Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods

інтелектуальних системах підтримки прийняття рішень. Зазначена методика призначена для підвищення оперативності обробки різнотипних даних в інтелектуальних системах підтримки прийняття рішень. Комплексна методика складається з наступних взаємопов’язаних процедур: модель зберігання різнорідних даних; алгоритм синхронізації різнорідних даних; алгоритм розділення різнорідних даних; алгоритм індексування різнорідних даних. Модель зберігання різнорідних розвідувальних даних, яка є основою методики, відрізняється наявністю шаблонів об’єктів розвідки і шаблонів параметрів об’єктів розвідки. Шаблони дозволяють розподіллено зберігати як неструктуровані різнорідні розвідувальні дані, так і структуровані розвідувальні дані відповідно до визначеної схеми, що дозволяє знизити часові витрати на доступ до даних. В моделі зберігання різнорідних розвідувальних даних розроблені алгоритм синхронізації різнорідних розвідувальних даних, алгоритм розділення різнорідних розвідувальних даних та алгоритм індексування різнорідних розвідувальних даних Ефективна структура зберігання різнорідних даних озер даних дозволяє зручно і швидко здійснювати доступ до сховища для пакетної обробки даних. Розробка запропонованої методики обумовлена необхідністю підвищення оперативності обробки різнотипної інформації в інтелектуальних системах підтримки прийняття рішень з прийнятною обчислювальною складністю. Запропонована методика дозволяє підвищити ефективність функціонування інтелектуальних систем підтримки прийняття рішень за рахунок комплексної обробки даних, що в них циркулюють. Запропонована методика дозволяє підвищити оперативність обробки інформації в системах підтримки прийняття рішень від 16 до 20 % в залежності від кількості інформації про об’єкт моніторингу

/pdf/development-of-complex-methodology-of-processing-2tj3jp2pb3.pdf

Development of complex methodology of processing heterogeneous data in intelligent decision support systems

Big Data have emerged and become the norm in the past decade with its well-known 4V challenges and now adds value (a 5th V) for scientific research and the development of new applications. Such Big...

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2019.1698743

Big Spatiotemporal Data Analytics: a research and innovation frontier

The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration

Major natural disasters have occurred frequently in the last few years, resulting in increased loss of life and economic damage. Most emergency responders do not have first-hand experience with major natural disasters, and thus, there is an urgent need for pre-disaster training. Due to the scenes unreality of traditional emergency drills, the failure to appeal to the target audience and the novel coronavirus pandemic, people are forced to maintain safe social distancing. Therefore, it is difficult to carry out transregional or transnational emergency drills in many countries under the lockdown. There is an increasing demand for simulation training systems that use virtual reality, augmented reality, and mixed reality visualization technologies to simulate major natural disasters. The simulation training system related to natural disasters provides a new way for popular emergency avoidance science education and emergency rescue personnel to master work responsibilities and improve emergency response capabilities. However, to our knowledge, there is no overview of the simulation training system for major natural disasters. Hence, this paper uncovers the visualization techniques commonly used in simulation training systems, and compares, analyses and summarizes the architecture and functions of the existing simulation training systems for different emergency phases of common natural disasters. In addition, the limitations of the existing simulation training system in practical applications and future development directions are discussed to provide reference for relevant researchers to better understand the modern simulation training system. 

/pdf/review-on-visualization-technology-in-simulation-training-1pagprwr.pdf

Review on visualization technology in simulation training system for major natural disasters

An integrated GIS platform architecture for spatiotemporal big data

The spatial optimization method between combinatorial optimization problems and GIS has many geographical applications. The p-center problem is a classic NP-hard location modeling problem, which has essential applications in many real-world scenarios, such as urban facility locations (ambulances, fire stations, pipelines maintenance centers, police stations, etc.). This study implements two methods to solve this problem: an exact algorithm and an approximate algorithm. Exact algorithms can get the optimal solution to the problem, but they are inefficient and time-consuming. The approximate algorithm can give the sub-optimal solution of the problem in polynomial time, which has high efficiency, but the accuracy of the solution is closely related to the initialization center point. We propose a new paradigm that combines a graph convolution network and greedy algorithm to solve the p-center problem through direct training and realize that the efficiency is faster than the exact algorithm. The accuracy is superior to the heuristic algorithm. We generate a large amount of p-center problems by the Erdos–Renyi graph, which can generate instances in many real problems. Experiments show that our method can compromise between time and accuracy and affect the solution of p-center problems.

/pdf/a-trade-off-algorithm-for-solving-p-center-problems-with-a-2gwffk0d.pdf

A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network

With the rapid development of geospatial data acquisition and processing technology, the scale of spatial data is expanding. Mass production applications put forward higher requirements for the performance of geospatial data analysis. In this study, we developed a geospatial big data analytics engine based on SuperMap iObject for Java and Apache Spark. The geospatial big data analytics engine can increase the RDD representation ability of spatial data. The spatial indexing can make the spatial calculation on the nodes of the Spark cluster distributed and efficient. The experimental results show that compared with the traditional algorithm, the geospatial big data analytics engine for Spark has better execution efficiency.

Geospatial Big Data Analytics Engine for Spark

The ability to quickly calculate or query the shortest path distance between nodes on a road network is essential for many real-world applications. However, the traditional graph traversal shortest path algorithm methods, such as Dijkstra and Floyd–Warshall, cannot be extended to large-scale road networks, or the traversal speed on large-scale networks is very slow, which is computational and memory intensive. Therefore, researchers have developed many approximate methods, such as the landmark method and the embedding method, to speed up the processing time of graphs and the shortest path query. This study proposes a new method based on landmarks and embedding technology, and it proposes a multilayer neural network model to solve this problem. On the one hand, we generate distance-preserving embedding for each node, and on the other hand, we predict the shortest path distance between two nodes of a given embedment. Our approach significantly reduces training time costs and is able to approximate the real distance with a relatively low Mean Absolute Error (MAE). The experimental results on a real road network confirm these advantages.

/pdf/ndist2vec-node-with-landmark-and-new-distance-to-vector-2im1jj3h.pdf

Ndist2vec: Node with Landmark and New Distance to Vector Method for Predicting Shortest Path Distance along Road Networks

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Papers

An integrated GIS platform architecture for spatiotemporal big data

A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network

Geospatial Big Data Analytics Engine for Spark

Ndist2vec: Node with Landmark and New Distance to Vector Method for Predicting Shortest Path Distance along Road Networks