Fuzzy Inference Based Stability Optimization for Iot Data Centers Dc Microgrids: Impact of Constant Power Loads on Smart Grid Communication Over the Powerline

Oyekanlu, Emmanuel

doi:10.37798/20196811

Cited by 2 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Designing reliable remotecontrol interfaces when AGVs and AMRs are deployed for such unusual missions may include application of AI paradigms that support using small amount of available data sets and interpretable models. Example of such AI paradigms include explainable AI (XAI) [86] [298], deterministic AI algorithms [290], exact algorithms, reliable, low complexity fuzzy inference systems [292], [299], collaborative and predictive AI models [318], [320]. Design of these types of AI systems for AGVs and AMRs interfacing in real-time with humans continues to be important AoRs worldwide.…”

Section: B Agv/amr Fleet Integration With 5g Mec For Factory Of the Future Applications -Aorsmentioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

View full text Add to dashboard Cite

In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.INDEX TERMS Intelligent factory, factory of the future, 5G, smart manufacturing, industry 4.0, autonomous industrial equipment, AGV, AMR, tactile Internet, virtual reality, lean manufacturing.

show abstract

Section: B Agv/amr Fleet Integration With 5g Mec For Factory Of the Future Applications -Aorsmentioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 1, cameras that can transmit data using PLC are now being used for monitoring of power system assets installed in remote locations [1]. PLC is also being extensively used for broadband Internet applications [2], consumer home automation applications [3], facilitating grid-wide artificial intelligence (AI) applications [4][5][6][7][8][9][10], monitoring grid health using PLC modems as sensors [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band

Oyekanlu¹,

Uddin²

2020

Deterministic Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

In this chapter, the random forest-based ensemble regression method is used for the prediction of powerline impedance at the powerline communication (PLC) narrowband frequency range. It is discovered that while PLC load transfer function, phase, and frequency are crucial to powerline impedance estimation, the problem of data multicollinearity can adversely impact accurate prediction and lead to excessive mean square error (MSE). High MSE is obtained when multiple transfer functions corresponding to different PLC load transfer functions are used for random forest ensemble regression. Low MSE indicating more accurate impedance prediction is obtained when PLC load transfer function data is selectively used. Using data corresponding to 200, 400, 600, 800, and 1000 W PLC load transfer functions together led to poor impedance prediction, while using lesser amount of carefully selected data led to better impedance prediction. These results show that artificial intelligence (AI) methods such as random forest ensemble regression and deterministic data-optimization approach can be utilized for smart grid (SG) health monitoring applications using PLC-based sensors. Machine learning can also be applied to the design of better powerline communication signal transceivers and equalizers.

show abstract

Fuzzy Inference Based Stability Optimization for Iot Data Centers Dc Microgrids: Impact of Constant Power Loads on Smart Grid Communication Over the Powerline

Cited by 2 publications

References 23 publications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band

Contact Info

Product

Resources

About