To solve the problem of low accuracy of remaining useful life (RUL) prediction caused by insufficient sample data of equipment under complex operating conditions, an RUL prediction method of small sample equipment based on a deep convolutional neural network—bidirectional long short-term memory network (DCNN-BiLSTM) and domain adaptation is proposed. Firstly, in order to extract the common features of the equipment under the condition of sufficient samples, a network model that combines the deep convolutional neural network (DCNN) and the bidirectional long short-term memory network (BiLSTM) was used to train the source domain and target domain data simultaneously. The Maximum Mean Discrepancy (MMD) was used to constrain the distribution difference and achieve adaptive matching and feature alignment between the target domain samples and the source domain samples. After obtaining the pre-trained model, fine-tuning was used to transfer the network structure and parameters of the pre-trained model to the target domain for training, perform network optimization and finally obtain an RUL prediction model that was more suitable for the target domain data. The method was validated on a simulation dataset of commercial modular aero-propulsion provided by NASA, and the experimental results show that the method improves the prediction accuracy and generalization ability of equipment RUL under cross-working conditions and small sample conditions.

In contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays a vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose a significant challenge. In this context, the present study proposes ReSSA-iTransformer, an advanced predictive model engineered to accurately forecast soil temperatures within greenhouses across diverse temporal scales, encompassing both long-term and short-term horizons. This model capitalizes on the iTransformer time-series forecasting framework and integrates Singular Spectrum Analysis (SSA) to decompose environmental variables, thereby augmenting the extraction of pivotal features, such as soil temperature. Furthermore, to mitigate the prevalent distribution shift issues inherent in time-series data, Reversible Instance Normalization (RevIN) is incorporated within the model architecture. ReSSA-iTransformer is adept at executing multi-step forecasts for both extended and immediate future intervals, thereby offering comprehensive predictive capabilities. Empirical evaluations substantiate that ReSSA-iTransformer surpasses conventional models, including LSTM, Informer, and Autoformer, across all assessed metrics. Specifically, it attained R² coefficients of 98.51%, 97.03%, 97.26%, and 94.83%, alongside MAE values of 0.271, 0.501, 0.648, and 1.633 for predictions at 3 h, 6 h, 24 h, and 48 h intervals, respectively. These results highlight the model’s superior accuracy and robustness. Ultimately, ReSSA-iTransformer not only provides dependable soil temperature forecasts but also delivers actionable insights, thereby facilitating enhanced greenhouse management practices.

The purpose of Low Illumination Image Enhancement (LLIE) is to improve the perception or interpretability of images taken in low illumination environments. This work inherits the work of Zero-Reference Deep Curve Estimation (Zero-DCE) and proposes a more effective image enhancement model, Zero-DCE Tiny. First, the new model introduces the Cross Stage Partial Network (CSPNet) into the original U-net structure, divides basic feature maps into two parts, and then recombines it through the structure of cross-phase connection to achieve a richer gradient combination with less computation. Second, we replace all the deep separable convolutions except the last layer with Ghost modules, which makes the network lighter. Finally, we introduce the channel consistency loss into the non-reference loss, which further strengthens the constraint on the pixel distribution of the enhanced image and the original image. Experiments show that compared with Zero-DCE++, the network proposed in this work is more lightweight and surpasses the Zero-DCE++ method in some important image enhancement evaluation indexes.

The representation and utilization of environmental information by service robots has become increasingly challenging. In order to solve the problems that the service robot platform has, such as high timeliness requirements for indoor environment recognition tasks and the small scale of indoor scene data, a method and model for rapid classification of household environment domain knowledge is proposed, which can achieve high recognition accuracy by using a small-scale indoor scene and tool dataset. This paper uses a knowledge graph to associate data for home service robots. The application requirements of knowledge graphs for home service robots are analyzed to establish a rule base for the system. A domain ontology of the home environment is constructed for use in the knowledge graph system, and the interior functional areas and functional tools are classified. This designed knowledge graph contributes to the state of the art by improving the accuracy and efficiency of service decision making. The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. This proposal adopts a combination of MobileNetV3 and transfer learning, integrating large-scale pre-training with fine-tuning for the home environment to address the challenge of limited data for home robots. The results show that the proposed model achieves higher recognition accuracy and recognition speed than other common methods, meeting the work requirements of service robots. With the Scene15 dataset, the proposed scheme has the highest recognition accuracy of 0.8815 and the fastest recognition speed of 63.11 microseconds per sheet.

This research presents an advanced YOLOv8-MMD framework specifically designed for intelligent white radish harvesting systems, addressing the critical need for simultaneous species recognition and quality evaluation. The proposed architecture is built upon a dual-branch detection system (YOLOv8-Dual) with a shared Backbone network, and is further enhanced by two novel components: the Multi-Scale Attention Aggregation (MSAA) module that strategically combines channel-wise and spatial attention mechanisms to refine feature representation, and the Multi-scale Feature Enhancement (MAFE) module that facilitates effective information fusion across different hierarchical levels of the network. Extensive experimental validation reveals that the YOLOv8-MMD model achieves remarkable performance metrics, including a species detection precision of 0.945 and a quality assessment precision of 0.812, representing improvements of 1.4% and 4%, respectively, over the baseline YOLOv8-Dual model. Under the comprehensive mAP@50 evaluation standard, the model reaches 0.949 for species identification and 0.859 for quality classification, while maintaining impressive recall rates of 0.924 and 0.836 for the respective tasks. The system demonstrates exceptional robustness when deployed in challenging field conditions, consistently performing well under varying lighting intensities, different growth stages, and partial occlusion scenarios. Computational analysis confirms the model’s practical viability, achieving a processing throughput of 112 frames per second with 8.1 GFLOPs of computational overhead, thereby meeting stringent real-time operational requirements for agricultural robotic applications. Comparative studies with existing methods further substantiate the superiority of the proposed approach in balancing detection accuracy with computational efficiency. The integration of multi-scale attention mechanisms and hierarchical feature enhancement strategies provides a comprehensive solution for automated agricultural harvesting in complex, unstructured environments, offering significant potential for practical implementation in precision agriculture systems.

As one of China’s major grain crops, wheat has a high demand for water resources, making it susceptible to drought stress. Traditional irrigation evaluation methods are often based on experience and rule-based calculations, which struggle to cope with complex environmental factors and dynamic changes in crop needs. With technological advancements, deep learning-based research methods, characterized by their strong data-driven analytical capabilities, are expected to improve the accuracy of evaluation results. This paper focuses on the irrigation demand assessment of winter wheat farmland, aiming to explore a new regional-scale irrigation demand assessment method based on deep learning. By establishing samples of different irrigation evaluation levels, this study seeks to better meet the requirements of irrigation demand assessment. For the problem of regional-scale irrigation-level discrimination, the Convolutional Network Attention(CONAT) module was proposed to optimize the backbone network structure of the Mask2Former model. To tackle issues related to data imbalance and underfitting across certain categories, a loss function tailored for imbalanced sample distributions was implemented, accompanied by enhancements to the training scheme. By contrasting this refined model with alternative methods for discriminating irrigation levels, the viability of this approach was showcased.