scholarly journals Quality Prediction of Continuous Ultrasonic Welded Seams of High-Performance Thermoplastic Composites by means of Artificial Intelligence

2021 ◽  
Vol 55 ◽  
pp. 116-123
Author(s):  
D. Görick ◽  
L. Larsen ◽  
M. Engelschall ◽  
A. Schuster
Keyword(s):  
Artificial Intelligence ◽  
High Performance ◽  
Thermoplastic Composites ◽  
Quality Prediction

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

Gasoline quality prediction using gas chromatography and FTIR spectroscopy: An artificial intelligence approach

Fuel ◽  
2006 ◽  
Vol 85 (4) ◽  
pp. 553-558 ◽  
Author(s):  
K. Brudzewski ◽  
A. Kesik ◽  
K. Kołodziejczyk ◽  
U. Zborowska ◽  
J. Ulaczyk
Keyword(s):  
Artificial Intelligence ◽  
Gas Chromatography ◽  
Ftir Spectroscopy ◽  
Quality Prediction ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Custom Built of Smart Computing Platform for Supporting Optimization Methods and Artificial Intelligence Research

2021 ◽  
Vol 58 (S) ◽  
pp. 59-64
Author(s):  
Indar Sugiarto ◽  
Doddy Prayogo ◽  
Henry Palit ◽  
Felix Pasila ◽  
Resmana Lim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Message Passing Interface ◽  
Optimization Methods ◽  
Computer Hardware ◽  
Production Environment ◽  
Computing Platform ◽  
Commercial Off The Shelf

This paper describes a prototype of a computing platform dedicated to artificial intelligence explorations. The platform, dubbed as PakCarik, is essentially a high throughput computing platform with GPU (graphics processing units) acceleration. PakCarik is an Indonesian acronym for Platform Komputasi Cerdas Ramah Industri Kreatif, which can be translated as “Creative Industry friendly Intelligence Computing Platform”. This platform aims to provide complete development and production environment for AI-based projects, especially to those that rely on machine learning and multiobjective optimization paradigms. The method for constructing PakCarik was based on a computer hardware assembling technique that uses commercial off-the-shelf hardware and was tested on several AI-related application scenarios. The testing methods in this experiment include: high-performance lapack (HPL) benchmarking, message passing interface (MPI) benchmarking, and TensorFlow (TF) benchmarking. From the experiment, the authors can observe that PakCarik's performance is quite similar to the commonly used cloud computing services such as Google Compute Engine and Amazon EC2, even though falls a bit behind the dedicated AI platform such as Nvidia DGX-1 used in the benchmarking experiment. Its maximum computing performance was measured at 326 Gflops. The authors conclude that PakCarik is ready to be deployed in real-world applications and it can be made even more powerful by adding more GPU cards in it.

Mechanical, Electrical and Thermal Behaviour of Additively Manufactured Thermoplastic Composites for High Performance Applications

2021 ◽  
pp. 167-199
Author(s):  
Praveenkumara Jagadeesh ◽  
Madhu Puttegowda ◽  
Yashas Gowda Thyavihalli Girijappa ◽  
Sanjay Mavinkere Rangappa ◽  
Munish Kumar Gupta ◽  
...  
Keyword(s):  
Thermal Behaviour ◽  
High Performance ◽  
Thermoplastic Composites

Intelligent Processes in Automated Production Involving Industry 4.0 Technologies and Artificial Intelligence

2021 ◽  
pp. 634-655
Author(s):  
Alessandro Massaro ◽  
Nicola Contuzzi ◽  
Angelo Galiano
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Process Simulation ◽  
Industry 4.0 ◽  
Process Reengineering ◽  
Quality Prediction ◽  
Automated Production ◽  
Industry Research ◽  
Processing Techniques ◽  
Technology Upgrading

The chapter presents different case studies involving technology upgrading involving Industry 4.0 technologies and artificial intelligence. The work analyzes four cases of study of industry projects related to manufacturing process of kitchen, tank production, pasta production, and electronic welding check. All the cases of study concern the analysis of engineered processes and the inline implementation of image vision techniques. The chapter discusses other topics involved in the production process such as augmented reality, quality prediction and predictive maintenance. The classic methodologies to map production processes are matched with innovative technologies of image segmentation and data mining predicting defects, machine failures, and product quality. The goal of the chapter is to prove how the combination of image processing techniques, data mining approaches, process simulation, chart process modeling, and process reengineering can constitute a scientific research project in industry research.

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