Abstract The paper presents a cloud-based data processing platform for the Internet of Things (IoT), designed to handle the collection, storage, processing, and visualization of sensor data. It leverages cloud computing to address the challenges posed by the massive volumes of data generated by IoT devices, utilizing scalable technologies like Hadoop and Hive for distributed storage and Map Reduce based analytics. The system architecture includes... The paper presents a cloud-based data processing platform for the Internet of Things (IoT), designed to handle the collection, storage, processing, and visualization of sensor data. It leverages cloud computing to address the challenges posed by the massive volumes of data generated by IoT devices, utilizing scalable technologies like Hadoop and Hive for distributed storage and Map Reduce based analytics. The system architecture includes a TCP server module built with PHP and the Swoole extension to manage real time sensor data ingestion, and a middleware layer based on Redis to decouple components and enable efficient inter module communication. The platform offers two primary external interfaces: one for task execution (via Hive) and another for data uploading to HDFS. Performance tests on the TCP server show high data processing efficiency at low concurrency, with performance degrading beyond 500 concurrent connections still sufficient for most real world IoT scenarios where data acquisition rates are lower. The Hadoop cluster demonstrates robust functionality, including dynamic node addition/removal without service interruption. While domestic IoT analytics platforms in China lag behind global counterparts, this work contributes a customizable, open framework that supports secondary development and real time monitoring. The authors conclude that the platform meets typical production needs and suggest future improvements in API design and developer usability to lower the barrier for customization and deployment. Read More Read Less

Abstract This paper explores the integration of digital technology into modern ceramic art, emphasizing its transformative potential in design, production, and creative expression. It begins by highlighting China's historical prominence in ceramics but notes that contemporary Chinese ceramic art lags behind global standards due to limited innovation and underutilization of digital tools. While industries like automotive and fashion have embraced digital advancements, ceramic art remains... This paper explores the integration of digital technology into modern ceramic art, emphasizing its transformative potential in design, production, and creative expression. It begins by highlighting China's historical prominence in ceramics but notes that contemporary Chinese ceramic art lags behind global standards due to limited innovation and underutilization of digital tools. While industries like automotive and fashion have embraced digital advancements, ceramic art remains largely reliant on traditional, manual techniques. The paper discusses how software such as 3ds Max and Photoshop can enhance ceramic design through 3D modeling, glaze simulation, and decorative pattern creation, offering greater efficiency, accuracy, and creative flexibility compared to conventional methods. Applications span functional ceramics, environmental ceramic installations, and artistic decoration, with digital renderings facilitating better communication, visualization, and ecological integration. The study underscores the need for Chinese ceramic artists to adopt digital technologies to bridge the gap with international counterparts, preserve cultural heritage, and meet modern aesthetic and market demands. Ultimately, the fusion of traditional ceramic practices with digital innovation is presented not as a replacement but as a vital evolution, enabling richer artistic expression, sustainability, and global competitiveness in ceramic art. The conclusion affirms that digital technology serves as both a practical tool and a source of creative inspiration, essential to the future development of the discipline. Read More Read Less

Abstract This paper explores how deep learning can be leveraged to preserve and promote traditional Chinese martial arts, which are a vital part of China's intangible cultural heritage. Historically transmitted through master apprentice relationships, these arts now face challenges due to societal modernization and demographic shifts. The author proposes using deep learning models particularly convolutional neural networks (CNNs) to capture, analyze, and reconstruct martial arts... This paper explores how deep learning can be leveraged to preserve and promote traditional Chinese martial arts, which are a vital part of China's intangible cultural heritage. Historically transmitted through master apprentice relationships, these arts now face challenges due to societal modernization and demographic shifts. The author proposes using deep learning models particularly convolutional neural networks (CNNs) to capture, analyze, and reconstruct martial arts movements through human pose estimation. A novel model, IPN (Involution Pose Estimation Net), built on Simple Baselines and Involution mechanisms, is introduced to identify key body joints from video data with high accuracy. The study utilizes datasets like NTURGB+ D and UTD-MHAD, though it acknowledges their limitations for martial arts specific actions, highlighting the need for a dedicated Chinese martial arts motion database. Evaluation metrics such as tMPJPE (time aware Mean Per Joint Position Error) are adapted to assess pose accuracy over time. Experimental results demonstrate effective movement recognition in 'Youth Serial Boxing,' with promising convergence and accuracy. The research underscores deep learning's potential not only in digitizing martial arts but also in enabling broader cultural preservation in the intelligent era. Future work includes expanding the model to other martial arts styles and improving dataset specificity to enhance training and generalization. Read More Read Less