The real-time monitoring of the grid-connected renewable energy generations is of great significance to the operation and control of active distribution systems (ADS). Considering the detailed photovoltaic (PV) system model and ambient influencing factors, this paper develops a state estimation framework for the ADS with integrated PV power plants. Firstly, suitable models of PV arrays and power converters for steady-state state estimation are developed. Based on these models, the state variables of a PV generation system are selected, and the measurement equations are derived. Finally, combined with the state estimation model of distribution networks, a framework for state estimation and bad data processing of an ADS with PV power plants is presented. A parameter estimation approach for PV power plants based on multiple measurement scans is also developed in order to address the practical situation of unknown model parameters. Simulation results show that the proposed framework can effectively capture the operating state of the distribution network and PV power plants. In addition, it enhances the estimation accuracy and bad data processing capability compared to conventional state estimation frameworks.

The poor quality of optical imaging caused by the complex and varying underwater environment is a significant challenge to underwater target recognition. Moreover, the insufficiency of relevant datasets may lead to the overfitting problem in target recognition models based on deep learning. Taking the instance segmentation of three underwater creatures (echinus, holothurian, and starfish) as an example, we propose a new method for recognition of underwater creatures. It combines the MSRCR (multi-scale Retinex with color restoration) image enhancement algorithm and the Mask R-CNN (region-based convolutional neural work) framework, and achieves a mAP (mean average accuracy) value higher than 90% on a small sample dataset. This method consists of three major steps. First, the dataset with 84 images is augmented (flip, adding noise, and GAN (generative adversarial networks)) to 430 images, and all images are enhanced with MSRCR to improve their qualities; Second, the model is pre-trained on the COCO (Microsoft common objects in context) dataset to shorten the training time and overcome overfitting; Finally, the pre-trained model is transferred to the underwater dataset, and the whole training process is completed. We achieve 97.46% precision and 94.52% recall, and the mAP (intersection over union (IOU) =50) is 94.84%. The effectiveness of the proposed method is verified by comparing it with several popular target recognition models, including SSD (Single Shot Detector), YOLOv3 (You only look once), original Mask R-CNN, and a SIFT-based (Scale-invariant feature transform) model.

It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively.

The accuracy of localization and mapping of automated guided vehicles (AGVs) using visual simultaneous localization and mapping (SLAM) is significantly reduced in a dynamic environment compared to a static environment due to incorrect data association caused by dynamic objects. To solve this problem, a robust stereo SLAM algorithm based on dynamic region rejection is proposed. The algorithm first detects dynamic feature points from the fundamental matrix of consecutive frames and then divides the current frame into superpixels and labels its boundaries with disparity. Finally, dynamic regions are obtained from dynamic feature points and superpixel boundaries types; only the static area is used to estimate the pose to improve the localization accuracy and robustness of the algorithm. Experiments show that the proposed algorithm outperforms ORB-SLAM2 in the KITTI dataset, and the absolute trajectory error in the actual dynamic environment can be reduced by 84% compared with the conventional ORB-SLAM2, which can effectively improve the localization and mapping accuracy of AGVs in dynamic environments.

Incremental extreme learning machine (I-ELM) randomly obtains the input weights and the hidden layer neuron bias during the training process. Some hidden nodes in the ELM play a minor role in the network outputs which may eventually increase the network complexity and even reduce the stability of the network. In order to avoid this issue, this paper proposed an enhanced method for the I-ELM which is referred to as the improved incremental extreme learning machine (II-ELM). At each learning step of original I-ELM, an additional offset k will be added to the hidden layer output matrix before computing the output weights for the new hidden node and analysed the existence of the offset k. Compared with several improved algorithms of ELM, the advantages of the II-ELM in the training time, the forecasting accuracy, and the stability are verified on several benchmark datasets in the UCI database.