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In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

The heating of high-voltage isolating switches above 110 kV will cause serious safety accidents. To identify the heating faults of isolating switches accurately, this study uses natural language processing technology, knowledge graph (KG) technology and machine learning algorithm to mine the structured and unstructured mixed data. Firstly, based on bidirectional encoder representations (BERT) pre-training model, a bidirectional long short-term memory network and conditional random field model are introduced to identify entities related to high-voltage isolating switches and the thermal failures from the text data. Further, a convolutional neural network fusion attention mechanism model is leveraged to recognize the corresponding entity relationships from the text records. Then, the KGs of isolating switches and the heating faults are constructed, and a path ranking algorithm is proposed to deduce and identify the possible factors causing overheating on the KGs. Combined with the structured data, a support vector machine model based on focal loss function (FL-SVM) is proposed to recognize the heating faults of isolating switches. Experiments show that the performances of all six models used in this paper on the hybrid dataset are higher than those on the structured dataset. Such results suggest that joint text-structured data mining can compensate for insufficient structured data and thus, achieving a better heating failure recognition, especially for the infrequent malfunctions. Meanwhile, the proposed FL-SVM performs better than other models due to its excellent ability to solve the problem of imbalanced faults and normal samples. [ABSTRACT FROM AUTHOR]

As smart homes become more prevalent, users expect to control smart devices through natural language commands and desire personalized smart home services. However, existing challenges include the interoperability of smart devices and a comprehensive understanding of the user environment. To address these issues, this paper proposed a framework supporting personalized smart home service recommendations for device-end users. Firstly, it constructed a runtime knowledge graph to reflect contextual information in specific smart homes and generated scenario-based sentences. Secondly, it trained a general recommendation model using pre-collected natural language instructions and corresponding scenario-based sentence representations from users in common scenarios. Finally, users interacted with smart home devices and services through natural language on the device end while fine-tuning the weights of the general model through feedback to obtain a personal model. Experimental results on three datasets-basic instruction set, paraphrase set, and scenario instruction set show that the personal model achieves an accuracy improvement of 6.5% to 30% compared to word embedding methods and 2.4% to 25% compared to the Sentence-BERT model, which validates that the device-end deep learning-based smart home service framework has a high service recommendation accuracy and effectively manages smart home devices and services. [ABSTRACT FROM AUTHOR]

New-generation intelligent construction places higher demands on digitisation and intelligence of tunnel. Digital twin (DT) effectively supports high-fidelity modelling, virtual-real mapping, and analysis-based decision-making but with research in the initial stage. To begin with, this paper delves into the complexity and uncertainty inherent in tunnel construction, highlighting DT as a promising solution compared to exisiting technologies such as Building Information Modelling. Then, a systematic literature survey is conducted, revealing growing focus on DT research topics. To provide comprehensive insights into DT-related technologies and their application in tunnel construction, this paper clusters literature from perspectives of sensor networks, Internet of Things (IoT), computer vision-based twin data acquisition, communication, natural language processing (NLP), automatic control-based connection, and geometric, semantic, analytical integrated twin modelling. These aspects shed light on potentials and limitations of existing researh in developing a functional DT. In response to the challenges of information richness, timeliness, and analytical capabilities, an improved conceptual framework tailored for tunnel is proposed to close the information and control loop. Finally, the paper discusses the prospects and gaps of DT in theory and practice to leverage DT implementation. • Digital Twin greatly enhances the development of intelligent tunnel construction. • A comprehensive literature analysis is conducted to assess status of Digital Twin technologies in tunnel construction. • A close-loop conceptual framework of Digital Twin is proposed. • Gaps and future prospects of Digital Twin for tunnel construction are discussed. [ABSTRACT FROM AUTHOR]

Traditional methods for designing concrete materials typically rely on labor-intensive laboratory experiments, resulting in time and cost inefficiencies. Recently, designing concrete using artificial intelligence (AI) methods has shown high efficiency, but existing AI methods often rely solely on data, which can lead to violation with scientific principles and result in models lacking reasoning abilities. To overcome these challenges, this paper presents an interpretable knowledge graph-guided data-driven design approach. By integrating advanced computing techniques with domain knowledge via knowledge graphs, this approach enables the interpretation of data-driven models and uncovers the underlying mechanisms behind predictions. This approach is applied to ultra-high-performance concrete (UHPC) involving complex physicochemical reactions. The domain knowledge about UHPC is imparted using a knowledge graph, and UHPC properties are predicted using a machine learning model considering mixing proportions, processing methods, and physiochemical properties of materials via natural language processing. The results show that the knowledge graph displays crucial design variables and their effects on UHPC properties, aiding in selecting variables for machine learning models and interpreting their results. The prediction accuracy of the machine learning model reached 0.95. The research paves the way for more transparent and scientific AI models for material design and AI-enabled discovery of scientific knowledge. [Display omitted] ● An artificial intelligence (AI) approach is presented to design concrete with solid wastes. ● The AI approach achieves interpretability by combining machine learning and knowledge graph. ● Knowledge about the physicochemical reactions and properties of concrete is learned and used. ● The AI approach is applied to low-carbon cost-effective ultra-high-performance concrete. ● The AI approach can interpret the underlying mechanisms and generate new knowledge. [ABSTRACT FROM AUTHOR]