term memory (LSTM) neural network, are applied in this framework respectively. These models do not rely on any prior knowledge about the soil site. The performance of the deep learning models is demonstrated through both numerical and recorded examples. Compared with the state-of-art FEM models, the proposed models could achieve better prediction performance with higher efficiency. The average prediction error is reduced by more than 40% in time domain and 30% in frequency domain. Even though great variability exists during the propagation of seismic in the reality, the models can still get satisfactory predictions.

Details DOI

JBHI Journal 2022 Journal Article

Divergent and Convergent Imaging Markers Between Bipolar and Unipolar Depression Based on Machine Learning

Huifeng Zhang
Zhen Zhou
Lei Ding
Chuangxin Wu
Meihui Qiu
Yueqi Huang
Feng Jin
Ting Shen

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain “high-order functional connectivity (HOFC)” network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex “chronnectome” metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthycontrols (HC). We achieved a satisfactory accuracy (70. 40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e. g. , olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.

Details DOI

AAAI Conference 2012 Conference Paper

Using First-Order Logic to Compress Sentences

Minlie Huang
Xing Shi
Feng Jin
Xiaoyan Zhu

Sentence compression is one of the most challenging tasks in natural language processing, which may be of increasing interest to many applications such as abstractive summarization and text simplification for mobile devices. In this paper, we present a novel sentence compression model based on first-order logic, using Markov Logic Network. Sentence compression is formulated as a word/phrase deletion problem in this model. By taking advantage of first-order logic, the proposed method is able to incorporate local linguistic features and to capture global dependencies between word deletion operations. Experiments on both written and spoken corpora show that our approach produces competitive performance against the state-of-the-art methods in terms of manual evaluation measures such as importance, grammaticality, and overall quality.

PDF Details