scholarly journals Extending the scope of reference models for smart factories

2021 ◽  
Vol 180 ◽  
pp. 102-111
Author(s):  
Nuno Soares ◽  
Paula Monteiro ◽  
Francisco J. Duarte ◽  
Ricardo J. Machado
Keyword(s):  
Reference Models ◽  
Smart Factories

AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen
Keyword(s):  
Neural Networks ◽  
Drug Discovery ◽  
Drug Design ◽  
State Of The Art ◽  
The State ◽  
Generative Model ◽  
Lead Optimization ◽  
Scaffold Hopping ◽  
Reference Models ◽  
Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

Developing Dynamic Reference Models and a Decision Support Framework for Southeastern Ecosystems: An Integrated Approach

2015 ◽  
Author(s):  
L. K. Kirkman ◽  
John K. Hiers A. ◽  
L. L. Smith ◽  
L. M. Conner ◽  
S. L. Zeigler ◽  
...  
Keyword(s):  
Decision Support ◽  
Integrated Approach ◽  
Reference Models ◽  
Decision Support Framework

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals A guide to choosing and implementing reference models for social network analysis

2021 ◽  
Author(s):  
Elizabeth A. Hobson ◽  
Matthew J. Silk ◽  
Nina H. Fefferman ◽  
Daniel B. Larremore ◽  
Puck Rombach ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Reference Models

Multi-agent reinforcement learning for online scheduling in smart factories

2021 ◽  
Vol 72 ◽  
pp. 102202
Author(s):  
Tong Zhou ◽  
Dunbing Tang ◽  
Haihua Zhu ◽  
Zequn Zhang
Keyword(s):  
Reinforcement Learning ◽  
Online Scheduling ◽  
Multi Agent ◽  
Smart Factories

scholarly journals Sex-Specific Associations between Blood Pressure and Risk of Atrial Fibrillation Subtypes in the Tromsø Study

2021 ◽  
Vol 10 (7) ◽  
pp. 1514
Author(s):  
Hilde Espnes ◽  
Jocasta Ball ◽  
Maja-Lisa Løchen ◽  
Tom Wilsgaard ◽  
Inger Njølstad ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Blood Pressure ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Pressure Management ◽  
Reference Models ◽  
Proportional Hazards Regression ◽  
Hazard Ratios ◽  
Increased Risk

The aim of this study was to explore sex-specific associations between systolic blood pressure (SBP), hypertension, and the risk of incident atrial fibrillation (AF) subtypes, including paroxysmal, persistent, and permanent AF, in a general population. A total of 13,137 women and 11,667 men who participated in the fourth survey of the Tromsø Study (1994–1995) were followed up for incident AF until the end of 2016. Cox proportional hazards regression analysis was conducted using fractional polynomials for SBP to provide sex- and AF-subtype-specific hazard ratios (HRs) for SBP. An SBP of 120 mmHg was used as the reference. Models were adjusted for other cardiovascular risk factors. Over a mean follow-up of 17.6 ± 6.6 years, incident AF occurred in 914 (7.0%) women (501 with paroxysmal/persistent AF and 413 with permanent AF) and 1104 (9.5%) men (606 with paroxysmal/persistent AF and 498 with permanent AF). In women, an SBP of 180 mmHg was associated with an HR of 2.10 (95% confidence interval [CI] 1.60–2.76) for paroxysmal/persistent AF and an HR of 1.80 (95% CI 1.33–2.44) for permanent AF. In men, an SBP of 180 mmHg was associated with an HR of 1.90 (95% CI 1.46–2.46) for paroxysmal/persistent AF, while there was no association with the risk of permanent AF. In conclusion, increasing SBP was associated with an increased risk of both paroxysmal/persistent AF and permanent AF in women, but only paroxysmal/persistent AF in men. Our findings highlight the importance of sex-specific risk stratification and optimizing blood pressure management for the prevention of AF subtypes in clinical practice.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

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