Automatically calibrated occupancy sensors for an ambient assisted living system

Fernández-Luque, Francisco J.; Pérez, David; Zapata, Juan; Ruíz, Ramón Eduardo

doi:10.3233/ica-160521

Cited by 2 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, more self-adaptive functions, like the nove global numerical optimization [95] and the adaptive regularization method [96], can be inserted into the membrane computing systems for a more rapid and robust matching result. Furthermore, more exquisite designs, as the electronic cluster eyes [97] and wireless sensors [98], are possible to make the system as a part of the internet of things. In addition, we only apply the most basic similarity matching strategy in this paper, without any machine learning algorithms, so we consider to introduce some advantaged machine learning approaches to improve the matching performance, like artificial neural network [11], random forests [99] and conditional random fields [63].…”

Section: B Future Workmentioning

confidence: 99%

Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework

Kong

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In this paper, a novel microscopic machine vision system is proposed to solve a degradation monitoring problem of low-voltage electromagnetic coil insulation in practical industrial fields, where an ensemble learning approach in a compound membrane computing framework is newly introduced. This membrane computing framework is constituted by eight layers, 29 membranes, 72 objects, and 35 rules. In this framework, multiple machine learning methods, including classical pattern recognition methods and novel deep learning methods, are tested and compared. First, the most optimal feature extraction approaches are selected. Then, the selected approaches are fused together to achieve an even better monitoring performance. Third, a large number of experiments are used to evaluate and prove the usefulness and potential of the proposed system, where a mean accuracy of 61.4% is achieved on 1035 validation images of six degradation states with single state matching, and mean accuracies of 61.0% and 77.4% are achieved on 622 test images of six degradation states with single state matching and state range matching, respectively. Finally, a mechanical device is designed to apply the system to real industrial tasks.

show abstract

Section: B Future Workmentioning

confidence: 99%

Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework

Kong

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Considering three types of dynamic loads, that is, earthquake, wind, and blast loads, Saleh and Adeli (, ) also demonstrate that LQR control algorithm is effective for vibration control of multistory building structures. Further, on the basis of the LQR control algorithm, Adeli and Saleh () present an innovative integrated structural/control optimization solution for active control of smart structures subjected to different dynamic loadings through adroit integration of four different computing paradigms and technologies: control theory, optimization theory (Wang & Szeto, ; Xu, Spencer, Lu, Chen, & Lu, ), sensor/actuator technology (Fernandez‐Luque, Perez, Zapata, & Ruiz, ; Matarazzo & Pakzad, ; Yin, Yuen, Lam, & Zhu, ), and high‐performance computing (Park, Torbol, & Kim, ). The solution is based on simultaneous minimization of structural weight and the required level of control forces using parallel processing on multiprocessor computers (Adeli & Kamal, ).…”

Section: Linear Control Algorithmsmentioning

confidence: 99%

“…control theory, optimization theory (Wang & Szeto, 2017;Xu, Spencer, Lu, Chen, & Lu, 2017), sensor/actuator technology (Fernandez-Luque, Perez, Zapata, & Ruiz, 2016;Matarazzo & Pakzad, 2018;Yin, Yuen, Lam, & Zhu, 2017), and high-performance computing (Park, Torbol, & Kim, 2018). The solution is based on simultaneous minimization of structural weight and the required level of control forces using parallel processing on multiprocessor computers (Adeli & Kamal, 1993).…”

Section: Linear Quadratic Regulator Controlmentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

View full text Add to dashboard Cite

Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

show abstract

Automatically calibrated occupancy sensors for an ambient assisted living system

Cited by 2 publications

References 25 publications

Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework

Microscopic Machine Vision Based Degradation Monitoring of Low-Voltage Electromagnetic Coil Insulation Using Ensemble Learning in a Membrane Computing Framework

Control methodologies for vibration control of smart civil and mechanical structures

Contact Info

Product

Resources

About