Research on dynamic load characteristics and active control strategy of electro-mechanical coupling powertrain of drum shearer cutting unit under impact load

In order to extend the service life of the long-chain gear transmission system of a drum shearer, an electro-mechanical coupling model of a drum shearer cutting unit is established. The model considers the dynamic characteristics of the motor, time-varying meshing stiffness, as well as the drum load characteristics. Additionally, the dynamic characteristics and control strategy for suppressing the dynamic load of the gear transmission system under impact load are investigated based on this model. Firstly, the influence of the gear transmission system of the drum shearer cutting unit under impact load is analyzed. Then, on that basis, the active control strategy based on motor torque compensation is proposed to suppress the dynamic load of the gear transmission system caused by mutational external load. Finally, the suppression effect on the dynamic load of the gear transmission system is analyzed. Research results indicate that this control strategy has good control effects to suppress the dynamic load caused by a mutational external load, which confirms the effectiveness of the proposed control strategy

Straggler Mitigation and Latency Optimization in Blockchain-based Hierarchical Federated Learning

Cloud-edge-device hierarchical federated learning (HFL) has been recently proposed to achieve communication-efficient and privacy-preserving distributed learning. However, there exist several critical challenges, such as the single point of failure and potential stragglers in both edge servers and local devices. To resolve these issues, we propose a decentralized and straggler-tolerant blockchain-based HFL (BHFL) framework. Specifically, a Raft-based consortium blockchain is deployed on edge servers to provide a distributed and trusted computing environment for global model aggregation in BHFL. To mitigate the influence of stragglers on learning, we propose a novel aggregation method, HieAvg, which utilizes the historical weights of stragglers to estimate the missing submissions. Furthermore, we optimize the overall latency of BHFL by jointly considering the constraints of global model convergence and blockchain consensus delay. Theoretical analysis and experimental evaluation show that our proposed BHFL based on HieAvg can converge in the presence of stragglers, which performs better than the traditional methods even when the loss function is non-convex and the data on local devices are non-independent and identically distributed (non-IID)

Shaping a subwavelength needle with ultra-long focal length by focusing azimuthally polarized light

10.1038/srep09977Scientific Reports

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes

Influence of motor control characteristics on load sharing behavior of torque coupling gear set

The influence of the control characteristics of a direct torque control (DTC) motor on the load sharing behavior of a torque coupling gear set is studied by establishing a dynamic model of a multi-motor torque coupling system. The effects of run-out errors of the pinions of the torque coupling gear set on the powertrain are analyzed under steady operating conditions. The difference in rotational speed among the pinions becomes apparent, and the load sharing behavior deteriorates when there are run-out errors. The relationship between the control characteristics and load sharing behavior is studied. The difference in rotational speed decreases and the load sharing behavior improves as proportional and integral gains of the speed controller increase

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes