scholarly journals Seamless Rendering of Large Scale Terrain

2012 ◽  
Vol 6-7 ◽  
pp. 1026-1030 ◽  
Author(s):  
Bao Song Deng ◽  
Tie Qing Deng ◽  
Rong Huan Yu ◽  
Jia Wei Yu
Keyword(s):  
Data Exchange ◽  
Large Scale ◽  
Computer Graphic ◽  
Open Data ◽  
Research Topic ◽  
Dependent Data ◽  
Huge Amount ◽  
Terrain Rendering ◽  
Data Control ◽  
Active Research

Terrain rendering has long been an active research topic in computer graphic and virtual reality. If large and detailed, digital terrains can be represented by a huge amount of data and therefore of graphical primitives to render in real-time. A dynamic, realistic and seamless rendering scheme for large scale terrain was proposed in this paper, based on successive LOD tiles and GPU acceleration. Multi-resolution girds and images were used for view-dependent data control and grid simplification, and multi-thread mechanism was employed for visibility clipping and data exchange between memory and disk, at the same time, a seamless combination algorithm between tiles of terrain and texture was proposed. Experimental results of real scenes with open data and comparisons with traditional method demonstrate the efficiency and practicality of our method.

scholarly journals Data Combination for Problem-Solving: A Case of an Open Data Exchange Platform

2021 ◽  
Author(s):  
Teruaki Hayashi ◽  
Hiroki Sakaji ◽  
Hiroyasu Matsushima ◽  
Yoshiaki Fukami ◽  
Takumi Shimizu ◽  
...  
Keyword(s):  
Problem Solving ◽  
Data Exchange ◽  
Large Scale ◽  
Open Data ◽  
Data Combination ◽  
Data Exchange Platform ◽  
Large Scale Data ◽  
Multiple Datasets ◽  
Combining Data ◽  
Shed Light

AbstractIn recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data exchange, but the characteristics of data that contribute to problem-solving through data combination have not been fully understood. In big data and interdisciplinary data combinations, large-scale data with many variables are expected to be used, and value is expected to be created by combining data as much as possible. In this study, we conducted three experiments to investigate the characteristics of data, focusing on the relationships between data combinations and variables in each dataset, using empirical data shared by the local government. The results indicate that even datasets that have a few variables are frequently used to propose solutions for problem-solving. Moreover, we found that even if the datasets in the solution do not have common variables, there are some well-established solutions to these problems. The findings of this study shed light on the mechanisms behind data combination for solving problems involving multiple datasets and variables.

scholarly journals Synthesis of Metal Organic Frameworks by Ball-Milling

Crystals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Cheng-An Tao ◽  
Jian-Fang Wang
Keyword(s):  
Ball Milling ◽  
Large Scale ◽  
Raw Materials ◽  
Metal Organic Frameworks ◽  
Research Topic ◽  
Mass Ratio ◽  
Time Consumption ◽  
Metal Organic ◽  
Short Time ◽  
Made In

Metal-organic frameworks (MOFs) have been used in adsorption, separation, catalysis, sensing, photo/electro/magnetics, and biomedical fields because of their unique periodic pore structure and excellent properties and have become a hot research topic in recent years. Ball milling is a method of small pollution, short time-consumption, and large-scale synthesis of MOFs. In recent years, many important advances have been made. In this paper, the influencing factors of MOFs synthesized by grinding were reviewed systematically from four aspects: auxiliary additives, metal sources, organic linkers, and reaction specific conditions (such as frequency, reaction time, and mass ratio of ball and raw materials). The prospect for the future development of the synthesis of MOFs by grinding was proposed.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

scholarly journals Prevention of Mountain Disasters and Maintenance of Residential Area through Real-Time Terrain Rendering

2021 ◽  
Vol 13 (5) ◽  
pp. 2950
Author(s):  
Su-Kyung Sung ◽  
Eun-Seok Lee ◽  
Byeong-Seok Shin
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
High Speed ◽  
Large Scale ◽  
Civil Engineering ◽  
Three Dimensional ◽  
Sampled Data ◽  
Residential Areas ◽  
Terrain Rendering ◽  
Mountain Disasters

Climate change increases the frequency of localized heavy rains and typhoons. As a result, mountain disasters, such as landslides and earthworks, continue to occur, causing damage to roads and residential areas downstream. Moreover, large-scale civil engineering works, including dam construction, cause rapid changes in the terrain, which harm the stability of residential areas. Disasters, such as landslides and earthenware, occur extensively, and there are limitations in the field of investigation; thus, there are many studies being conducted to model terrain geometrically and to observe changes in terrain according to external factors. However, conventional topography methods are expressed in a way that can only be interpreted by people with specialized knowledge. Therefore, there is a lack of consideration for three-dimensional visualization that helps non-experts understand. We need a way to express changes in terrain in real time and to make it intuitive for non-experts to understand. In conventional height-based terrain modeling and simulation, there is a problem in which some of the sampled data are irregularly distorted and do not show the exact terrain shape. The proposed method utilizes a hierarchical vertex cohesion map to correct inaccurately modeled terrain caused by uniform height sampling, and to compensate for geometric errors using Hausdorff distances, while not considering only the elevation difference of the terrain. The mesh reconstruction, which triangulates the three-vertex placed at each location and makes it the smallest unit of 3D model data, can be done at high speed on graphics processing units (GPUs). Our experiments confirm that it is possible to express changes in terrain accurately and quickly compared with existing methods. These functions can improve the sustainability of residential spaces by predicting the damage caused by mountainous disasters or civil engineering works around the city and make it easy for non-experts to understand.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

Advances in Water Treatment Application of Sepiolite Mineral Materials

2013 ◽  
Vol 710 ◽  
pp. 217-220 ◽  
Author(s):  
Fei Wang ◽  
Lei Feng ◽  
Meng Ran Tang ◽  
Ji Yuan Li ◽  
Qing Guo Tang
Keyword(s):  
Water Treatment ◽  
Size Effect ◽  
Large Scale ◽  
Surface Effect ◽  
Development Trend ◽  
High Energy ◽  
Research Area ◽  
Group Mineral ◽  
Active Research ◽  
Treatment Application

Synthetic nanomaterials have the disadvantages of large-scale investment, high energy consumption, complex production process and heavy environmental load. Mineral nanomaterials such as sepiolite group mineral nanomaterials are characterized by small size effect, quantum size effect and surface effect. Water treatment application of sepiolite group mineral nanomaterials has become an active research area and showed good development and application prospects. Based on the above reasons, this paper systematically summarizes the water treatment application of sepiolite group mineral nanomaterials, and development trend related to water treatment application of sepiolite group mineral nanomaterials were also proposed.

scholarly journals Comparison of Three Different Parallel Computation Methods for a Two-Dimensional Dam-Break Model

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Shanghong Zhang ◽  
Wenda Li ◽  
Zhu Jing ◽  
Yujun Yi ◽  
Yong Zhao
Keyword(s):  
Parallel Computation ◽  
Data Exchange ◽  
Large Scale ◽  
Good Choice ◽  
Dam Break ◽  
Two Dimensional ◽  
Parallel Method ◽  
Parallel Methods ◽  
Storage Area ◽  
Speedup Factor

Three parallel methods (OpenMP, MPI, and OpenACC) are evaluated for the computation of a two-dimensional dam-break model using the explicit finite volume method. A dam-break event in the Pangtoupao flood storage area in China is selected as a case study to demonstrate the key technologies for implementing parallel computation. The subsequent acceleration of the methods is also evaluated. The simulation results show that the OpenMP and MPI parallel methods achieve a speedup factor of 9.8× and 5.1×, respectively, on a 32-core computer, whereas the OpenACC parallel method achieves a speedup factor of 20.7× on NVIDIA Tesla K20c graphics card. The results show that if the memory required by the dam-break simulation does not exceed the memory capacity of a single computer, the OpenMP parallel method is a good choice. Moreover, if GPU acceleration is used, the acceleration of the OpenACC parallel method is the best. Finally, the MPI parallel method is suitable for a model that requires little data exchange and large-scale calculation. This study compares the efficiency and methodology of accelerating algorithms for a dam-break model and can also be used as a reference for selecting the best acceleration method for a similar hydrodynamic model.

Mobile Agent-Based Computing Resource and Usage Monitoring at Large Scale Computer Centers

2011 ◽  
Author(s):  
Zhixin Tie ◽  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Monitoring System ◽  
Mobile Agent ◽  
Computer Games ◽  
Data Exchange ◽  
Large Scale ◽  
Agent Based ◽  
Computer Center ◽  
Control Command ◽  
Computer Centers ◽  
Computer Resources

Mobile agent technology has become an important approach for the design and development of distributed systems. However, there is little research regarding the monitoring of computer resources and usage at large scale distributed computer centers. This paper presents a mobile agent-based system called the Mobile Agent Based Computer Monitoring System (MABCMS) that supports the dynamic sending and executing of control command, dynamic data exchange, and dynamic deployment of mobile code in C/C++. Based on the Mobile-C library, agents can call low level functions in binary dynamic or static libraries, and thus can monitor computer resources and usage conveniently and efficiently. Two experimental applications have been designed using the MABCMS. The experiments were conducted in a university computer center with hundreds of computer workstations and 15 server machines. The first experiment uses the MABCMS to detect improper usage of the computer workstations, such as playing computer games. The second experimental application uses the MABCMS to detect system resources such as available hard disk space. The experiments show that the mobile agent based monitoring system is an effective method for detecting and interacting with students playing computer games and a practical way to monitor computer resources in large scale distributed computer centers.

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