Human-in-the-loop assistive cyber physical system control using physiological signals

Recently, cyber-physical system is widely used for smart system control in various fields. Various functions of the cyber-physical system must overcome the limited hardware resources constraint of an embedded system. In addition, the data required from the industrial cyber-physical system are critical; therefore, a highly secure encryption technique is required. However, security and computational throughput are incompatible with each other in the cryptographic technique; therefore, the industrial cyber-physical system needs to adopt a highly efficient and secure encryption technique considering the limited available resources. This study applies the m-folding method to the highly secure elliptic curve algorithm to improve efficiency and proposes the cryptosystem optimized for the resource-constrained industrial cyber-physical system. The proposed m-folding method–based elliptic curve encryption showed 50% faster encryption than the existing methods.

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High-Precision Modulation of a Safety-Critical Cyber-Physical System: Control Synthesis and Experimental Validation

IEEE/ASME Transactions on Mechatronics ◽

10.1109/tmech.2018.2833542 ◽

2018 ◽

Vol 23 (6) ◽

pp. 2599-2608 ◽

Cited By ~ 3

Author(s):

Yutong Li ◽

Chen Lv ◽

Junzhi Zhang ◽

Yun Zhang ◽

Wanjing Ma

Keyword(s):

High Precision ◽

Physical System ◽

Experimental Validation ◽

Control Synthesis ◽

Cyber Physical System ◽

System Control ◽

Safety Critical

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A human-in-the-loop cyber-physical system for collaborative assembly in smart manufacturing

Procedia CIRP ◽

10.1016/j.procir.2019.03.162 ◽

2019 ◽

Vol 81 ◽

pp. 600-605 ◽

Cited By ~ 8

Author(s):

Manuel A. Ruiz Garcia ◽

Rafael Rojas ◽

Luca Gualtieri ◽

Erwin Rauch ◽

Dominik Matt

Keyword(s):

Physical System ◽

Smart Manufacturing ◽

Cyber Physical System ◽

Human In The Loop ◽

Collaborative Assembly

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Human-in-the-Loop Predictive Analytics Using Statistical Learning

Journal of Healthcare Engineering ◽

10.1155/2021/9955635 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Anusha Ganesan ◽

Anand Paul ◽

Ganesan Nagabushnam ◽

Malik Junaid Jami Gul

Keyword(s):

Statistical Learning ◽

Physical System ◽

Mean Squared Error ◽

Predictive Analytics ◽

Learning Algorithm ◽

Linear Trend ◽

Cyber Physical System ◽

Medical Field ◽

Human In The Loop ◽

Squared Error

The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human’s input is much essential to accurately understand their health condition to take appropriate decision on time. Therefore, we have proposed a healthcare framework involving the concept of artificial intelligence in the human-in- the-loop cyber-physical system. This model works via a response loop in which the human’s intention is concluded by gathering biological signals and context data, and then, the decision is interpreted to a system action that is recognizable to the human in the physical environment, thereby completing the loop. In this paper, we have designed a model for early prognosis of coma using the electroencephalogram dataset. In the proposed approach, we have achieved the best results using a statistical learning algorithm called autoregressive integrated moving average in comparison to artificial neural networks and long short-term memory models. In order to measure the efficiency of our model, we have used the root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) value to evaluate the linear models as it gives the difference between the measured value and true or correct value. We have achieved the least possible error value for our dataset. To conduct this experiment, we used the dataset available in the phsyionet opensource community.

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