Power-Line Extraction Method for UAV Point Cloud Based on Region Growing Algorithm

[Introduction] Since the power line has the characteristics of long transmission distance and a complex spatial environment, the UAV LiDAR point cloud technology can completely and efficiently obtain the geometric information of the power line and its surrounding spatial objects, and the existing supervised extraction and unsupervised extraction methods are deficient in point cloud data extraction in a large range of complex environments, according to the spatial environment characteristics of the main network and distribution network line point cloud data, a rapid extraction method of point cloud power line is proposed based on projection line characteristics and region growing algorithm. [Method] Firstly, in view of the characteristics that the overhead lines of the main network were usually higher than the surrounding spatial objects, the power lines were roughly extracted by the elevation histogram threshold method. Then, considering the characteristics that the vegetation canopy was higher than the distribution network line in the distribution network area, the KNN data points of the roughly extracted power line point cloud were obtained, and the point cloud was projected on the horizontal plane, and whether the point cloud was a power line point cloud was judged by the linear measurement of the point cloud. [Result] According to the existence of missing power line point clouds, all the power line point cloud clusters are obtained through a region growing mode, and on this basis, the catenary formula of each power line point cloud cluster is calculated through the catenary formula, and the point cloud with a fitting distance less than the threshold is merged as the same power line point cloud. [Conclusion] The proposed method aims at the problem of rapid power line extraction in inspection applications and overcomes the problem of power line point cloud missing and vegetation impact in the process of power line extraction, so this method can achieve power line point cloud extraction with high efficiency and accuracy

xFraud: Explainable Fraud Transaction Detection

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.Comment: This is the extended version of a full paper to appear in PVLDB 15 (3) (VLDB 2022

Effects of Chinese Medicine Tong xinluo on Diabetic Nephropathy via Inhibiting TGF- β

Diabetic nephropathy (DN) is a major cause of chronic kidney failure and characterized by interstitial and glomeruli fibrosis. Epithelial-to-mesenchymal transition (EMT) plays an important role in the pathogenesis of DN. Tong xinluo (TXL), a Chinese herbal compound, has been used in China with established therapeutic efficacy in patients with DN. To investigate the molecular mechanism of TXL improving DN, KK-Ay mice were selected as models for the evaluation of pathogenesis and treatment in DN. In vitro, TGF-β1 was used to induce EMT. Western blot (WB), immunofluorescence staining, and real-time polymerase chain reaction (RT-PCR) were applied to detect the changes of EMT markers in vivo and in vitro, respectively. Results showed the expressions of TGF-β1 and its downstream proteins smad3/p-smad3 were greatly reduced in TXL group; meantime, TXL restored the expression of smad7. As a result, the expressions of collagen IV (Col IV) and fibronectin (FN) were significantly decreased in TXL group. In vivo, 24 h-UAER (24-hour urine albumin excretion ratio) and BUN (blood urea nitrogen) were decreased and Ccr (creatinine clearance ratio) was increased in TXL group compared with DN group. In summary, the present study demonstrates that TXL successfully inhibits TGF-β1-induced epithelial-to-mesenchymal transition in DN, which may account for the therapeutic efficacy in TXL-mediated renoprotection

The Changes in China's Forests: An Analysis Using the Forest Identity

Changes in forest carbon stocks are a determinant of the regional carbon budget. In the past several decades, China has experienced a pronounced increase in forest area and density. However, few comprehensive analyses have been conducted. In this study, we employed the Forest Identity concept to evaluate the changing status of China's forests over the past three decades, using national forest inventory data of five periods (1977–1981, 1984–1988, 1989–1993, 1994–1998, and 1999–2003). The results showed that forest area and growing stock density increased by 0.51% and 0.44% annually over the past three decades, while the conversion ratio of forest biomass to growing stock declined by 0.10% annually. These developments resulted in a net annual increase of 0.85% in forest carbon sequestration, which is equivalent to a net biomass carbon uptake of 43.8 Tg per year (1 Tg = 1012 g). This increase can be attributed to the national reforestation/afforestation programs, environmentally enhanced forest growth and economic development as indicated by the average gross domestic product

Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model

Multirotors play an important role in electric power inspection, border control, modern agriculture, forest fire fighting, flood control, disaster prevention, etc. Multirotor failures, such as a communication fault, a sensor failure, or a power system anomaly, may well lead to mission interruption, multirotor crashes, and even casualties. To ensure flight safety, a multirotor decision module should be established to prevent or reduce the adverse effects caused by failure. Therefore, this paper proposes a real-time safety decision-making method for multirotor flight strategies based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, the flight of the multirotor was simulated based on the Rflysim UAV flight simulation platform, and a fault-injection module was constructed to simulate different types of faults, so as to realize real-time monitoring of the flight status of the multirotor, and to collect flight data under various faults to establish condition assessment information sources. Then, based on the random forest algorithm, a failure level classification model of the multirotor was constructed, the model was trained and verified by inputting flight data of three types of safety level failures, and the model effectively classified the failure levels of the multirotor. Under this framework, a real-time safety decision-making model for the multirotor based on the TOPSIS model was constructed to realize the flight safety decision-making of the multirotor under different faults. This method can effectively realize the real-time decision-making for the flight strategy of a multirotor. By comparison with other models, the classification accuracy of the failure level classification model is higher, and the consideration of flight decision-making is more comprehensive and accurate, thus effectively ensuring the flight safety of the multirotor

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Abstract Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively

Why Are Bidder Termination Provisions Included In Takeovers?

We present a rationale for bidder termination provisions that considers their effect on bidders' and targets' joint takeover gains. The provision's inclusion can create value by enabling termination when the target becomes less valuable to the bidder than on its own, but creates a trade-off because termination may also occur when the target is more valuable to the bidder than on its own. This trade-off explains why the provision is included in only some deals, and explains variation in termination fees. Inclusion of the provision is associated with larger combined announcement returns, provided that the termination fee is priced appropriately.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]