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Yu Xue

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7 papers

1 author row

EAAI Journal 2026 Journal Article

Application of a dynamic object detector with adaptive adjustment based on image complexity in the detection of drone aerial images

Ferrante Neri
Zehua Zhang
Yu Xue
Márcio P. Basgalupp

The detection of drone aerial images has become a research hotspot due to the extensive application of drones in various fields. However, the presence of a large number of small objects and complex scenes in the images poses severe challenges to the detection task. As an emerging technology, dynamic neural networks, with their input adaptive adjustment mechanism, provide important theoretical and technical support for solving the problem of detecting small objects in complex scenes of drone aerial images. In this work, we propose Dynamic You Only Look Once Object Detector (Dynamic-YOLO). Firstly, we improve You only look once (YOLO) v9. By leveraging the Convolutional Block Attention Module (CBAM) attention mechanism and Space-to-Depth Convolution (SPD-Conv), we optimise its backbone network to enhance the model’s feature extraction ability. Then, we adjust the structure of its neck network, delete the detection head P5 for large objects, and add the detection head P2 for tiny objects. After that, we combine a dynamic router with the improved YOLOv9 to form a dynamic detector, achieving adaptive processing of images. The experimental results on the Vision Meets Drone (VisDrone) benchmark dataset have demonstrated the excellent performance and flexibility of Dynamic-YOLO. When the Floating Point Operations (FLOPs) increase to 101. 5 G, the highest mean Average Precision at Intersection over Union 0. 5: 0. 95 (mAP@0. 5: 0. 95) reaches 32. 3% and mAP@0. 5 hits 51%, representing a 4. 0% and 10% improvement over YOLOv9-M. In addition, by dynamically adjusting the parameters of the router, the computational resource consumption and detection accuracy of the model can be flexibly adjusted, effectively achieving an optimised balance between detection performance and resource costs.

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EAAI Journal 2025 Journal Article

Cooperative traffic signal control for a partially observed vehicular network using multi-agent reinforcement learning

Chong Wang
Yueqi Li
Jiale Chen
Jian Zhang
Yu Xue

Cooperative traffic signal control aims to alleviate congestion and reduce travel times in urban environments by coordinating traffic lights. However, accurately modeling urban traffic dynamics and managing multiple intersections remain challenging tasks. Although Reinforcement Learning (RL) offers adaptability without requiring predefined traffic models, it faces challenges such as high dimensionality and data dependency, especially in real-world conditions like adverse weather or device malfunctions. In this paper, we propose a novel multi-tiered, multi-agent RL approach, named Multi-Agent Partially Observed Light (MAPOLight), designed to operate in a Vehicle-to-Infrastructure (V2I) environment with a limited number of Connected and Automated Vehicles (CAVs). The upper tier emphasizes agent collaboration by aggregating states and actions into mean values, significantly reducing state dimensionality. The lower tier employs advanced deep RL algorithms for optimization, ensuring both flexibility and scalability. Additionally, we introduce the Observation Correlation Indicator to relate CAV penetration rates (CAV P-Rates) with RL convergence. Simulation results reveal that our method offers stability and superior performance across diverse scenarios compared to traditional approaches. MAPOLight demonstrates robustness with CAV P-Rates above 20% and achieves convergence with a minimum penetration rate of 5%, outperforming existing methods. Moreover, our approach promotes smoother vehicle trajectories and exhibits strong adaptability, enabling rapid congestion relief in respond to unexpected traffic accidents. These results highlight the effectiveness and adaptability of the proposed approach.

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EAAI Journal 2025 Journal Article

State estimation of Lithium-ion Batteries with state space model

Zihao Lv
Yi Song
Yu Xue
Shijie Xu
Chunlin He
Liming Xu

For fault prediction and health management of lithium-ion batteries, numerous data-driven models leveraging Recurrent Neural Network and Transformer have been developed to characterize their degradation processes. However, the high computational cost poses significant challenges for their effective deployment on mobile devices. Recently, state space models like Mamba have mitigated these issues by managing sequence dependencies with near-linear computational complexity. Nonetheless, Mamba is constrained by its recurrent structure, resulting in limitations in modeling long-term dependencies and leveraging global contextual information. To address these limitations, we propose a simple and efficient model based on Mamba, named SE-Mamba. Specifically, SE-Mamba utilizes Mamba for sequence modeling and incorporates a Frequency Adaptive Filtering token mixer to transform latent representations into the frequency domain. This approach leverages element-wise multiplication to perform semantically adaptive frequency filtering, thereby enhancing the capacity to model long-term dependencies and exploit global contextual information. Additionally, the Particle Swarm Optimization algorithm is used to find the best hyperparameters. Experimental results across multiple datasets demonstrate that SE-Mamba maintains low computational overhead while achieving state-of-the-art performance.

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EAAI Journal 2023 Journal Article

Automatic topology optimization of echo state network based on particle swarm optimization

Yu Xue
Qi Zhang
Adam Slowik

The task of time series forecasting is to predict the future trend of data based on the collected historical data, providing theoretical and data support for human judgment and decision making. Randomization-based echo state networks (ESNs) are widely used in the research and application field of time series analysis for their simple structure and fast training speed. The core of the ESN is its dynamic reservoir, which the original reservoir are randomly generated and controlled only by parameter sparsity, often performing poorly on complex tasks and affecting the performance of networks. Manual design of the topology of reservoir is difficult, time-consuming and inconvenient to operate, which is not conducive to the development of ESNs. The construction of a suitable reservoir topology for practical application problems to enrich reservoir dynamics is a hot research point for researchers. In this paper, an automatic optimization method is introduced into the topology optimization of ESN (TP-ESN), and the particle swarm optimization algorithm is used to optimize the topological construction of the ESN. The connection structure between the reservoir neurons is first encoded and then iteratively optimized. The optimized structure is decoded and then the reservoir is initialized for ESN training. Prediction results on Mackey–Glass benchmark time series and two electroencephalogram (EEG) datasets demonstrate that TP-ESN method can have better adaptability, stronger prediction ability and stability than several other manually designed ESN reservoir topologies in the case of relatively complex tasks.

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EAAI Journal 2023 Journal Article

Continuously evolving dropout with multi-objective evolutionary optimisation

Pengcheng Jiang
Yu Xue
Ferrante Neri

Dropout is an effective method of mitigating over-fitting while training deep neural networks (DNNs). This method consists of switching off (dropping) some of the neurons of the DNN and training it by keeping the remaining neurons active. This approach makes the DNN general and resilient to changes in its inputs. However, the probability of a neuron belonging to a layer to be dropped, the ’dropout rate’, is a hard-to-tune parameter that affects the performance of the trained model. Moreover, there is no reason, besides being more practical during parameter tuning, why the dropout rate should be the same for all neurons across a layer. This paper proposes a novel method to guide the dropout rate based on an evolutionary algorithm. In contrast to previous studies, we associate a dropout with each individual neuron of the network, thus allowing more flexibility in the training phase. The vector encoding the dropouts for the entire network is interpreted as the candidate solution of a bi-objective optimisation problem, where the first objective is the error reduction due to a set of dropout rates for a given data batch, while the second objective is the distance of the used dropout rates from a pre-arranged constant. The second objective is used to control the dropout rates and prevent them from becoming too small, hence ineffective; or too large, thereby dropping a too-large portion of the network. Experimental results show that the proposed method, namely GADropout, produces DNNs that consistently outperform DNNs designed by other dropout methods, some of them being modern advanced dropout methods representing the state-of-the-art. GADroput has been tested on multiple datasets and network architectures.

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EAAI Journal 2022 Journal Article

An improved brain storm optimization algorithm with new solution generation strategies for classification

Yu Xue
Qi Zhang
Yan Zhao

In recent years, brain storm optimization (BSO) algorithm has received much attention in solving classical optimization problems and is used to implement evolutionary classification models. However, in practical applications, large-scale datasets complicate the structure of the classification model, which can have a great impact on the classification performance. In the optimization process, the traditional single-strategy BSO cannot preserve the information of dominant solution well, and its generation strategy is inefficient in solving various complex practical problems. To solve this problem, we introduce feature selection to improve the optimization model structure. Meanwhile, in order to enhance the search capability of BSO, three new generation strategy are embedded in the BSO algorithm in this paper. With the three generation methods of global optimal, local optimal and nearest neighbor, the information of the dominant solution can be better preserved and the search efficiency can be improved. The performance of the proposed generation strategy in solving classification problems is demonstrated on ten datasets with different sizes and dimensions. The experimental results reveal that the new generation strategy can enhance the performance of BSO algorithm for solving classification problems.

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EAAI Journal 2020 Journal Article

Genetic programming based feature construction methods for foreground object segmentation

Jiayu Liang
Yu Xue
Jianming Wang

Foreground object segmentation is a crucial preprocessing step for many high-level computer vision tasks, e. g. object recognition. It is still challenging to achieve accurate segmentation, especially for complex images (e. g. with high variations). Feature construction can help to improve the segmentation performance by extracting more distinctive features for foreground/background regions from the original features. However, commonly-used feature construction methods (e. g. principle component analysis) often involve certain assumptions/constraints, and the constructed features cannot be interpreted. To address these problems, genetic programming (GP) is employed in this paper, which is a well-suited feature construction technique. The aim of this work is to design new feature construction methods using GP, and analyse/compare popular GP-based feature construction methods for foreground object segmentation, especially on complex image datasets with high variations. Specifically, one new feature construction method that incorporates the subtree technique in GP is designed, which can construct multiple features simultaneously (called SubtMFC, Subtree Multiple Feature Construction). Moreover, a parsimony pressure technique is introduced to improve SubtMFC for bloat control (a common issue for GP-based methods), which forms the method, PSubtMFC (Parsimony SubtMFC). In addition, comparison of popular GP-based feature construction methods for foreground object segmentation is conducted for the first time. Results show that SubtMFC achieves better or similar performance compared with three reference methods. In addition, compared with SubtMFC that does not control bloat, PSubtMFC can significantly reduce the solution size while maintain similar performance in the segmentation accuracy. The GP-based feature construction framework is further extended for feature representation based knowledge transfer, which can handle the problem of the scare labelled training data. Moreover, after GP is thoroughly investigated on benchmark datasets with one type of foreground objects (i. e. the Weizmann horse dataset and Pascal aeroplane dataset), it is considered whether the GP methods can perform well on datasets containing multiple types of foreground objects. Compared with three other well-performing GP-based feature construction methods, the proposed method achieves better or comparable results for the given segmentation tasks. In addition, this paper thoroughly compares/analyses popular GP-based feature construction methods for complex figure-ground segmentation for the first time. Moreover, further analyses on the input features frequently used by the GP-evolved feature construction functions reflect the effectiveness of the extracted high-level features.

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