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In transportation, LiDAR has been primarily used in autonomous vehicles to assist self-driving until recently when people realized it could also be installed at the roadside to support connected vehicles and infrastructure systems. Unlike onboard LiDAR sensors used in autonomous vehicles, roadside applications must perform complete background filtering and clustering as well as tracking real-time traffic movements within the detection zone. This paper presents an unsupervised clustering method for roadside or infrastructure-based LiDAR applications. It first converts 3D LiDAR data points into 2D so that only target points (after background filtering) will be saved in the channel-azimuth 2D structure; then, a method combining the region growing algorithm and counted component labeling is used to perform clustering. Lastly, a merging process is conducted to enhance the connected component labeling method for better outcomes. Experimental studies demonstrate that the proposed method could reach 0.011s per frame (10 Hz sensor rotation frequency) in clustering while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

This study proposes a short-term traffic flow prediction framework. The vector autoregression (VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model based on deep learning are employed in the analysis. An intrinsic association among traffic variables is first evaluated using the VAR model, and the predictable relationship of these variables is determined. Then the multi-features speed prediction for one spatial location using the CNN-LSTM hybrid neural network model is conducted, the prediction results prove that prediction with multi-feature is better than that with a single feature. Subsequently, several popular deep learning models and other shallow predicted models are proposed to be compared with the constructed CNN-LSTM network model, and the comparison illustrates that the model performance of the developed CNN-LSTM network model is superior to other models in forecasting the short-term traffic flow. Then the multi-feature speed predictions for a group of spatial locations are further conducted using the CNN-LSTM model. The result demonstrates the predictive accuracies are associated with the spatial correlation of traffic flow. Finally, a heatmap is produced to visualize the predicted speed, from which the spatial-temporal traffic condition can be presented clearly. The research results have the potential to be applied to the travel information releasing and traffic congestion management. [ABSTRACT FROM AUTHOR]

Unlike conventional interconnected power systems, energy Internet presents an unsolved and more challenging problem for the society including transfer impedance, damping, large penetration of distributed generation, and numerous hybrid integration of generators and converters. In this paper, a novel energy function designed for energy internet router is proposed to accurately evaluate its transfer stability. The reliability of the proposed energy function is confirmed through both theoretical analysis and empirical simulations. Furthermore, generalized methods to determine critical stable energy, stable domain, and critical clearing time are proposed. By updating stability criterion and evaluating system energy of post-disturbance system, fault energy-based impulsive feedback control method is specifically designed for energy Internet to stabilize the system. Simulation and experimental results are provided to validate the effectiveness of the proposed energy function and nonlinear control method. [ABSTRACT FROM PUBLISHER]

As system transient stability is one of the most important criterions of microgrid (MG) security operation, and the performance of an MG strongly depends on the placement of its energy storage devices (ESDs); optimal placement of ESDs for improving system transient stability is required for MGs. An MG structure preserving energy function is first developed for voltage source inverter-based MGs since the existing energy functions, based on synchronous generators and the conventional power system, are not applicable for MGs. The concept of internal potential energy of distributed energy resource is presented instead of the kinetic energy term in traditional energy function. Then, a novel approach for the optimal placement of ESDs is proposed based on MG structure preserving energy function for improving MG transient stability. Simulation and experimental results show that the proposed method can be used to find the optimal placement of ESDs and improve the system stability effectively. [ABSTRACT FROM PUBLISHER]

A simplified packaging process was successfully developed for a wafer-level light emitting diode (WL-LED) chip aiming at very-high power solid-state lighting (SSL) applications. Compared with the traditional chip-on-board (COB) technology, WL-LED chip not only greatly simplifies the packaging process but also enables the lighting source more compact. The fabricated blue WL-LED SSL source with a record-high light output power of 305 W exhibits $\sim 30$ % wall plug efficiency at an input electrical power of 1026 W. [ABSTRACT FROM PUBLISHER]