Complete Convergence for Moving Average Process of Martingale Differences

Under some simple conditions, by using some techniques such as truncated method for random variables (see e.g., Gut (2005)) and properties of martingale differences, we studied the moving process based on martingale differences and obtained complete convergence and complete moment convergence for this moving process. Our results extend some related ones

Convergence Properties for Asymptotically almost Negatively Associated Sequence

We get the strong law of large numbers, strong growth rate, and the integrability of supremum for the partial sums of asymptotically almost negatively associated sequence. In addition, the complete convergence for weighted sums of asymptotically almost negatively associated sequences is also studied

Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems

Linear Network Coding Based Fast Data Synchronization for Wireless Ad Hoc Networks with Controlled Topology

Fast data synchronization in wireless ad hoc networks is a challenging and critical problem. It is fundamental for efficient information fusion, control and decision in distributed systems. Previously, distributed data synchronization was mainly studied in the latency-tolerant distributed databases, or assuming the general model of wireless ad hoc networks. In this paper, we propose a pair of linear network coding (NC) and all-to-all broadcast based fast data synchronization algorithms for wireless ad hoc networks whose topology is under operator's control. We consider both data block selection and transmitting node selection for exploiting the benefits of NC. Instead of using the store-and-forward protocol as in the conventional uncoded approach, a compute-and-forward protocol is used in our scheme, which improves the transmission efficiency. The performance of the proposed algorithms is studied under different values of network size, network connection degree, and per-hop packet error rate. Simulation results demonstrate that our algorithms significantly reduce the times slots used for data synchronization compared with the baseline that does not use NC.Comment: 9 pages, 9 figures, published on China Communications, vol. 19, no. 5, May 202

Hydraulic supports for polishing TMT M3MP

For polishing the ultra-thin TMT M3MP, a polishing support system with 18 hydraulic supports (HS) is introduced. This work focuses on the designing and testing of these HSs. Firstly the design concept of HS system is discussed; then mechanical implementation of the HS structure is carried out, with special consideration of fluid cycling, work pressurization and the weight component. Afterward the piping installation and the de-gas process for the working fluid are implemented. Pressurization and stiffness are well checked before system integration for the single HS unit. Finally the support system is integrated for the polishing process

The Application of Pentaprism Scanning Technology on the Manufacturing of M3MP

The PSS (pentaprism scanning system) has advantages of simple structure, needless of reference flat, be able of on-site testing, etc, it plays an important role in large flat reflective mirror’s manufacturing, especially the high accuracy testing of low order aberrations. The PSS system measures directly the slope information of the tested flat surface. Aimed at the unique requirement of M3MP, which is the prototype mirror of the tertiary mirror in TMT (Thirty Meter Telescope) project, this paper analyzed the slope distribution of low order aberrations, power and astigmatism, which is very important in the manufacturing process of M3MP. Then the sample route lines of PSS are reorganized and new data process algorism is implemented. All this work is done to improve PSS’s measure sensitivity of power and astigmatism, for guiding the manufacturing process of M3MP

MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection

Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm to satisfy the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection, that consists of the following three components: an under-sampling module targeting to construct a novel training set that accommodates the true few-shot setting, a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the PLMs sufficiently for tackling the context-bypassing problem, and a prototypical module compensating for the weakness of classifying events with sparse data and boost the generalization performance. Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best-performing baseline and achieving an outstanding debiasing performance