An integrated open-die forging centre can realize programmed incremental forging processes to improve safety and efficiency; the centre consists of a forging press and one or two heavy-duty manipulators. This paper focuses on the coordinated kinematic modelling of the integrated system. First a newly designed serial—parallel forging manipulator is presented and the closed-form inverse kinematic solution is derived based on homogeneous co-ordinate transformation. Then the deformation of the forged workpiece is investigated by the spread coefficient method of plastic deformation theory, and the relation between the motion of the holding end of the workpiece and the movement of the upper die is formulated. Finally, the coordinated kinematic modelling of the integrated system is established, where the press and the manipulator are coordinated in movement. Simulation of a typical open-die forging process shows that the gripper of the manipulator should have good compliance abilities in horizontal and vertical directions during forging; however, it should have high rotation stiffness with respect to the transverse axis. The automatic programmed forging plan and coordination control strategy can be pre-designed based on this model.
Multi-robot with advantages of spatial distribution and fault tolerance is competent for patrol missions and has the potential to be used in security and surveillance applications. This article focuses on the frequency-based patrol designed to guarantee the frequent access to key positions in the environment. A distributed algorithm based on expected idleness is proposed, aiming to promote the efficiency of cooperation, which remains to be fault tolerant and scalable. The expected idleness is estimated with information shared between robots and utilized to avoid conflicts in the decision process. Comparisons with state-of-the-art algorithms have been conducted in a realistic simulator, Stage; moreover, the fault tolerance and scalability have also been tested. Experiments on real robots have further verified the applicability of the proposed algorithm.
Summary
Polyploid plants typically display advantages on some agronomically important traits over their diploid counterparts. Extensive studies have shown genetic, transcriptomic and epigenetic dynamics upon polyploidization in multiple plant species. However, few studies are dedicated to unveil those alternations imposed only by ploidy level, without any interference from heterozygosity.
Cultivated potato is highly heterozygous. Thus, in this study, we developed two homozygous autotetraploid lines and one homozygous diploid line in parallel from a homozygous diploid potato. We confirmed their ploidy levels using chloroplast counting and karyotyping. Oligo-FISH and genome re-sequencing validated that these potato lines are nearly homozygous. We investigated variations in phenotypes, transcription and histone modifications between two ploidies. Both autotetraploid lines produced larger but fewer tubers than the diploid line. Interestingly, each autotetraploid line displayed ploidy-related differential expression for various genes. We also discovered a genome-wide enrichment of H3K27ac in genic regions upon whole-genome doubling (WGD). However, such enrichment was not associated with the differential gene expression between two ploidies. The tetraploid lines may exhibit better resistance to cold-induced sweetening (CIS) than the diploid line in tubers, potentially regulated through the expression of CIS-related key genes, which seems to be associated with the levels of H3K4me3 in cold-stored tubers.
These findings will help to understand the impacts of autotetraploidization on dynamics of phenotypes, transcription and histone modifications, as well as on CIS-related genes in response to cold storage.
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<p>Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, which will cause the global model to be manipulated by the attacker or fail to converge. On non-iid data, the current methods are not effective in defensing against Byzantine attacks. In this paper, we propose a Byzantine-robust framework for federated learning via credibility assessment on non-iid data (BRCA). Credibility assessment is designed to detect Byzantine attacks by combing adaptive anomaly detection model and data verification. Specially, an adaptive mechanism is incorporated into the anomaly detection model for the training and prediction of the model. Simultaneously, a unified update algorithm is given to guarantee that the global model has a consistent direction. On non-iid data, our experiments demonstrate that the BRCA is more robust to Byzantine attacks compared with conventional methods.</p>
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