Deep Reinforcement Learning Based Joint Edge Resource Management in Maritime Network

Due to the rapid development of the maritime networks, there has been a growing demand for computation-intensive applications which have various energy consumption, transmission bandwidth and computing latency requirements. Mobile edge computing(MEC) can efficiently minimize computational latency by offloading computation tasks by the terrestrial access network. In this work, we introduce a space-air-ground-sea integrated network architecture with edge and cloud computing components to provide flexible hybrid computing service for maritime service. In the integrated network, satellites and unmanned aerial vehicles(UAVs) provide the users with edge computing services and network access. Based on the architecture, the joint communication and computation resource allocation problem is modelled as a complex decision process, and a deep reinforcement learning based solution is designed to solve the complex optimization problem. Finally, numerical results verify that the proposed approach can improve the communication and computing efficiency greatly.

Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio(SNR) circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine(ELM) to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR(0 d B) environment in the time-varying multipath Rayleigh fading channel.

Collaborative Clustering Parallel Reinforcement Learning for Edge-Cloud Digital Twins Manufacturing System

To realize high-accuracy physical-cyber digital twin(DT)mapping in a manufacturing system,a huge amount of data need to be collected and analyzed in real-time.Traditional DTs systems are deployed in cloud or edge servers independently,whilst it is hard to apply in real production systems due to the high interaction or execution delay.This results in a low consistency in the temporal dimension of the physical-cyber model.In this work,we propose a novel efficient edge-cloud DT manufacturing system,which is inspired by resource scheduling technology.Specifically,an edge-cloud collaborative DTs system deployment architecture is first constructed.Then,deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications.We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning(CCPQL)algorithm and prediction-based CCPQL to solve the problems.The proposed approach reduces the total delay with a higher convergence rate.Numerical simulation results are provided to validate the approach,which would have great potential in dynamic and complex industrial internet environments.

Distribution of chlorpyrifos in rice paddy environment and its potential dietary risk

Chlorpyrifos is one of the most extensively used insecticides in China. The distribution and residues of chlorpyrifos in a paddy environment were characterized under field and laboratory conditions. The half-lives of chlorpyrifos in the two conditions were 0.9–3.8 days(field) and 2.8–10.3 days(laboratory), respectively. The initial distribution of chlorpyrifos followed the increasing order of water < straw < soil, and soil was characterized as the major absorber. The ultimate residues in rice grain were below the maximum residue limit(MRL) with a harvest interval of 14 days. The chronic exposure for chlorpyrifos was rather low compared to the acceptable daily intake(ADI = 0.01 mg/kg bw) due to rice consumption. The chronic exposure risk from chlorpyrifos in rice grain was 5.90% and 1.30% ADI from field and laboratory results respectively. Concerning the acute dietary exposure,intake estimated for the highest chlorpyrifos level did not exceed the acute reference dose(ARf D = 0.1 mg/kg bw). The estimated short-term intakes(ESTIs) were 0.78% and 0.25% of the ARf D for chlorpyrifos. The results showed that the use of chlorpyrifos in rice paddies was fairly safe for consumption of rice grain by consumers.