The registration based compensation (RBC) method is an effective method to compensate the range-dependence of the main-lobe clutter and side-lobe clutter in the same time. However, the compensation performance of the RBC degrades because of the mismatch of prior information and the loss of degree of system freedom. Moreover, the RBC is not very suited for real-time implementation because of the enormous computational complexity and memory usage of eigenvalue decomposition. In this paper, a novel clutter range-dependence compensation method using the modified maximum likelihood adaptive subspace estimation algorithm, which is named the MRBC method for short, is proposed. The eigenvectors matrix and eigenvalues matrix of the clutter covariance matrix are estimated by iterative tracking instead of temporal and spatial smoothing, spectrum calculation and eigenvalue decomposition. Compared with the traditional RBC method, the proposed method can reduce the computational complexity significantly and maintain the performance of clutter range-dependence compensation. In addition, the proposed method can also achieve good performance when the system error exists because of no use of prior information. Experimental simulations demonstrate the validity of this method. 相似文献
The oxygen reduction reaction (ORR) is essential in research pertaining to life science and energy. In applications, platinum-based catalysts give ideal reactivity, but, in practice, are often subject to high costs and poor stability. Some cost-efficient transition metal oxides have exhibited excellent ORR reactivity, but the stability and durability of such alternative catalyst materials pose serious challenges. Here, we present a facile method to fabricate uniform CoxOy nanoparticles and embed them into N-doped carbon, which results in a composite of extraordinary stability and durability, while maintaining its high reactivity. The half-wave potential shows a negative shift of only 21 mV after 10,000 cycles, only one third of that observed for Pt/C (63 mV). Furthermore, after 100,000 s testing at a constant potential, the current decreases by only 17%, significantly less than for Pt/C (35%). The exceptional stability and durability results from the system architecture, which comprises a thin carbon shell that prevents agglomeration of the CoxOy nanoparticles and their detaching from the substrate.
This paper presents a multi-robot open architecture of an intelligent computer numerical control (CNC) system based on parameter-driven technology that has been developed for flexible and high-efficiency manipulation. An open architecture control system capable of distributed processing of decision-making and extraction of task information provides a premise for intelligent control and flexible operation. Intelligent detection with database feedback based on real-time assignment of tasks is proposed to achieve dynamic modification of the processing trajectory. In the context of flexible task control, a multi-robot architecture with collision-free path planning and a novel programming approach based on parameter-driven technology are developed. The proposed CNC system has been successfully implemented and demonstrated on an H-beam steel-cutting task that requires flexible and accurate machining. 相似文献
Session-based recommendation (SBR) and multi-behavior recommendation (MBR) are both important problems and have attracted the attention of many researchers and practitioners. Different from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics, heterogeneous SBR (HSBR) that exploits different types of behavioral information (e.g., examinations like clicks or browses, purchases, adds-to-carts and adds-to-favorites) in sequences is more consistent with real-world recommendation scenarios, but it is rarely studied. Early efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of behaviors. However, all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of behaviors. However, all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of behaviors. The limitation hinders the development of HSBR and results in unsatisfactory performance. As a response, we propose a novel behavior-aware graph neural network (BGNN) for HSBR. Our BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a session. Moreover, our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified way. We then conduct extensive empirical studies on three real-world datasets, and find that our BGNN outperforms the best baseline by 21.87%, 18.49%, and 37.16% on average correspondingly. A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our BGNN. An exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can easily and effectively be extended to multi-behavior scenarios. 相似文献
