Intelligent Analysis and Presentation of IOT Image Collection in Private Cloud

2021 ◽  
pp. 119-125
Author(s):  
Hongyu Zhang ◽  
Chaoen Xiao ◽  
Xiaodong Li ◽  
Beisheng Liu
Keyword(s):  
Private Cloud ◽  
Intelligent Analysis ◽  
Image Collection

Use of digital microscopy in process control during the manufacture of rubber-modified thermoplastics

1996 ◽  
Vol 54 ◽  
pp. 616-617
Author(s):  
M. T. Dineen
Keyword(s):  
Ccd Camera ◽  
Operating Costs ◽  
Hard Copy ◽  
Production Plant ◽  
Digital Microscopy ◽  
Nih Image ◽  
Transmission Electron ◽  
Conventional Film ◽  
Product Loss ◽  
Image Collection

The production of rubber modified thermoplastics can exceed rates of 30,000 pounds per hour. If a production plant needs to equilibrate or has an upset, that means operating costs and lost revenue. Results of transmission electron microscopy (TEM) can be used for process adjustments to minimize product loss. Conventional TEM, however, is not a rapid turnaround technique. The TEM process was examined, and it was determined that 50% of the time it took to complete a polymer sample was related to film processing, even when using automated equipment. By replacing the conventional film portion of the process with a commercially available system to digitally acquire the TEM image, a production plant can have the same TEM image in the control room within 1.5 hours of sampling.A Hitachi H-600 TEM Operated at 100 kV with a tungsten filament was retrofitted with a SEMICAPS™ image collection and processing workstation and a KODAK MEGAPLUS™ charged coupled device (CCD) camera (Fig. 1). Media Cybernetics Image-Pro Plus software was included, and connections to a Phaser II SDX printer and the network were made. Network printers and other PC and Mac software (e.g. NIH Image) were available. By using digital acquisition and processing, the time it takes to produce a hard copy of a digital image is greatly reduced compared to the time it takes to process film.

RANCANG BANGUN CLOUD COMPUTING DI LABORATORIUM KOMPUTER TEKNIK ELEKTRO UNIVERSITAS BANGKA BELITUNG

2014 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Ghiri Basuki Putra
Keyword(s):  
Cloud Computing ◽  
Software As A Service ◽  
Private Cloud ◽  
Private Cloud Computing

Cloud computing telah menjadi hal yang menarik untuk dibahas dikarenakan perkembangannya yang begitu pesat sejak pertama kali diperkenalkan mulai tahun 2000. Pemanfaatan cloud computing kepada penyimpanan data, pemakaian software secara bersama- sama serta penggunaan infrastruktur dan hardware pada jaringan atau komputer yang tergabung dalam sebuah cloud computing. Dengan cloud computing diharapkan adanya efesiensi dan kemudahan dalam  sumber daya baik software, data maupun hardware agar dapat digunakan bersama – sama. Perancangan cloud computing untuk laboratorium komputer Teknik Elektro Universitas Bangka Belitung bertujuan sebagai rancangan awal untuk pengembangan laboratorium komputer serta sebagai pusat pembelajaran dan penelitian cloud computing bagi mahasiswa Teknik Elektro. Perancangan cloud computing ini menggunakan metode Software as a Service (SaaS) dimana SaaS adalah layanan dari Cloud Computing dimana memakai software (perangkat lunak) yang telah disediakan sehingga tidak perlu setiap komputer di laboratorium menginstall software yang diperlukan selama tersedia di layanan Cloud Computing. Rancangan cloud computing di laboratorium menggunakan Private Cloud Computing merupakan pemodelan Cloud Computing yang memberikan lingkup yang lebih kecil untuk dapat memberikan layanan kepada pengguna tertentu misalnya pada sebuah jaringan komputer  lokal maupun pada skala perusahaan kecil maupun menengah.

PEMANFAATAN PRIVATE CLOUD STORAGE SEBAGAI MEDIA PENYIMPANAN DATA E-LEARNING PADA LEMBAGA PENDIDIKAN

2017 ◽  
Vol 10 (2) ◽  
Author(s):  
Irfan Santiko ◽  
Rahman Rosidi ◽  
Seta Agung Wibawa
Keyword(s):  
Cloud Storage ◽  
Private Cloud ◽  
E Learning

INTELLIGENT ANALYSIS OF RADAR DATA BASED ON FUZZY TRANSFORMS

2018 ◽  
Vol 77 (15) ◽  
pp. 1321-1329 ◽  
Author(s):  
S.V. Solonskaya ◽  
V. V. Zhirnov
Keyword(s):  
Radar Data ◽  
Intelligent Analysis

Arcus Cloud A Private Cloud Establishment

2018 ◽  
Vol 6 (3) ◽  
pp. 283-291
Author(s):  
Vishva Patel ◽  
◽  
Dhara Patel ◽  
Sunit Parmar ◽  
◽  
...  
Keyword(s):  
Private Cloud

scholarly journals Image collection strategies for single particle cryoEM

2021 ◽  
Vol 27 (S1) ◽  
pp. 1904-1906
Author(s):  
Farzad Jalali-Yazdi ◽  
Eric Gouaux
Keyword(s):  
Single Particle ◽  
Image Collection ◽  
Collection Strategies

Research on the strategy of intelligent analysis to improve sports person psychological experience in the era of artificial intelligence

2021 ◽  
pp. 1-11
Author(s):  
Lei Wu ◽  
Juan Wang ◽  
Long Jin ◽  
P. Hemalatha ◽  
R Premalatha
Keyword(s):  
Artificial Intelligence ◽  
Vital Role ◽  
Performance Ratio ◽  
Psychological Experience ◽  
The Public ◽  
Usable Knowledge ◽  
Intelligent Analysis ◽  
Accuracy Ratio ◽  
Depression Level ◽  
Predictive Research

Artificial intelligence (AI) is an excellent potential technology that is evolving day-to-day and a critical avenue for exploration in the world of computer science & engineering. Owing to the vast volume of data and the eventual need to turn this data into usable knowledge and realistic solutions, artificial intelligence approaches and methods have gained substantial prominence in the knowledge economy and community world in general. AI revolutionizes and raises athletics to an entirely different level. Although it is clear that analytics and predictive research have long played a vital role in sports, AI has a massive effect on how games are played, structured, and engaged by the public. Apart from these, AI helps to analyze the mental stability of the athletes. This research proposes the Artificial Intelligence assisted Effective Monitoring System (AIEMS) for the specific intelligent analysis of sports people’s psychological experience. The comparative analysis suggests the best AI strategies for analyzing mental stability using different criteria and resource factors. It is observed that the growth in the present incarnation indicates a promising future concerning AI use in elite athletes. The study ends with the predictive efficiency of particular AI approaches and procedures for further predictive analysis focused on retrospective methods. The experimental results show that the proposed AIEMS model enhances the athlete performance ratio of 98.8%, emotion state prediction of 95.7%, accuracy ratio of 97.3%, perception level of 98.1%, and reduces the anxiety and depression level of 15.4% compared to other existing models.

Sign in / Sign up

Export Citation Format

Share Document