scholarly journals Modeling simulation and virtual platform construction of crossbow making technology

2019 ◽  
Vol 136 ◽  
pp. 03032
Author(s):  
Cong Wen ◽  
Wu Xu ◽  
Wenquan Tang ◽  
Xing Guo ◽  
Bing Zhou
Keyword(s):  
Data Storage ◽  
Storage Management ◽  
3D Interaction ◽  
Data Set ◽  
Organization Model ◽  
Arithmetic Average ◽  
Modeling Simulation ◽  
Virtual Platform ◽  
Query Operations ◽  
Entity Class

In this paper, a set of virtual simulation platform for crossbow making is established. First, build a virtual model of Lisu crossbow, optimize the model, accelerate the organization, model generation, modification and editing of the database; then complete the database establishment of the corresponding model, nest the comment entity class and filemeta entity class into the file entity class, store them in a nested data set, and complete the data storage, management and query operations; finally, combined with Kalman filtering, arithmetic average filtering and KNN algorithm are used to build the quest 3D virtual platform to improve the 3D interaction effect with data gloves and complete the construction of the 3D platform.

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

Data Storage Management in Cloud Environments

2018 ◽  
Vol 50 (6) ◽  
pp. 1-51 ◽  
Author(s):  
Yaser Mansouri ◽  
Adel Nadjaran Toosi ◽  
Rajkumar Buyya
Keyword(s):  
Data Storage ◽  
Storage Management ◽  
Cloud Environments

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

scholarly journals Heterogeneous Data Storage Management with Deduplication in Cloud Computing

2019 ◽  
Vol 5 (3) ◽  
pp. 393-407 ◽  
Author(s):  
Zheng Yan ◽  
Lifang Zhang ◽  
Wenxiu Ding ◽  
Qinghua Zheng
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Heterogeneous Data ◽  
Storage Management
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