scholarly journals Service Orchestration for Object Detection on Edge and Cloud in Dependable Industrial Vehicles

2021 ◽  
Author(s):  
Henri Pettinen ◽  
David Hästbacka
Keyword(s):  
Object Detection ◽  
Time Use ◽  
Service Discovery ◽  
Heterogeneous Computing ◽  
Autonomous Systems ◽  
Industrial Applications ◽  
Use Case ◽  
Service Orchestration ◽  
Processing Data ◽  
Viable Solution

Industrial applications, including autonomous systems and vehicles, rely on processing data on multiple physical devices. The composition of functionality across heterogeneous computing infrastructure is challenging, and will likely get even more challenging in the future as software in vehicles is updated to introduce new features and ensure the safety. New soft real-time use cases emerge and in such cases the model of offloading processing from a limited or malfunctioning device is a viable solution. This study examines orchestration of services across edge and cloud for an industrial vehicle application use case involving image based object detection using machine learning (ML) based models. First, service orchestration requirements are defined taking into account the dependable nature of industrial vehicle applications. Second, an implementation based on Arrowhead framework is presented and evaluated. The open Arrowhead framework offers means for dynamic service discovery, authorization and late binding of computational units. The feasibility of object detection as a service and the suitability of Arrowhead framework to support such orchestrations across edge and cloud is assessed.

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

Incorporating LDA With Word Embedding for Web Service Clustering

2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Word Embedding ◽  
The Internet ◽  
Word Embeddings ◽  
Training Process ◽  
Web Service Discovery ◽  
Processing Data ◽  
Clustering Approach ◽  
Service Clustering

With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

Computer Vision-Based Non-Magnetic Object Detection on Moving Conveyors in Steel Industry through Differential Techniques and Performance Evaluation

2014 ◽  
pp. 247-261
Author(s):  
K. C. Manjunatha ◽  
H. S. Mohana ◽  
P. A. Vijaya
Keyword(s):  
Object Detection ◽  
Steel Industry ◽  
Material Handling ◽  
Raw Materials ◽  
Industrial Applications ◽  
Raw Material ◽  
Smart Control ◽  
Image Pixels ◽  
And Performance ◽  
Video Input

Intelligent process control technology in various manufacturing industries is important. Vision-based non-magnetic object detection on moving conveyor in the steel industry will play a vital role for intelligent processes and raw material handling. This chapter presents an approach for a vision-based system that performs the detection of non-magnetic objects on raw material moving conveyor in a secondary steel-making industry. At single camera level, a vision-based differential algorithm is applied to recognize an object. Image pixels-based differential techniques, optical flow, and motion-based segmentations are used for traffic parameters extraction; the proposed approach extends those futures into industrial applications. The authors implement a smart control system, since they can save the energy and control unnecessary breakdowns in a robust manner. The technique developed for non-magnetic object detection has a single static background. Establishing background and background subtraction from continuous video input frames forms the basis. Detection of non-magnetic materials, which are moving with raw materials, and taking immediate action at the same stage as the material handling system will avoid the breakdowns or power wastage. The authors achieve accuracy up to 95% with the computational time of not more than 1.5 seconds for complete system execution.

Computer Vision-Based Non-Magnetic Object Detection on Moving Conveyors in Steel Industry Through Differential Techniques and Performance Evaluation

2018 ◽  
pp. 1820-1837
Author(s):  
K. C. Manjunatha ◽  
H. S. Mohana ◽  
P. A. Vijaya
Keyword(s):  
Object Detection ◽  
Steel Industry ◽  
Material Handling ◽  
Raw Materials ◽  
Industrial Applications ◽  
Raw Material ◽  
Smart Control ◽  
Image Pixels ◽  
And Performance ◽  
Video Input

Intelligent process control technology in various manufacturing industries is important. Vision-based non-magnetic object detection on moving conveyor in the steel industry will play a vital role for intelligent processes and raw material handling. This chapter presents an approach for a vision-based system that performs the detection of non-magnetic objects on raw material moving conveyor in a secondary steel-making industry. At single camera level, a vision-based differential algorithm is applied to recognize an object. Image pixels-based differential techniques, optical flow, and motion-based segmentations are used for traffic parameters extraction; the proposed approach extends those futures into industrial applications. The authors implement a smart control system, since they can save the energy and control unnecessary breakdowns in a robust manner. The technique developed for non-magnetic object detection has a single static background. Establishing background and background subtraction from continuous video input frames forms the basis. Detection of non-magnetic materials, which are moving with raw materials, and taking immediate action at the same stage as the material handling system will avoid the breakdowns or power wastage. The authors achieve accuracy up to 95% with the computational time of not more than 1.5 seconds for complete system execution.

Machine Vision Based Non-Magnetic Object Detection and Removal on Moving Conveyors in Steel Industry through Differential Techniques

2012 ◽  
Vol 2 (3) ◽  
pp. 59-70
Author(s):  
K. C. Manjunatha ◽  
H. S. Mohana ◽  
P. A. Vijaya
Keyword(s):  
Object Detection ◽  
Steel Industry ◽  
Material Handling ◽  
Raw Materials ◽  
Industrial Applications ◽  
Vital Role ◽  
Raw Material ◽  
Computational Time ◽  
Video Input ◽  
Intelligent Process

Intelligent process control technology in various manufacturing industries is important. Vision based non-magnetic object detection on moving conveyor in the steel industry will play a vital role for intelligent process and raw material handling. This paper presents an approach for a vision based system which performs the detection of non-magnetic objects on raw material moving conveyor in a secondary steel making industry. At single camera level, a vision based differential algorithm is applied to recognize an object. Image pixels based differential techniques; optical flow and motion based segmentations are used for traffic parameters extraction, the proposed approach extends those futures into industrial applications. The authors can implement smart control system, since they can save the energy and control unnecessary breakdowns in a robust manner. The technique developed for non-magnetic object detection is having single static background. Establishing background and background subtraction from continuous video input frames forms the basis. Detection of non-magnetic materials which are moving with raw materials and taking immediate action at the same stage as material handling system will avoid the breakdowns or power wastage. The authors achieve accuracy up to 95% with the computational time of not more than 1.5 seconds for complete system execution.

scholarly journals PROLISEAN

2021 ◽  
Vol 21 (1) ◽  
pp. 1-29
Author(s):  
Edy Hourany ◽  
Bachir Habib ◽  
Camille Fountaine ◽  
Abdallah Makhoul ◽  
Benoit Piranda ◽  
...  
Keyword(s):  
Autonomous Systems ◽  
Industrial Applications ◽  
Security Protocol ◽  
Distributed Architecture ◽  
Programmable Matter ◽  
Huge Number ◽  
Lightweight Cryptography ◽  
Different Shapes ◽  
Encryption Algorithms ◽  
Hashing Function

The vision for programmable matter is to create a material that can be reprogrammed to have different shapes and to change its physical properties on demand. They are autonomous systems composed of a huge number of independent connected elements called particles. The connections to one another form the overall shape of the system. These particles are capable of interacting with each other and take decisions based on their environment. Beyond sensing, processing, and communication capabilities, programmable matter includes actuation and motion capabilities. It could be deployed in different domains and will constitute an intelligent component of the IoT. A lot of applications can derive from this technology, such as medical or industrial applications. However, just like any other technology, security is a huge concern. Given its distributed architecture and its processing limitations, programmable matter cannot handle the traditional security protocols and encryption algorithms. This article proposes a new security protocol optimized and dedicated for IoT programmable matter. This protocol is based on lightweight cryptography and uses the same encryption protocol as a hashing function while keeping the distributed architecture in mind. The analysis and simulation results show the efficiency of the proposed method and that a supercomputer will need about 5.93 × 10 25 years to decrypt the message.

scholarly journals Functionally Graded Materials (FGM) for Spacers in Gas Insulated Systems: A Concise Review and Some Comments

2021 ◽  
Vol 11 (6) ◽  
pp. 7887-7891
Author(s):  
K. L. Wong ◽  
M. Danikas
Keyword(s):  
Electric Field ◽  
Functionally Graded Materials ◽  
High Voltage ◽  
Electric Field Distribution ◽  
Industrial Applications ◽  
Functionally Graded ◽  
Graded Materials ◽  
Concise Review ◽  
Viable Solution ◽  
Future Challenges

Functionally Graded Materials (FGMs) present a solution to control electrical stresses in high voltage applications. In this paper, a concise review is presented on the FGMs for spacers in gas-insulated systems. FGMs offer the possibility of a more even electric field distribution and thus a viable solution for industrial applications. FGMs are investigated here primarily as materials for permittivity control. Some aspects of FGMs are discussed as well as some thoughts on future challenges.

Hand-Held Computing Devices for Industrial Applications: Some Case Studies

2010 ◽  
Author(s):  
Pingyu Jiang ◽  
Wei Cao ◽  
Qiqi Zhu ◽  
Yingbin Fu ◽  
Feng Jia
Keyword(s):  
Industrial Applications ◽  
Time Data ◽  
Huge Number ◽  
Industrial Activities ◽  
Processing Data ◽  
Real Time Data ◽  
Cross Platform ◽  
Basic Concepts ◽  
Collaborative Activities ◽  
Computing Models

This paper deals with an approach to the modes and methods of using hand-held computing devices in industry. The basic concepts and cross-platform computing models related to the hand-held computing devices are described firstly. Then three key and fundamental enabling technologies, including sampling real-time data, implementing interoperations with different database, and supporting collaborative activities, are presented in detail so as to use such mobile devices for potential industrial applications. Thirdly, five typical cases of using hand-held computing devices in design, manufacturing and logistic are studied in depth. Through analyzing and discussing the cases, four viewpoints are put forward: (1) using hand-held computing devices to supporting industrial activities is feasible; (2) hand-held computing devices play an important role in collecting, querying, scanning and simply processing data; (3) integrating hand-held computing devices with desktop computing devices together is needed if a huge number of data are involved in industrial applications; (4) hand-held computing devices will be used more widely and deeply in the future.

CLEVER

2012 ◽  
pp. 219-241
Author(s):  
Francesco Tusa ◽  
Maurizio Paone ◽  
Massimo Villari
Keyword(s):  
Internet Of Things ◽  
Heterogeneous Computing ◽  
Electronic Device ◽  
The Internet ◽  
Use Case ◽  
Grid Infrastructures ◽  
The Internet Of Things ◽  
Computing Infrastructure ◽  
Cloud Resources

This chapter describes both the design and architecture of the CLEVER cloud middleware, pointing out the possibilities it offers towards enlarging the concept of federation in more directions. CLEVER is able to accomplish such an enlargement enabling the interaction among whatever type of electronic device connected to Internet, thus offering the opportunity of implementing the Internet of Things. Together with this type of perspective, CLEVER aims to “aggregate” heterogeneous computing infrastructure by putting together Cloud and Grid, as an example. The chapter starts with a description of the cloud projects related to CLEVER, followed by a discussion on the middleware components that mainly focuses on the innovative features they have, in particular the communication mechanisms adopted. The second part of the chapter presents a real use case that exploits the CLEVER features that allow easy creation of federated clouds’ infrastructures that can be also based on integration with existing Grids; it is demonstrated thanks to the “oneshot” CLEVER deploying mechanism. It is possible to scale dynamically the cloud resources by taking advantage of the existing Grid infrastructures, and minimizing the changes needed at the involved management middleware.

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