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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. [ABSTRACT FROM AUTHOR]

Motivation The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug–microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse, and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug–microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. Results We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug–microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug–microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug–microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on three drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs. Availability and implementation Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDMP. [ABSTRACT FROM AUTHOR]

The precise and robust localization of pupils is crucial for advancing medical diagnostics and enhancing user experience. Currently, the predominant method for determining the center of the pupil relies on the principles of multi-view geometry, necessitating the simultaneous operation of multiple sensors at different angles. This study introduces a single-stage pupil localization network named ResDenseDilateNet, which is aimed at utilizing a single sensor for pupil localization and ensuring accuracy and stability across various application environments. Our network utilizes near-infrared (NIR) imaging to ensure high-quality image output, meeting the demands of most current applications. A unique technical highlight is the seamless integration of the efficient characteristics of the Deep Residual Network (ResNet) with the Dense Dilated Convolutions Merging Module (DDCM), which substantially enhances the network's performance in precisely capturing pupil features, providing a deep and accurate understanding and extraction of pupil details. This innovative combination strategy greatly improves the system's ability to handle the complexity and subtleties of pupil detection, as well as its adaptability to dynamic pupil changes and environmental factors. Furthermore, we have proposed an innovative loss function, the Contour Centering Loss, which is specifically designed for irregular or partially occluded pupil scenarios. This method innovatively calculates the pupil center point, significantly enhancing the accuracy of pupil localization and robustness of the model in dealing with varied pupil morphologies and partial occlusions. The technology presented in this study not only significantly improves the precision of pupil localization but also exhibits exceptional adaptability and robustness in dealing with complex scenarios, diverse pupil shapes, and occlusions, laying a solid foundation for the future development and application of pupil localization technology. [ABSTRACT FROM AUTHOR]

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.

To study the influence of Chinese Pinyin on detecting and correcting text errors in speech recognition, a text proofreading model based on Chinese semantic and phonological information was proposed. Five Pinyin coding methods were designed to construct the character-Pinyin embedding vector that was employed as the input of the Seq2Seq model based on gated recurrent unit. At the same time, the attention mechanism was adopted to extract the Chinese semantic and phonological information of sentences to correct speech recognition errors. Aiming at the problem of insufficient labeled corpus, a data augmentation method was introduced, which could automatically obtain annotated corpora by exchanging the initials or finals of Chinese Pinyin. The experimental results on AISHELL-3's public data show that phonological information is conducive to the text proofreading model to detect and correct text errors after speech recognition, and the proposed data augmentation method can improve the error detection performance of the model. [ABSTRACT FROM AUTHOR]