A Novel Spark-based Attribute Reduction and Neighborhood Classification for Rough Evidence

Neighborhood classification (NEC) algorithms have been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the entire data into memory at once, which is computationally inefficient when the size of the dataset is large. To address these problems, we propose a novel Spark-based attribute reduction and NEC for rough evidence in this article. Specifically, we first construct a multigranular sample space using the parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale datasets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency

Operation optimization considering multiple uncertainties for the multi-energy system of data center parks based on information gap decision theory

With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods

Optical Spectroscopic Observations of CI Camelopardalis

We present the results of optical spectroscopic observations of CI Cam. Double-peaked profiles were simultaneously observed for the first time in the hydrogen Balmer, He {\small I} $\lambda$6678 and Fe {\small II} lines during an observational run in 2001 September. An intermediate viewing angle of the circumstellar disk around the B[e] star is consistent with our data. A significant decrease in the intensity of the H$\alpha$ and He {\small I} lines in our 2004 September observations might have been the precursor of a line outburst at the end of 2004. The remarkable increase in the intensity of all lines and the decrease in visual brightness in 2005 might be due to the environment filling with new material ejected during the outburst. The environment of CI Cam is influenced by mass loss from the B[e] star and the outburst of its compact companion.Comment: 18 pages, 4 figures, 1 tabl

Design of remote overload monitoring and diagnosis system for electric spindle based on three-axis MEMS device

Abstract The MEMS sensor is used to measure the overload signal of the electric spindle, so as to get response of overload status and send alarm signal to PLC timely, which is a kind of machine controlling tool. By this system, the collision of the electric axes can be avoided effectively. The vibration data of spindle is also can be transmitted real-timely via internet, which is supposed to be combined with data mining and machine learning for daily monitor of spindles and intelligent faults diagnose.</jats:p