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Given a set of objects and a query q, a point p is q’s Reverse k Nearest Neighbour (RkNN) if q is one of p’s k-closest objects. RkNN queries have received significant research attention in the past few years. However, we realize that the state-of-the-art algorithm, SLICE, accesses many objects that do not contribute to its RkNN results when running...
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We present an experimental study on the edge-emitting lasers based on coupled ridges. The main idea of these structures is to ensure fundamental mode lasing in broadened multi-mode ridges by means of using a high-order mode filtering based on the resonant optical tunneling into a nearby passive stripe. For our experiments, we used a conventional In...
Citations
Recently, Masud et al. [M. A. Masud, J. Z. Huang, C. H. Wei, et al. I-nice: A new approach for identifying the number of clusters and initial cluster centres. Information Sciences 466 (2018) 129–151] proposed a parameter-free clustering algorithm, named I-nice, which can identify the number of clusters and initial cluster centres using observation points. Although the experiment presented good clustering performance of I-nice, there are two inherent limitations that can be further improved. One is that I-niceSO is sensitive to the position of the observation point, and the other is that the number of nearest neighbours affects the determination of high-density areas in I-niceMO. Inspired by density peaks clustering, we propose a density-peaks-based I-nice (I-niceDP) clustering algorithm to improve the existing I-nice clustering algorithm. In I-niceDP, we use density peaks to determine the number of clusters and cluster centres in the components of the gamma mixture model rather than the k-nearest neighbours method. The comparative results using I-niceSO and I-niceMO indicate that I-niceDP can more accurately identify the number of clusters and initial cluster centres for datasets with large cluster numbers. Furthermore, I-niceDP obtains higher normalised mutual information values in comparison with seven other clustering algorithms. The experimental results demonstrate the feasibility and effectiveness of the I-niceDP clustering algorithm.
This paper proposes a near-infrared (NIR) fault detection technology based on a process pattern via a potential function. Near-infrared spectroscopy is used to acquire process information at the molecular level. In this study, the process pattern concept is first introduced in the field of process control and a process pattern construction method based on elastic net-PCA is put forth. Next, the potential function discriminant method is applied to distinguish and classify the constructed process pattern and identify the running state of the industrial system. Finally, the proposed method is verified and analyzed using spectra data of the crude oil desalination and dehydration process. Compared with existing fault detection methods, the proposed approach offers the following advantages: (1) potential function discrimination achieves nonlinear process classification with better fault detection accuracy and good visualization performance; (2) fault detection based on NIR spectra is faster with and possesses greater accuracy because it acquires process information from a microscopic molecular perspective; and (3) the process pattern contains more effective process information and can more comprehensively characterize the essential features of processes.
