Recurrent Temporal Revision Graph Networks

Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for temporal graphs, is currently straightforwardly extended from that of static graphs. It can be computationally expensive when involving all historical neighbors during such aggregation. In practice, typically only a subset of the most recent neighbors are involved. However, such subsampling leads to incomplete and biased neighbor information. To address this limitation, we propose a novel framework for temporal neighbor aggregation that uses the recurrent neural network with node-wise hidden states to integrate information from all historical neighbors for each node to acquire the complete neighbor information. We demonstrate the superior theoretical expressiveness of the proposed framework as well as its state-of-the-art performance in real-world applications. Notably, it achieves a significant +9.6% improvement on averaged precision in a real-world Ecommerce dataset over existing methods on 2-layer models

Random Violation Risk Degree Based Service Channel Routing Mechanism in Smart Grid

Smart gird, integrated power network with communication network, has brought an innovation of traditional power for future green energy. Optical fiber technology and synchronous digital hierarchy (SDH) technology is widely used in smart grid communication transmission network. It is a challenge to reduce impact of the availability of smart grid communication services caused by random failures and random time to repair. Firstly, we create a service channel violation risk degree (SCVRD) model to precisely track the violation risk change of communication service channel. It is denoted by the probability of service channel cumulative failure duration exceeding the prescribed duration. Secondly, a service channel violation risk degree routing mechanism is proposed to improve the availability of communication service. At last, the simulation is implemented with MATLAB and network data in one province are used as data instance. The simulation results show that the average service channel failure rate of availability-aware routing based on statistics (AAR-OS) algorithm and risk-aware provisioning algorithm are reduced by 15% and 6%, respectively

Polarimetric SAR Image Affine Registration Based on Neighborhood Consensus

As the base of Synthetic Aperture Radar (SAR) image processing, the registration of polarimetric SAR images requires high accuracy and a fast speed. Most methods used to register polarimetric SAR images based on deep learning are combined with patch matching and iterative estimation, e.g. the random sample consensus algorithm. However, end-to-end deep convolutional neural networks have not been used in the non-iterative affine registration of polarimetric SAR images. This paper proposes a framework for end-to-end polarimetric SAR image registration that is based on weakly-supervised learning and uses no image patch processing or iterative parameter estimation. First, feature extraction is performed on input image pairs to obtain dense feature maps with the most relevant k matches kept for each feature point. To filter the matched feature pairs, the 4D sparse feature matching maps are then fed into a 4D sparse convolutional network based on neighborhood consensus. Lastly, the affine parameters are solved by the weighted least square method according to the degree of confidence of the matches, which enables the affine registration of the input image pair. As test image pairs, we use farmland data from Wallerfing, Germany obtained by the RADARSAT-2 satellite and Zhoushan port data from China obtained by the PAZ satellite. Comprehensive experiments were conducted on polarimetric SAR image pairs using different orbit directions, imaging modes, polarization types and resolutions. Compared with four existing methods, the proposed method was found to have high accuracy and a fast speed

Fracture behavior of highly toughened poly(lactic acid)/ethylene-co-vinyl acetate blends

\u3cp\u3ePoly(lactic acid) (PLA) is brittle which restricts the range of its applications. The toughness of PLA was effectively improved in this work by incorporation of rubber grade ethylene-co-vinyl acetate (EVM). For example, the elongation at break of PLA increased by about 50 times after the addition of the EVM (10-30 wt%), although the EVM was not miscible with the PLA matrix. Furthermore, the notched impact toughness of PLA/EVM blend (70/30 wt/wt) reached to 85 kJ/m\u3csup\u3e2\u3c/sup\u3e even at a temperature as low as -10°C. The critical temperatures of brittle-to-ductile transition (BDT) for PLA/EVM blends are observed at -20~0°C depending on the composition, while no BTD transition appeared for neat PLA. The impact fracture surface morphology of PLA and PLA/EVM blends observed by SEM indicates that the toughening modification was achieved through obvious matrix yielding. Moreover, the toughening behavior of the PLA/EVM blends was also interpreted quantitatively by using a single-edge notched three-point bending model (SEN3PB). The SEN3PB experiments reveal that the fracture energy was consumed in an outer plastic zone away from the fracture surface rather than in the inner fracture process zone, which accounts for the high toughness of the PLA/EVM blends.\u3c/p\u3

Exploration the Road to Prosperity in China’s Rural Areas Based on the Investigation of the Villages around Beijing

In order to explore the ways to get rich in villages, we had been to the villages around Beijing. On the basis of the field investigation of four villages around Beijing: Huoshaoying Village, Baojiapu Village, Xiaoyuzi Village and Niuzhan Village, the way to get rich in rural areas is discussed in depth based on their rich history, achievements and experience, after investigating four villages that develop homestay tourism, art, agricultural products and food processing industries respectively, we have explored the ways to get rich in villages more comprehensively from different angles. The result of the investigation shows that different villages have different situations, and different villages have different optimal development paths, which requires someone to explore and discover.Rural revitalization must be adapted to local conditions and cannot blindly follow the trend

An Improved Principal Component Analysis in the Fault Detection of Multi-sensor System of Mobile Robot

To cope with the fault detection in dynamic conditions of inertial components in the mobile robots, an improved principal component analysis (PCA) method was proposed. This work took a five gyroscopes redundancy allocation model to realize the measurement of the attitude. It is hard to distinguish the fault message from dynamic message in dynamic system that results in false alarm and missing inspection, so we firstly used the parity vector to preprocess the measurement data from the sensors. A fault was detected when the preprocessed data was dealt with PCA method. The effectiveness of the improved PCA method introduced in this paper was verified by comparing fault detection capabilities of conventional PCA method under the dynamic conditions of the step fault. The results of the simulation and experimental verification of the method was expected to contribute to the fault detection and improve the accuracy and reliability of the multi-sensors system in dynamic conditions.</p

An Improved Principal Component Analysis in the Fault Detection of Multi-sensor System of Mobile Robot

To cope with the fault detection in dynamic conditions of inertial components in the mobile robots, an improved principal component analysis (PCA) method was proposed. This work took a five gyroscopes redundancy allocation model to realize the measurement of the attitude. It is hard to distinguish the fault message from dynamic message in dynamic system that results in false alarm and missing inspection, so we firstly used the parity vector to preprocess the measurement data from the sensors. A fault was detected when the preprocessed data was dealt with PCA method. The effectiveness of the improved PCA method introduced in this paper was verified by comparing fault detection capabilities of conventional PCA method under the dynamic conditions of the step fault. The results of the simulation and experimental verification of the method was expected to contribute to the fault detection and improve the accuracy and reliability of the multi-sensors system in dynamic conditions