MBNN: A Multi-Branch Neural Network Capable of Utilizing Industrial Sample Unbalance for Fast Inference

Deep neural networks has been widely used in industrial equipment fault diagnosis. The accuracy of deep neural network is usually proportional to the complexity, but the high inference delay and energy consumption caused by the complex model make it difficult to be applied in the industrial environment of real-time demand. At the same time, in the diagnosis of industrial equipment, different categories of samples have unbalanced characteristics in terms of number, difficulty of identification, and demand of identification. In order to solve this problem, this paper designs Multi-Branch Neural Network (MBNN), which is a new type neural network architecture that can use the unbalance of sample categories in industrial equipment fault diagnosis for fast inference. MBNN has multiple sub-networks with different complexity, and each branch is responsible for processing different categories of samples. Categories with large numbers, easy to process, and high demand of identification are processed through simple branches, such as normal samples. Categories with small numbers, difficult to identification, and low demand of identification are processed through complex branches, such as potential failure samples. The feasibility of MBNN has been verified on motor bearing fault diagnosis and gearbox fault diagnosis, and its performance has been evaluated on multiple computing platforms. The results show that MBNN can greatly improve the inference speed while ensuring the recognition accuracy, especially on resource-constrained platforms

3D Quantitative Metallogenic Prediction of Indium-Rich Ore Bodies in the Dulong Sn-Zn Polymetallic Deposit, Yunnan Province, SW China

The southwestern South China Block is one of the most important Sn polymetallic ore districts in the world, of which the Dulong Sn-Zn polymetallic deposit, closely related to Late Cretaceous granitic magmatism, contains 0.4 Mt Sn, 5.0 Mt Zn, 0.2 Mt Pb, and 7 Kt In, and is one of the largest Sn-Zn polymetallic deposits in this region. In this paper, on the basis of a 3D model of ore bodies established by the cut-off grade of the main ore-forming elements, the In grades were estimated by the ordinary Kriging method and the In-rich cells were extracted. The 3D models of strata, faults, granites, and granite porphyries in the mining area were established and assigned the attributes to the cells, which built buffer zones representing the influence space of the geological factors. The weight of evidence and artificial neural network methods were used to quantitatively evaluate the contribution of each geological factor to mineralization. The results show that the Neoproterozoic Xinzhai Formation (Pt3x), fault (F1), and Silurian granites (S3L) have considerable control effects on the occurrence of In-rich ore bodies. The metallogenic predictions according to the spatial coupling relationship of each geological factor in 3D space were carried out, and then the 3D-space-prospecting target areas of In-rich ore bodies were delineated. In addition, the early geological maps and data information of the mining area were comprehensively integrated in 3D space. The feasibility of 3D quantitative metallogenic prediction based on the deposit model was explored by comparing the two methods, and then, the 3D-space prospecting target area was delineated. The ROC curve evaluation shows that the results of two methods have indicative value for prospecting. The modeling results may support its use for future deep prospecting and exploitation of the Dulong and other similar deposits

3D Quantitative Metallogenic Prediction of Indium-Rich Ore Bodies in the Dulong Sn-Zn Polymetallic Deposit, Yunnan Province, SW China

The southwestern South China Block is one of the most important Sn polymetallic ore districts in the world, of which the Dulong Sn-Zn polymetallic deposit, closely related to Late Cretaceous granitic magmatism, contains 0.4 Mt Sn, 5.0 Mt Zn, 0.2 Mt Pb, and 7 Kt In, and is one of the largest Sn-Zn polymetallic deposits in this region. In this paper, on the basis of a 3D model of ore bodies established by the cut-off grade of the main ore-forming elements, the In grades were estimated by the ordinary Kriging method and the In-rich cells were extracted. The 3D models of strata, faults, granites, and granite porphyries in the mining area were established and assigned the attributes to the cells, which built buffer zones representing the influence space of the geological factors. The weight of evidence and artificial neural network methods were used to quantitatively evaluate the contribution of each geological factor to mineralization. The results show that the Neoproterozoic Xinzhai Formation (Pt3x), fault (F1), and Silurian granites (S3L) have considerable control effects on the occurrence of In-rich ore bodies. The metallogenic predictions according to the spatial coupling relationship of each geological factor in 3D space were carried out, and then the 3D-space-prospecting target areas of In-rich ore bodies were delineated. In addition, the early geological maps and data information of the mining area were comprehensively integrated in 3D space. The feasibility of 3D quantitative metallogenic prediction based on the deposit model was explored by comparing the two methods, and then, the 3D-space prospecting target area was delineated. The ROC curve evaluation shows that the results of two methods have indicative value for prospecting. The modeling results may support its use for future deep prospecting and exploitation of the Dulong and other similar deposits

An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm

In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount

Maximizing the scattering of multiwavelength phonons in novel biphasic high-entropy ZrCoSb-based half-Heusler alloys

The thermoelectric (TE) performance of p-type ZrCoSb-based half-Heusler (HH) alloys has been improved tremendously in recent years; however, it remains challenging to find suitable n-type ZrCoSb-based HH alloys due to their high lattice thermal conductivity (κL). In this work, n-type Zr1−xTaxCo1−xNixSb HH alloys were firstly designed by multisite alloying. The evolution of the Raman peak proved that alloy scattering, phonon softening, anharmonicity, entropy-driven disorder, and precipitates had a combined effect on decreasing κL by 46.7% compared to that of pristine ZrCoSb. Subsequently, Hf0.75Zr0.25NiSn0.99Sb0.01 was introduced into Zr0.88Ta0.12Co0.88Ni0.12Sb to further suppress κL. Remarkably, the grain size of the biphasic HH alloys was refined by at least one order of magnitude. A biphasic high-entropy HH alloy with y = 0.2 exhibited the minimum κL of ∼2.44 W/(m·K) at 923 K, reducing by 67.7% compared to that of ZrCoSb. Consequently, (Zr0.88Ta0.12Co0.88Ni0.12Sb)0.9(Hf0.75Zr0.25NiSn0.99Sb0.01)0.1 exhibited the highest TE figure of merit (∼0.38) at 923 K. The cooperation between the entropy and biphasic microstructure resulted in multiscale defects, refined grains, and biphasic interfaces, which maximized the scattering of the multiwavelength phonons in HH alloys. This work provides a new strategy for further reducing the grain size and κL of medium- and high-entropy HH alloys

Therapeutic Effect and Location of GFP-Labeled Placental Mesenchymal Stem Cells on Hepatic Fibrosis in Rats

Background. Liver fibrosis is a chronic progressive liver disease, but no established effective treatment exists except for liver transplantation. The present study was designed to investigate the effect of human placenta mesenchymal stem cells (hPMSCs) expressing green fluorescent protein (GFP) on carbon tetrachloride- (CCl4-) induced liver fibrosis in rats. Methods. Liver fibrosis was induced by subcutaneous injection with CCl4; hPMSCs were directly transplanted into rats through the caudal vein. The therapeutic efficacy of hPMSCs on liver fibrosis was measured by liver function tests, liver elastography, histopathology, Masson’s trichrome and Sirius red staining, and immunohistochemical studies. The expression levels of fibrotic markers, transforming growth factor β1 (TGF-β1) and α-smooth muscle actin (α-SMA), were assessed using real-time polymerase chain reaction. Results. We demonstrated that liver fibrosis was significantly dampened in the hPMSC transplantation group according to the Laennec fibrosis scoring system and histological data. The Sirius red-stained collagen area and the elastography score were significantly reduced in the hPMSC-treated group. Meanwhile, hPMSC administration significantly decreased TGF-β1 and α-SMA expression and enhanced liver functions in CCl4-induced fibrotic rats. Conclusion. This study indicates that transplantation of hPMSCs could repair liver fibrosis induced by CCl4 in rats, which may serve as a valuable therapeutic approach to treat liver diseases