Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning

IntroductionWith the rapid development of the tourism industry, the demand for accurate and personalized travel route recommendations has significantly increased. However, traditional methods often fail to effectively integrate visual and sequential information, leading to recommendations that are both less accurate and less personalized.MethodsThis paper introduces SelfAM-Vtrans, a novel algorithm that leverages multimodal data—combining visual Transformers, LSTMs, and self-attention mechanisms—to enhance the accuracy and personalization of travel route recommendations. SelfAM-Vtrans integrates visual and sequential information by employing a visual Transformer to extract features from travel images, thereby capturing spatial relationships within them. Concurrently, a Long Short-Term Memory (LSTM) network encodes sequential data to capture the temporal dependencies within travel sequences. To effectively merge these two modalities, a self-attention mechanism fuses the visual features and sequential encodings, thoroughly accounting for their interdependencies. Based on this fused representation, a classification or regression model is trained using real travel datasets to recommend optimal travel routes.Results and discussionThe algorithm was rigorously evaluated through experiments conducted on real-world travel datasets, and its performance was benchmarked against other route recommendation methods. The results demonstrate that SelfAM-Vtrans significantly outperforms traditional approaches in terms of both recommendation accuracy and personalization. By comprehensively incorporating both visual and sequential data, this method offers travelers more tailored and precise route suggestions, thereby enriching the overall travel experience.

A multimodal travel route recommendation system leveraging visual Transformers and self-attention mechanisms

Juan,

Zhang,

Gao

2024

Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration

Dong,

Li,

Zheng

et al. 2024

With the rapid development of Industrial Internet of Things (IIoT) technology, various IIoT devices are generating large amounts of industrial sensor data that are spatiotemporally correlated and heterogeneous from multi-source and multi-domain. This poses a challenge to current detection algorithms. Therefore, this paper proposes an improved long short-term memory (LSTM) neural network model based on the genetic algorithm, attention mechanism and edge-cloud collaboration (GA-Att-LSTM) framework is proposed to detect anomalies of IIoT facilities. Firstly, an edge-cloud collaboration framework is established to real-time process a large amount of sensor data at the edge node in real time, which reduces the time of uploading sensor data to the cloud platform. Secondly, to overcome the problem of insufficient attention to important features in the input sequence in traditional LSTM algorithms, we introduce an attention mechanism to adaptively adjust the weights of important features in the model. Meanwhile, a genetic algorithm optimized hyperparameters of the LSTM neural network is proposed to transform anomaly detection into a classification problem and effectively extract the correlation of time-series data, which improves the recognition rate of fault detection. Finally, the proposed method has been evaluated on a publicly available fault database. The results indicate an accuracy of 99.6%, an F1-score of 84.2%, a precision of 89.8%, and a recall of 77.6%, all of which exceed the performance of five traditional machine learning methods.

Architectural planning robot driven by unsupervised learning for space optimization

Zhang,

Zheng

2025

IntroductionSpace optimization in architectural planning is a crucial task for maximizing functionality and improving user experience in built environments. Traditional approaches often rely on manual planning or supervised learning techniques, which can be limited by the availability of labeled data and may not adapt well to complex spatial requirements.MethodsTo address these limitations, this paper presents a novel architectural planning robot driven by unsupervised learning for automatic space optimization. The proposed framework integrates spatial attention, clustering, and state refinement mechanisms to autonomously learn and optimize spatial configurations without the need for labeled training data. The spatial attention mechanism focuses the model on key areas within the architectural space, clustering identifies functional zones, and state refinement iteratively improves the spatial layout by adjusting based on learned patterns. Experiments conducted on multiple 3D datasets demonstrate the effectiveness of the proposed approach in achieving optimized space layouts with reduced computational requirements.Results and discussionThe results show significant improvements in layout efficiency and processing time compared to traditional methods, indicating the potential for real-world applications in automated architectural planning and dynamic space management. This work contributes to the field by providing a scalable solution for architectural space optimization that adapts to diverse spatial requirements through unsupervised learning.