![]() It can effectively divide the point set into several groups to achieve the model segmentation. The experimental results demonstrate that (1) the proposed approach can effectively transform the b-rep model into a two-dimensional coordinate point set (2) the k-means algorithm can efficiently cluster points to achieve segmentation and (3) in view of human cognition, the segmentation results are more reasonable. The k-means approach with the Silhouette coefficient was employed to conduct unsupervised learning of the coordinate points. By means of PERT, spectral theory, and the CAD models’ geometrical and topological information, we transform the b-rep model faces into two-dimensional coordinate points corresponding to the nodes of the attributed adjacent graph (AAG). In this article, we first propose a novel CAD model segmentation method that uses the fusion of the program/project evaluation and review technique (PERT) and the Laplacian spectrum theory. ![]() With our high performance, low latency, and purpose-built streaming charts and alerts, your IT infrastructure team can gain real-time observability with much lower complexity and cost-of-ownership.For complex CAD models, model segmentation technology is an important support for model retrieval and reuse. Timeplus provides a unified storage and processing engine for both historical and real-time data. With Pulsar’s built-in multi-tenant support, tier-storage, and the separation of computing and storage, it’s one of the most common tools to consolidate logs/metrics/tracing from various IT systems and applications. Real-time visibility can be provided to the internal/external users to improve operational efficiency or customer satisfaction.Īpplication monitoring and observability. By connecting Timeplus with Pulsar, the real-time data from IoT sensors can be easily processed with sub-second latency. It supports multiple messaging protocols, including MQTT, AMQP, and JMS. Pulsar is cloud-native and can run in any cloud, on-premises, or Kubernetes environment. Internet-of-Things (IoT): The explosive growth of connected remote devices is posing challenges for the centralized computing paradigm. ![]() With Pulsar as a new supported source and sink in Timeplus, your data engineering team can use Timeplus as the tool to transform data among Pulsar topics, or migrate data from one message bus to another. ![]() In the Timeplus web UI, you can write ad-hoc streaming SQL, and get results in the next second, creating a seamless, fast experience.įull SQL-based routing/transformation/alerting. For example, writing a new hopping window analytics app with FlinkSQL may take upwards of 1-2 minutes while the Flink job submits and waits to return results. With Timeplus, a similar analytics workload can be implemented using just one-tenth of the time and cost. Flink can be a powerful and flexible tool however, when building real-time analytics applications, it’s oftentimes not easy to integrate Pulsar and Flink. Your data team can use the Timeplus web UI to quickly load data from Pulsar to Timeplus, then use the purpose-built streaming tables and charts in Timeplus to view live data and understand their patterns. Timeplus systems fuzion code#No need to write Java code to connect to the Pulsar clusters or use command line interface to print the raw messages. ![]()
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