The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher.
Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.
While wireless sensor networks are proving to be a versatile tool, many of the applications in which they are implemented have sensitive data. In other words, security is crucial in many of these applications. Once a sensor node has been compromised, the security of the network degrades quickly if there are not measures taken to deal with this event. There have been many approaches researched to tackle the issue. In this paper, we look into an anomaly-based intrusion detection system to detect compromised nodes in wireless sensor networks. An algorithm to detect the compromised sensor nodes has been developed. Simulations are conducted to verify the design.
Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.
The Keda Torus eXperiment (KTX) is still operated in the commissioning phase, and preparation for the operation capability of the KTX phase II upgrade is underway. The diagnostics in the KTX have been greatly developed: (1) the terahertz interferometer has been upgraded to seven chords for electron density profile inversion; (2) a Thomson scattering system with a 5 Joule laser has been installed and commissioning is in progress; (3) a 3D movable probe system has been developed for the electromagnetic turbulence measurement; (4) double-foil soft x-ray imaging diagnostics have been set up and a bench test has been completed; (5) an edge capacitive probe has been installed for the radial electrical field measurement; (6) a multi-channel spectrograph system has been built for detecting impurities of carbon and oxygen. In addition, the design of a new compact torus injection system has been completed for feeding and momentum driving. Pilot research, such as the 3D reversed field pinch physics and electromagnetic turbulence, etc, have been conducted in the discharge status of the KTX. The 3D spectra characters of electromagnetic turbulence are firstly measured using a classical two-point technique by Langmuir probe arrays set on the 3D movable probe system and edge magnetic sensors. The forward scattering is collected by the interferometer system, which shows the potential for turbulence research. The electromagnetic turbulence is tentatively investigated in the KTX. The formation of a quasi-single-helicity state in the KTX regime is also preliminarily explored in simulation.
The Saima deposit is a newly discovered niobium deposit which is located in the eastern of Liaoning Province, NE China. Its mineralization age and geochemical characteristics are firstly reported in this study. The Nb orebodies are hosted by the grey-brown to grass-green aegirine nepheline syenite. Detailed petrographical studies show that the syenite consists of orthoclase (~50%), nepheline (~30%), biotite (~15%) and minor arfvedsonite (~3%) and aegirine (~2%), with weak hydrothermal alteration dominated by silicification. In situ LA-ICP-MS zircon U-Pb dating indicates that the aegirine nepheline syenite was emplaced in the Late Triassic (229.5 ± 2.2 Ma), which is spatially, temporally and genetically related to Nb mineralization. These aegirine nepheline syenites have SiO 2 contents in the range of 55.86-63.80 wt. %, low TiO 2 contents of 0.36-0.64 wt. %, P 2 O 5 contents of 0.04-0.11 wt. % and Al 2 O 3 contents of more than 15 wt. %. They are characterized by relatively high (K 2 O + Na 2 O) values of 9.72-15.51 wt. %, K 2 O/Na 2 O ratios of 2.42-3.64 wt. % and Rittmann indexes (σ = [ω(K 2 O + Na 2 O)] 2 /[ω(SiO 2 − 43)]) of 6.84-17.10, belonging to the high-K peralkaline, metaluminous type. These syenites are enriched in large ion lithophile elements (LILEs, e.g., Cs, Rb and Ba) and light rare earth elements (LREEs) and relatively depleted in high field strength elements (HFSEs, e.g., Nb, Zr and Ti) and heavy rare earth elements (HREEs), with transitional elements showing an obvious W-shaped distribution pattern. Based on these geochronological and geochemical features, we propose that the ore-forming intrusion associated with the Nb mineralization was formed under post-collision continental-rift setting, which is consistent with the tectonic regime of post-collision between the North China Craton and Paleo-Asian oceanic plate during the age in Ma for Indosinian . Intensive magmatic and metallogenic events resulted from partial melting of lithospheric mantle occurred during the post-collisional rifting, resulting in the development of large-scale Cu-Mo mineralization and rare earth deposits in the eastern part of Liaoning Province.
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