Different from the ground image with uniform haze, the haze in remote sensing (RS) image has the characteristics of irregular shape and uneven concentration in hazy weather. It brings a great challenge to the application of RS image data in advanced image processing tasks. A novel dehazing network for non-uniform hazy remote sensing image, named as KFA-Net, is proposed to solve the aforementioned issues. The designed asymmetric size feature cascade (ASFC), k-means pixel attention (KPA) and FFT channel attention (FCA) in KFA-Net all show excellent effects. Compared with symmetrically linked typical Unet, ASFC can more easily extract shallow features for feature reconstruction. Furthermore, different from the commonly used pixel attention that compresses feature maps directly, KPA introduces k-means clustering algorithm in machine learning into the attention mechanism, which facilitates network training to focus on the thick hazy region. Compared to typical squeeze-and-excitation block, FCA uses the low-frequency region feature of spectrogram to obtain the attention weight coefficient in the frequency domain, making network training pay more attention to the feature of image low-frequency region. Extensive comparison experiments verify that the proposed KFA-Net has the great superiority. PSNR/SSIM of KFA-Net are 31.0952% and 6.6401% higher than DCP with the highest citation in traditional dehazing methods, respectively. PSNR/SSIM of KFA-Net are 2.2049% and 0.4966% higher than the recently proposed 4KDehazing with the best performance among all comparison dehazing methods, respectively. The KFA-Net proposed in this research can greatly enhance the temporal and spatial scope of RS image application in hazy weather conditions.

Accurate citywide traffic inference is critical for improving intelligent transportation systems with smart city applications. However, this task is very challenging given the limited training data, due to the high cost of sensor installment and maintenance across the entire urban space. A more practical scenario to study the citywide traffic inference is effectively modeling the spatial and temporal traffic patterns with limited historical traffic observations. In this work, we propose a dynamic multi-view graph neural network for citywide traffic inference with the method CTVI+. Specifically, for the temporal dimension, we propose a temporal self-attention mechanism that is capable of learning the dynamics of traffic data with the time-evolving traffic volume variations. For spatial dimension, we build a multi-view graph neural network, employing the road-wise message passing scheme to capture the region dependencies. With the designed spatial-temporal learning paradigms, we enable our traffic inference model to encode the dynamism from both spatial and temporal traffic patterns, which is reflective of intra- and inter-road traffic correlations. In our evaluation, CTVI+ achieves consistent better performance compared with different baselines on real-world traffic volume datasets. Further ablation study validates the effectiveness of key components in CTVI+. We release the model implementation at https://github.com/dsj96/TKDD.

A three-phase 3.3kV/400kVA voltage source converter (VSC) based on series-connected power devices for medium voltage variable speed drives is designed in this paper. The series-connected power devices are balanced using an active clamping circuit, which consists of an auxiliary switch and a capacitor. During the switching period, the power device voltage will be clamped at the voltage of ACM capacitor. By balancing the voltages of ACM capacitors, we can balance the voltages of series-connected power devices as well. The balancing circuit does not affect the switching of main power devices, and the power devices in the prototype switch at 10kHz. The power loss of the balancing circuit is minor. In each arm of the prototype, double-sided cooling modules as well as heatsinks are stacked vertically to series connect 6 power devices. Experimental tests on the prototype have been tested under 1kV with 4 power devices connected in each arm, and the voltage imbalance degree of the 4 series-connected power devices is less than 5%. Thermal simulation results show the junction temperature is 79°C under the rated power 400kVA. The power density of the prototype is 4.3MVA/m 3 .

With the development of mobile position techniques, sensing the citywide traffic information has been well recognized as a crucial task for various urban computing applications, such as intelligent transportation system, location-based recommendation, and user mobility modeling. With the consideration of high cost for sensor installment and maintenance, the traffic monitoring spatial coverage is often very limited in practical urban sensing scenarios. The goal of this paper is to perform the traffic inference over road segments which lack of (with very limited) historical traffic observations. Towards this end, we propose a temporal multi-view graph convolutional network for C itywide T raffic V olume I nference (CTVI) which jointly captures the spatial-temporal dependencies across different time intervals and geographical locations. In our CTVI framework, we design our attentive multi-view graph neural architecture based on our generated spatial and feature affinity graphs, to perform the cross-layer message passing with the preservation of road segment-wise topological context. In addition, we develop a temporal self-attention module to encode the evolving traffic patterns over time, which incorporates the time-wise relation contextual signals into the main embedding space. Furthermore, we propose a joint learning objective function that consists of an unsupervised random walk enhancement and a semi-supervised spatio-temporal volume constraint to guide the learning of road segment representations for citywide traffic volume inference. Evaluation results on real-world traffic datasets demonstrate the superiority of our proposed CTVI framework as compared to state-of-the-art baselines.