scholarly journals Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 807 ◽  
Author(s):  
Jude Adeleke ◽  
Deshendran Moodley ◽  
Gavin Rens ◽  
Aderemi Adewumi
Keyword(s):  
Machine Learning ◽  
Monitoring And Control ◽  
Statistical Machine Learning ◽  
Sensor Web ◽  
Semantic Sensor Web ◽  
And Control ◽  
Proactive Monitoring

scholarly journals Enabling Smart Buildings by Indoor Visible Light Communications and Machine Learning

2019 ◽  
Author(s):  
Shuping Dang ◽  
Guoqing Ma ◽  
Basem Shihada ◽  
Mohamed-Slim Alouini
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Data Transmission ◽  
Visible Light Communications ◽  
Monitoring And Control ◽  
Smart Building ◽  
Wide Range ◽  
Tremendous Amount ◽  
Processing Techniques ◽  
And Control

<pre>The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers. The SB is capable of sensing, acquiring and processing a tremendous amount of data as well as performing proper action and adaptation accordingly. With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques are insufficient to provide satisfactory services. To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing and reasoning abilities. In this regard, we envision an SB framework enabled by indoor VLC and ML in this article.</pre>

Environmental building monitoring and control based on machine learning and fog computing on an IoT architecture

2020 ◽  
Author(s):  
Rafael L. Patrao ◽  
Francisco L. de Caldas Filho ◽  
Lucas M. C. e Martins ◽  
Gerson do N. Silva ◽  
Matheus S. Monteiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Monitoring And Control ◽  
Building Monitoring ◽  
And Control

scholarly journals Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 11910
Author(s):  
Dalia Mahmoud ◽  
Marcin Magolon ◽  
Jan Boer ◽  
M.A Elbestawi ◽  
Mohammad Ghayoomi Mohammadi
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Sensor Data ◽  
Learning Approaches ◽  
Monitoring And Control ◽  
In Situ Sensors ◽  
Applications Of Machine Learning ◽  
And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

scholarly journals Aflatoxin detection on direction of the 4.0 age at 3.0 costs

2019 ◽  
Vol 7 (7) ◽  
pp. 338-346 ◽  
Author(s):  
Mariana Matulovic ◽  
Flávio José de Oliveira Morais ◽  
Angela Vacaro de Souza ◽  
Cleber Aalexandre de Amorim ◽  
Luiz Fernando Sommaggio Coletta
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Environmental Variables ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Toxic Metabolite ◽  
Monitoring And Control ◽  
Carcinogenic Effects ◽  
And Control

Articulate the most diverse and sophisticated technologies, such as Remote Sensing, Big Data, Cloud Computing, Internet of Things, 3D Printing, among others, is part of universe 4.0, whether industrial or agricultural. Focusing on agricultural context, this paper proposes a low-cost 4.0 device to perform the monitoring and control of certain environmental variables for the detection of aflatoxins in peanut crops. Aflatoxins are toxic metabolite of fungi genus Aspergillus that can cause toxic and carcinogenic effects in humans and animals. The device developed was able to monitor temperature and humidity variations helping the aflatoxins identification. The equipment portability allows its use in silos with encapsulation via Additive Manufacturing, besides the aflatoxin prediction from Machine Learning algorithms.

scholarly journals Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2344 ◽  
Author(s):  
Federico Pittino ◽  
Michael Puggl ◽  
Thomas Moldaschl ◽  
Christina Hirschl
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
Control Charts ◽  
Industry 4.0 ◽  
Production Environment ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
And Control

Anomaly detection is becoming increasingly important to enhance reliability and resiliency in the Industry 4.0 framework. In this work, we investigate different methods for anomaly detection on in-production manufacturing machines taking into account their variability, both in operation and in wear conditions. We demonstrate how the nature of the available data, featuring any anomaly or not, is of importance for the algorithmic choice, discussing both statistical machine learning methods and control charts. We finally develop methods for automatic anomaly detection, which obtain a recall close to one on our data. Our developed methods are designed not to rely on a continuous recalibration and hand-tuning by the machine user, thereby allowing their deployment in an in-production environment robustly and efficiently.

scholarly journals IOT-HML: A Hybrid Machine Learning Technique for IoT Enabled Industrial Monitoring and Control System

2021 ◽  
Author(s):  
Kota P.N. ◽  
Chandak A.S. ◽  
Patil B.P.
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Information Security ◽  
Monitoring And Control ◽  
Machine Learning Technique ◽  
Monitoring And Control System ◽  
Learning Technique ◽  
Industrial Monitoring ◽  
Hybrid Machine ◽  
And Control

Abstract Industry 4.0 makes manufacturers more vulnerable to current challenges and makes it easier to adapt to market changes. This will increase the speed of innovation, make it more customer-oriented and lead to faster design processes. It is essential to focus on monitoring and controlling the production system before complex accidents occur. Moreover, an industrial control system facing information security problems in recent times because of the nature of IoT which affects the evaluation of abnormal predication. To overcome above research gaps, we shift to industrial 4.0 which combine IoT and mechanism learning for industrial monitor and manage. We propose a hybrid machine learning technique for IoT enabled industrial monitoring and control system (IoT-HML). Here, we concentrate both information security issues with accurate monitoring and control system. The first section of proposed IoT-HML system is to introduce the cat induced wheel optimization (IWO) algorithm for cluster formation. The process consists of clustering and cluster head (CH) selection. The source node forward information to destination through CH only which avoids the unwanted data loss and improve the security, because the information travel through trusted path. For route selection process, we utilize the cuckoo search algorithm to compute the optimal best path among multiples. In second section, we illustrate a coach and player learned neural network (CP-LNN) for monitoring the industrial and prevent from accidents by basic control strategies. Finally, the proposed IoT-HML system can evaluate with different set of data’s to prove the effectiveness.

IoT Based monitoring and control of fluid transportation using machine learning

2021 ◽  
Vol 89 ◽  
pp. 106899
Author(s):  
Priyanka E. Bhaskaran ◽  
C. Maheswari ◽  
S. Thangavel ◽  
M. Ponnibala ◽  
T. Kalavathidevi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monitoring And Control ◽  
And Control
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