Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process...
详细信息
Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and ***-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor ***,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the *** this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster *** major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster *** achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence *** addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management *** experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.
In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,...
详细信息
In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.
In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative ne...
详细信息
Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accurac...
详细信息
Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a cl...
详细信息
Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.
The advancement of deep learning models has led to the creation of novel techniques for image and video synthesis. One such technique is the deepfake, which swaps faces among persons and then produces hyper-realistic ...
详细信息
Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature prop...
详细信息
This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random gra...
详细信息
We propose FlexibleBP, a novel cuffless blood pressure monitoring system using a wrist-worn flexible sensor to enhance comfort and accuracy. By capturing pulse wave signals from the radial artery, we develop a persona...
详细信息
Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and co...
详细信息
Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.
暂无评论