International Journal of Simulation and Process Modelling (5 papers in press)

Regular Issues

• Improving maritime distress target detection through modelling and simulation with YOLOv5s and Next-ViT  
  by Xinbo Chang, Kun Liu, Zhen Liu 
  Abstract: To additionally raise the accuracy of maritime distress target detection, an improved YOLOv5s model with Next-ViT is proposed through modeling and simulation. In this model, Next-ViT is applied to extract representations, followed by adopting a neck network with spatial context pyramid and Focal-GIoU loss to identify the targets. To validate its effectiveness, extensive experiments are conducted on the sub-dataset of the SeaDronesSee dataset. Contrasted to the primary YOLOv5s model, the proposed model has promoted Recall, mAP0.5 and mAP0.5?0.95 by 9.2, 6.3 and 3.3 percentage points, respectively, demonstrating superior performance over existing models.
  Keywords: YOLOv5s model; target detection; Next-ViT; spatial context pyramid.
  DOI: 10.1504/IJSPM.2025.10070958
   
  
• Design of ventilation preheating system for pig nursery based on ANFIS  
  by Zhidong Wu, Kaixiang Xu, Yanwei Chen, Meiqi Liu, Yonglan Liu 
  Abstract: Aiming at the problem that the low temperature of the air supply in the pig nursery in cold areas during winter, the ventilation preheating system and control strategy are designed. The proposed system combines in house heating, a heat exchanger with a ventilation heater. The adaptive neuro fuzzy inference system (ANFIS) is established by learning sample data, and the ventilated heater controller is designed. The results of simulations and field tests show that the proposed system based on the ANFIS algorithm can efficiently raise the temperature of the air supply to 20
  Keywords: ventilation preheating; computational fluid dynamics; adaptive neuro fuzzy inference system; cold region.
  DOI: 10.1504/IJSPM.2025.10071181
   
  
• Defect detection method of Chinese mitten crab based on improved EfficientVit  
  by Guangyu Mu, Heng Zhang, Zhongxiang Fei, Guodong Gao, Haiguang Zhang 
  Abstract: An improved EfficientVit-based defect detection method for Chinese mitten crabs (Eriocheir sinensis) is proposed to enhance detection efficiency and accuracy while reducing human interference. A dataset of intact and defective crab images was constructed to improve the model’s adaptability. The Squeeze-and-Excitation (SE) module and Mix of Experts Hidden (MoH) module were integrated into the EfficientVit model to strengthen its feature extraction ability and sensitivity to subtle differences. The ReLU activation function was replaced with the Parametric Rectified Linear Unit (PReLU) activation function to address the “dying neuron” issue and improve the model’s sensitivity to negative inputs. The improved model achieved an accuracy of 97.34%, precision of 97.35%, recall of 97.34%, and F1 score of 97.34%, showing significant improvements over the baseline model. Future work will focus on optimizing model performance and exploring its adaptability and generalization in different environments.
  Keywords: Chinese mitten crab; EfficientVit; defect detection; attention mechanism; activation function.
  DOI: 10.1504/IJSPM.2025.10071192
   
  
• Simulation-Driven Fault Diagnosis for Track Circuits Using Multi-Scale Convolution and Transformers Under Imbalanced Data Conditions  
  by Jundong Fu, Xiaojun Yuan 
  Abstract: With the increasing complexity of railway systems, diagnosing faults in ZPW-2000A track circuits poses significant challenges, especially under imbalanced data conditions. This paper introduces a novel simulation-driven fault diagnosis framework combining multi-scale convolution, Transformer encoders, and LDAM loss to enhance classification accuracy. The method extracts short- and long-term features from time-series data, effectively addressing data imbalance through gated convolution and optimized classification boundaries. Simulation experiments on 1,600 samples demonstrated a 98.75% accuracy, significantly outperforming existing approaches. Ablation studies validated the contribution of each module. The findings show potential for real-time fault detection and improved railway safety. Future work includes validating the model on real-world data and optimizing its computational efficiency. This research contributes to simulation and process modelling by offering a robust, scalable solution for fault diagnosis, aligning with industrial needs and advancing the state-of-the-art in imbalanced data analysis.
  Keywords: Fault Diagnosis; Imbalanced Data Handling; Simulation-Based Methods ; Railway Safety Systems; Deep Learning Models; Process Modelling.
  
  
• Optimising multi-modal fusion with a tri-encoder tensor network for process applications  
  by Jiayao Li, Li Li, Xiaochen Shi, Zeqiu Chen, Kaiyi Zhao, Ruizhi Sun 
  Abstract: Multi-modal fusion combines information from diverse modalities, enabling scalable predictions in complex data environments. To address the existing limitations in capturing modalities interactions and reducing time consumption, we propose an optimising multi-modal fusion with a tri-encoder tensor network for process applications (TS-OMMF). Specifically, the input modalities are encoded to abstract the intra-modal feature and obtain the representation, respectively. The representations are fused into a high dimension space with a low-rank factor to limit the dimension and linear extension pattern is employed to assist model extend for multi-modal. The fusion features are input into a novel tri-encoder to capture the inter-modal feature at a finer granularity and obtain the complementary features, thereby reducing the time consuming. Extensive experiments on benchmark multi-modal datasets demonstrate that TS-OMMF improves performance metrics by 0.8% to 6.1%. These results highlight TS-OMMF practical applicability, scalability, and potential for advancing process modelling and other complex multi-modal data-driven tasks.
  Keywords: multi-modal tensor fusion; encoder-decoder; tensor representation; multi-modal fusion; auto-encoder.
  DOI: 10.1504/IJSPM.2025.10071854