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Junjie Shen

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3 papers

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EAAI Journal 2026 Journal Article

A two-stage framework for photovoltaic power forecasting: Integrating adaptive hybrid decomposition with a novel predictor

Xiaonan Shen
Junjie Shen
Tianle Zhang
Yuting Zhang
Yang Wang

To address the severe non-stationarity and multi-scale fluctuations in photovoltaic (PV) power output, this paper proposes a novel two-stage forecasting framework that integrates complexity-aware adaptive hybrid decomposition with an xLSTM (Extended Long-Short Term Memory)-KAN (Kolmogorov-Arnold Network) model. Initially, the original time series is decomposed using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). A K-Means clustering method based on Sample Entropy (SE) is then employed to identify the high-frequency component exhibiting the greatest stochasticity. This component subsequently undergoes a secondary adaptive decomposition via Sequential Variational Mode Decomposition (SVMD), thereby effectively isolating noise and enhancing the signal's purity. The optimized components are then fed into the xLSTM-KAN prediction model. Unlike traditional “black-box" deep learning architectures, this model integrates xLSTM to capture long-term dependencies and enhance parallel computation efficiency, while also leveraging KAN's learnable activation functions, which are parameterized by B-splines, to significantly improve approximation accuracy and model interpretability. Experimental results from four actual photovoltaic power plants (30–130 MW) show that the proposed temporal forecasting model, xLSTM-KAN, reduces MAE (Mean Absolute Error) by an average of 5. 81% and RMSE (Root Mean Square Error) by 7. 45% compared to other advanced architectures like iTransformer and LSTMformer. It also exhibits superior stability, with VAE (Variance of Absolute Error) decreasing by an average of 15. 02%. Moreover, the proposed adaptive decomposition strategy, SVMD-ICEEMDAN, lowers MAE by an average of 9. 12% and RMSE by 18. 44% compared to traditional hybrid methods such as VMD-CEEMDAN. These results validate the framework's robustness across different scales and climatic conditions, providing reliable and interpretable decision support for power systems with high renewable energy penetration.

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AAAI Conference 2026 Conference Paper

AutoPP: Towards Automated Product Poster Generation and Optimization

Jiahao Fan
Yuxin Qin
Wei Feng
Yanyin Chen
Yaoyu Li
Ao Ma
Yixiu Li
Li Zhuang

Product posters blend striking visuals with informative text to highlight the product and capture customer attention. However, crafting appealing posters and manually optimizing them based on online performance is laborious and resource-consuming. To address this, we introduce AutoPP, an automated pipeline for product poster generation and optimization that eliminates the need for human intervention. Specifically, the generator, relying solely on basic product information, first uses a unified design module to integrate the three key elements of a poster (background, text, and layout) into a cohesive output. Then, an element rendering module encodes these elements into condition tokens, efficiently and controllably generating the product poster. Based on the generated poster, the optimizer enhances its Click-Through Rate (CTR) by leveraging online feedback. It systematically replaces elements to gather fine-grained CTR comparisons and utilizes Isolated Direct Preference Optimization (IDPO) to attribute CTR gains to isolated elements. Our work is supported by AutoPP1M, the largest dataset specifically designed for product poster generation and optimization, which contains one million high-quality posters and feedback collected from over one million users. Experiments demonstrate that AutoPP achieves state-of-the-art results in both offline and online settings.

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AAAI Conference 2026 Conference Paper

MoFu: Scale-Aware Modulation and Fourier Fusion for Multi-Subject Video Generation

Run Ling
Ke Cao
Jian Lu
Ao Ma
Haowei Liu
Runze He
Changwei Wang
Rongtao Xu

Multi-subject video generation aims to synthesize videos from textual prompts and multiple reference images, ensuring that each subject preserves natural scale and visual fidelity. However, current methods face two challenges: scale inconsistency, where variations in subject size lead to unnatural generation, and permutation sensitivity, where the order of reference inputs causes subject distortion. In this paper, we propose MoFu, a unified framework that tackles both challenges. For scale inconsistency, we introduce Scale-Aware Modulation (SMO), an LLM-guided module that extracts implicit scale cues from the prompt and modulates features to ensure consistent subject sizes. To address permutation sensitivity, we present a simple yet effective Fourier Fusion strategy that processes the frequency information of reference features via the Fast Fourier Transform to produce a unified representation. Besides, we design a Scale-Permutation Stability Loss to jointly encourage scale-consistent and permutation-invariant generation. To further evaluate these challenges, we establish a dedicated benchmark with controlled variations in subject scale and reference permutation. Extensive experiments demonstrate that MoFu significantly outperforms existing methods in preserving natural scale, subject fidelity, and overall visual quality.

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