An enhanced architecture of online 3D visualization framework for monitoring coconut plantation

The visualization of existing and future agricultural plantation is becoming more important for monitoring crops as well as for decision-making, as it considerably helps to influences the production. The concept of best monitoring coconut plantation is an important stage of agricultural technology development; for instance, utilizing online 3D visualization system to support monitoring processes. The goal of this research is to present and justify an identified research problem with the utilization of a proposed enhanced architecture of online 3D visualization framework. The identified research problem was investigated since the current 3-layer framework has shortcomings, such as, weaknesses in the size of graph visualization, especially the ability to visualize large size of graph in online 3D visualization. In such situation, 3D visualization seems challenging as it generates a massive amount of image datasets and large 3D objects or graphs for each of the coconut trees. Therefore, in this novel approach, this study introduced a client/server structure-based framework which subdivides the total process into the concept of layer to overcome the existing issue. One more layer will be added to the existing three-layer framework to formalize into 3-layer framework for handling the large size graph visualization. It consists of four separate layers, namely interface layer, visualization process layer, display information layer, and database layer. Each layer has its own specific function and distinct from others. The framework was reviewed, evaluated and validated by the coconut plantation manager and 3D visualization experts; it was then used as a basis to develop a prototype to visualize the large virtual area of coconut plantation. Subsequently, the prototype was evaluated by users with diverse experience. Overall, results from the usability testing demonstrated that it can comfortably support or handle more graphs of the coconut plantation, thus achieving its satisfaction through formulating identified graph visualization problem

Visual analytics for relationships in scientific data

Domain scientists hope to address grand scientific challenges by exploring the abundance of data generated and made available through modern high-throughput techniques. Typical scientific investigations can make use of novel visualization tools that enable dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters. These general tools should be applicable to many disciplines: allowing biologists to develop an intuitive understanding of the structure of coexpression networks and discover genes that reside in critical positions of biological pathways, intelligence analysts to decompose social networks, and climate scientists to model extrapolate future climate conditions. By using a graph as a universal data representation of correlation, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool integrates techniques such as graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized B-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using real-world workflows from several large-scale studies. Parallel coordinates has proven to be a scalable visualization and navigation framework for multivariate data. However, when data with thousands of variables are at hand, we do not have a comprehensive solution to select the right set of variables and order them to uncover important or potentially insightful patterns. We present algorithms to rank axes based upon the importance of bivariate relationships among the variables and showcase the efficacy of the proposed system by demonstrating autonomous detection of patterns in a modern large-scale dataset of time-varying climate simulation

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.Comment: Accepcted by IEEE Journal on Selected Areas in Communicatio

A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians’ writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge