A novel traveling-wave-based method improved by unsupervised learning for fault location of power cables via sheath current monitoring

In order to improve the practice in maintenance of power cables, this paper proposes a novel traveling-wave-based fault location method improved by unsupervised learning. The improvement mainly lies in the identification of the arrival time of the traveling wave. The proposed approach consists of four steps: (1) The traveling wave associated with the sheath currents of the cables are grouped in a matrix; (2) the use of dimensionality reduction by t-SNE (t-distributed Stochastic Neighbor Embedding) to reconstruct the matrix features in a low dimension; (3) application of the DBSCAN (density-based spatial clustering of applications with noise) clustering to cluster the sample points by the closeness of the sample distribution; (4) the arrival time of the traveling wave can be identified by searching for the maximum slope point of the non-noise cluster with the fewest samples. Simulations and calculations have been carried out for both HV (high voltage) and MV (medium voltage) cables. Results indicate that the arrival time of the traveling wave can be identified for both HV cables and MV cables with/without noise, and the method is suitable with few random time errors of the recorded data. A lab-based experiment was carried out to validate the proposed method and helped to prove the effectiveness of the clustering and the fault location

Grid investment capability prediction based on path analysis and BP neural network

With the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capacity of the power grid from the internal and external environment, and establishes the index system of the factors affecting the investment capacity. Secondly, the path analysis is used to deeply explore the interaction relationship and influence degree of each index and investment capacity. Finally, the maximum investment capacity of the power network can be predicted based on the BP neural network prediction model. The results show that the BP neural network prediction model can achieve higher prediction accuracy when predicting the power grid investment capability

A Modular Method for GPR Hyperbolic Feature Detection and Quantitative Parameter Inversion of Underground Pipelines

Ground penetrating radar (GPR) is widely used to inspect underground pipelines because it is non-destructive. When the scan line of GPR is perpendicular to the pipe, it will exhibit hyperbolic features in GPR B-scan images, which have no intuitive relationship with the geometric and physical parameters of the pipeline, making the interpretation of GPR images difficult. This paper proposes a modular detection and quantitative inversion method for the hyperbolic features in GPR B-scan images, which is divided into two steps. In the first step, the YOLOv7 object detection network is used to automatically detect the hyperbolic features in GPR images. In the second step, a two-stage curve fitting method is proposed based on the characteristics of the detection model. It uses a few key point annotations of the hyperbolic pattern and some parameters of the GPR system to quantitatively invert the depth and radius of pipes. Using the same hardware and data set, YOLOv7 achieves an 11.1% improvement in detection accuracy and an 18.2% improvement in speed compared to YOLOv5. The relative errors of the proposed method for the depth and radius of the synthetic data in homogeneous media are 0.6% and 4.4%, respectively, and 4.8% and 15% in non-homogeneous media. The relative error of the depth inversion of the measured data TU1208 is less than 10%. The results show that the method can effectively invert the depth and radius of underground pipelines and reduce the difficulty of GPR data interpretation

Motivation Analysis of Market and Institution on Corporate Leasing Financialization from the Perspective of Regulatory Arbitrage: Evidence from Chinese Listed Companies

In recent years, more and more real enterprises speculate and arbitrage in the financial market by participating in financial institutions, and the financialization of micro enterprises has become a general trend. However, the empirical conclusions of existing literature from different dimensions of enterprise development are not consistent. This paper uses the data from Shanghai and Shenzhen A-share companies from 2007 to 2019 to perform an empirical analysis on the market and institutional motivations of the entity enterprises’ sharing and holding financial leasing companies (SHFL). It is found that the fundamental reason for enterprises to SHFL is the profit gap between the financial industry and the real industry. The more intense the industry competition, the lower the profit rate, the larger the spread, and the stronger the incentive to SHFL. In addition, the continuous improvement of the national system construction in the financial leasing industry has played an essential role in promoting it. In the heterogeneity analysis, it is found that private enterprises are also motivated to ease financing constraints except interest rate spread. On the contrary, they are not significant in the sample of state-owned enterprises. Equipment manufacturing industries have both narrowing interest rates and equipment promotion motivation, while the non-equipment manufacturing industry has no such characteristics. Finally, the limitations and future research directions of this paper are discussed

Game Approach for H∞ Robust Control Strategy to Follow the Production in the Singularly Perturbed Bilinear Dynamic Input-Output Systems

For simulating and analyzing the input and output problem of national economy more accurately, this paper considers the fast and slow production processes during the course of social production development, takes stochastic economic risks into consideration, and constructs a H∞ robust control model to follow the production in the singularly perturbed dynamic input-output systems. Further introducing ideas of noncooperative differential game theory, the H∞ robust control model is transformed into a saddle-point equilibrium game model, and a new method for solving dynamic input-output problem by using saddle-point equilibrium strategies is obtained. A numerical result is presented in the end to illustrate the effectiveness of the method

Application of Ground-Penetrating Radar Broadband Antenna in Underground Detection

When GPR is detecting unknown objects underground, different antenna working frequency, different antenna size and different antenna internal structure will affect the final data quality and ultimately affect the detection accuracy of GPR. Therefore, when actualizing and evaluating the uwb signal of GPR electromagnetic wave, the electromagnetic properties of underground medium should be fully considered, and the influence of relevant parameters of GPR antenna on the transmitted and received electromagnetic signals should be analyzed by using numerical analysis method. This paper mainly describes the design characteristics of GPR antenna and antenna array, as well as the types, characteristics and application convenience of antenna array under different positioning purposes of GPR

Research on Influence of Road Tunnels with Different Lanes on Surrounding Rock Characteristic Curve

Convergence confinement method is an important guidance method for tunnel construction and support design. Numerical simulation method was used to comparatively analyze the ground reaction curve and the plastic zone under different rock grade and roadway tunnel size. The results show that the change of tunnel size has different effects on the maximum deformation of the tunnel arch crown, the ground reaction curve and the plastic zone range. Finally, some suggestions were put forward for the construction and optimization of the large span arch tunnel support structure. The research results may provide some guidance for related engineerin

Grid investment capability prediction based on path analysis and BP neural network

With the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capacity of the power grid from the internal and external environment, and establishes the index system of the factors affecting the investment capacity. Secondly, the path analysis is used to deeply explore the interaction relationship and influence degree of each index and investment capacity. Finally, the maximum investment capacity of the power network can be predicted based on the BP neural network prediction model. The results show that the BP neural network prediction model can achieve higher prediction accuracy when predicting the power grid investment capability

Dose- and Rate-Dependent Effects of Cocaine on Striatal Firing Related to Licking

To examine the role of striatal mechanisms in cocaine-induced stereotyped licking, we investigated the acute effects of cocaine on striatal neurons in awake, freely moving rats before and after cocaine administration (0, 5, 10, or 20 mg/kg). Stereotyped licking was induced only by the high dose. Relative to control (saline), cocaine reduced lick duration and concurrently increased interlick interval, particularly at the high dose, but it did not affect licking rhythm. Firing rates of striatal neurons phasically related to licking movements were compared between matched licks before and after injection, minimizing any influence of sensorimotor variables on changes in firing. Both increases and decreases in average firing rate of striatal neurons were observed after cocaine injection,and these changes exhibited a dose-dependent pattern that strongly depended on predrug firing rate. At the middle and high doses relative to the saline group, the average firing rates of slow firing neurons were increased by cocaine, resulting from a general elevation of movement-related firing rates. In contrast, fast firing neurons showed decreased average firing rates only in the high-dose group, with reduced firing rates across the entire range for these neurons. Our findings suggest that at the high dose, increased phasic activity of slow firing striatal neurons and simultaneously reduced phasic activity of fast firing striatal neurons may contribute, respectively, to the continual initiation of stereotypic movements and the absence of longer movements

Chemokine signaling links cell-cycle progression and cilia formation for left-right symmetry breaking.

Zebrafish dorsal forerunner cells (DFCs) undergo vigorous proliferation during epiboly and then exit the cell cycle to generate Kupffer's vesicle (KV), a ciliated organ necessary for establishing left-right (L-R) asymmetry. DFC proliferation defects are often accompanied by impaired cilia elongation in KV, but the functional and molecular interaction between cell-cycle progression and cilia formation remains unknown. Here, we show that chemokine receptor Cxcr4a is required for L-R laterality by controlling DFC proliferation and KV ciliogenesis. Functional analysis revealed that Cxcr4a accelerates G1/S transition in DFCs and stabilizes forkhead box j1a (Foxj1a), a master regulator of motile cilia, by stimulating Cyclin D1 expression through extracellular regulated MAP kinase (ERK) 1/2 signaling. Mechanistically, Cyclin D1-cyclin-dependent kinase (CDK) 4/6 drives G1/S transition during DFC proliferation and phosphorylates Foxj1a, thereby disrupting its association with proteasome 26S subunit, non-ATPase 4b (Psmd4b), a 19S regulatory subunit. This prevents the ubiquitin (Ub)-independent proteasomal degradation of Foxj1a. Our study uncovers a role for Cxcr4 signaling in L-R patterning and provides fundamental insights into the molecular linkage between cell-cycle progression and ciliogenesis