Support vector machine optimization via an improved elephant herding algorithm for motor energy efficiency rating.

Accurate evaluation of motor energy efficiency under off-condition operation can provide an important basis for an energy-saving upgrade of the motor and the elimination of backward motors. By considering the power quality, motor characteristics and load characteristics, a motor energy efficiency evaluation system with seven indexes and 10 grades was constructed. An improved elephant herding optimization method combined with a support vector machine rating model is proposed, it achieved an accuracy higher than 98%. Considering the slow convergence speed and low convergence precision of the standard elephant herding optimization (EHO) method, it is easy to fall into the local optimum problem. To improve population initialization, chaotic mapping and adversarial learning were used to achieve EHO with population diversity and global search capability. Group learning and elite retention have been added to improve the local development ability of the algorithm. The improved EHO has been compared with other intelligent optimization algorithms by using 12 benchmark functions, and the results show that the improved algorithm has better optimization performance.

Research on track fastener positioning method based on local unidirectional template matching.

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

AFI-Net: Attention-Guided Feature Integration Network for RGBD Saliency Detection.

This article proposes an innovative RGBD saliency model, that is, attention-guided feature integration network, which can extract and fuse features and perform saliency inference. Specifically, the model first extracts multimodal and level deep features. Then, a series of attention modules are deployed to the multilevel RGB and depth features, yielding enhanced deep features. Next, the enhanced multimodal deep features are hierarchically fused. Lastly, the RGB and depth boundary features, that is, low-level spatial details, are added to the integrated feature to perform saliency inference. The key points of the AFI-Net are the attention-guided feature enhancement and the boundary-aware saliency inference, where the attention module indicates salient objects coarsely, and the boundary information is used to equip the deep feature with more spatial details. Therefore, salient objects are well characterized, that is, well highlighted. The comprehensive experiments on five challenging public RGBD datasets clearly exhibit the superiority and effectiveness of the proposed AFI-Net.

Effect of thermal expansion additives on alleviating the ash deposition of high-sodium coal.

The high content of sodium in coal ash can induce severe ash deposit problems on heated surface. Vermiculite has been investigated to solve this problem in drop-tube furnace recently. In this work, the effects of vermiculite and perlite on appearances, inorganic mineral transformation, elemental composition change and Na capture efficiency of ash deposit were investigated. The results show that the molten deposit obtained by drop-tube furnace at 1373 K was transformed into weakly-condensed deposit and strongly-sticky deposit respectively when vermiculite and perlite were added separately. Vermiculite has a better effect on improving the ash deposition than perlite. The mechanism of alleviating the ash deposition by vermiculite and perlite is proposed as follows: (1) The interaction between ash particles is inhibited due to the combination reactions of thermal expansion additive particles with coal ash particles. (2) The coal ash particles attach to the surface and the gap of thermal expansion additive particles, forming a porous structure. (3) With vermiculite added, Mg2SiO4 (forsterite) increases the fusion point of ash deposit. NaCa2Mg4Al(Si6Al2)O22(OH)2 (pargasite) and Mg1.8Fe0.2SiO4 (forsterite ferroan) result in the weak viscosity of ash deposit. (4) With perlite added, silicate and sodium aluminosilicate in perlite react with coal ash to produce a large amount of amorphous substance, which can flow downwards to make the bottom deposit molten and lead to the strong viscosity of total deposit. (5) Vermiculite has a strong capacity for Na capture at 1023 K, and perlite has a strong capacity for Na capture at 1373 K.

Batch process monitoring based on multiple-phase online sorting principal component analysis.

Existing phase-based batch or fed-batch process monitoring strategies generally have two problems: (1) phase number, which is difficult to determine, and (2) uneven length feature of data. In this study, a multiple-phase online sorting principal component analysis modeling strategy (MPOSPCA) is proposed to monitor multiple-phase batch processes online. Based on all batches of off-line normal data, a new multiple-phase partition algorithm is proposed, where k-means and a defined average Euclidean radius are employed to determine the multiple-phase data set and phase number. Principal component analysis is then applied to build the model in each phase, and all the components are retained. In online monitoring, the Euclidean distance is used to select the monitoring model. All the components undergo online sorting through a parameter defined by Bayesian inference (BI). The first several components are retained to calculate the T(2) statistics. Finally, the respective probability indices of [Formula: see text] is obtained using BI as the moving average strategy. The feasibility and effectiveness of MPOSPCA are demonstrated through a simple numerical example and the fed-batch penicillin fermentation process.