scholarly journals Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82844-82854 ◽  
Author(s):  
Zhaoyang Qu ◽  
Qianhui Xie ◽  
Yuqing Liu ◽  
Yang Li ◽  
Lei Wang ◽  
...  
Keyword(s):  
Markov Chain ◽  
Physical System ◽  
Cyber Physical System ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Risk Area ◽  
System Risk

scholarly journals Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

2021 ◽  
Vol 13 (6) ◽  
pp. 3314
Author(s):  
Rawan Shabbar ◽  
Anemone Kasasbeh ◽  
Mohamed M. Ahmed
Keyword(s):  
Markov Chain ◽  
Electric Vehicles ◽  
Process Model ◽  
Transportation Network ◽  
Optimal Placement ◽  
Death Process ◽  
Chain Model ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Net Profit

Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Optimal Conductor Selection Using Grey Wolf Optimization

2016 ◽  
Vol 4 (3) ◽  
pp. 39
Author(s):  
Ramanaiah M. LAXMIDEVI ◽  
REDDY M. DAMODAR ◽  
◽  
Keyword(s):  
Grey Wolf ◽  
Grey Wolf Optimization

Implementation of a Cyber-Physical System Using Wireless Sensor Networks for Monitoring Patients

2015 ◽  
Vol 1 ◽  
pp. 26 ◽  
Author(s):  
Vo Que Son ◽  
Do Tan A
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Physical System ◽  
Patient Monitoring ◽  
High Reliability ◽  
Vital Signs ◽  
Wireless Sensor ◽  
Cyber Physical System ◽  
Self Administration ◽  
Hospital Units

Sensing, distributed computation and wireless communication are the essential building components of a Cyber-Physical System (CPS). Having many advantages such as mobility, low power, multi-hop routing, low latency, self-administration, utonomous data acquisition, and fault tolerance, Wireless Sensor Networks (WSNs) have gone beyond the scope of monitoring the environment and can be a way to support CPS. This paper presents the design, deployment, and empirical study of an eHealth system, which can remotely monitor vital signs from patients such as body temperature, blood pressure, SPO2, and heart rate. The primary contribution of this paper is the measurements of the proposed eHealth device that assesses the feasibility of WSNs for patient monitoring in hospitals in two aspects of communication and clinical sensing. Moreover, both simulation and experiment are used to investigate the performance of the design in many aspects such as networking reliability, sensing reliability, or end-to-end delay. The results show that the network achieved high reliability - nearly 97% while the sensing reliability of the vital signs can be obtained at approximately 98%. This indicates the feasibility and promise of using WSNs for continuous patient monitoring and clinical worsening detection in general hospital units.

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