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This paper aims to reasonably demonstrate the role of the rotor-side converter (RSC) in subsynchronous oscillation (SSO) and investigates the influence and relationships of all control parameters. Thus, the equivalent circuit of a doubly fed induction generator (DFIG) that considers the RSC is imperative. First, the pertinent equivalent model of a DFIG with RSC control is established. Compared with previous works, this paper innovatively merges the RSC and induction generator with flux and voltage, obtains a complete analytical expression for the equivalent power supply and impedance containing all parameters of both the inner and outer loops of the RSC and then constructs a reasonable equivalent model. The action of the RSC is successfully demonstrated and reflects how the control loops and parameters of the RSC affect the circuit structures and properties of the equivalent model. Then, based on this proposed model, the influence mechanism of the RSC on the SSO of the grid-connected system is carefully analyzed.

In this paper, a two-stage method is proposed for predicting the catch of skipjack tuna (Katsuwonus pelamis) from a 2D sea environmental pattern. Following the assumption that sea water temperature and sea surface height (SSH) which fishermen often use for finding fishing spots has a correlation with the skipjack tuna catch, a new approach of using Faster R-CNN in object detection is proposed. The proposed method consists of two part. In the first part, taking a sea temperature map as input, Faster R-CNN extracts the candidates of where skipjack tuna would be on the map in order to imitate the behaviors of fishers. In the second part, Support Vector Regression (SVR) estimates the catch amount in each candidate. Fater R-CNN is applied to several sea environmental patterns with three different loss functions and compares each performance. The proposed model is evaluated by comparing the result with average fishers’ ability on the skipjack tuna catches and several criteria for evaluating the proposed model. The results show that the proposed method is able to outperform the average fishers’ ability by an average of 3%.

Farmers around the world face the challenge of growing more food for the increasing world population. On top of that, external threats such as pests (weeds and insects) pose a threat to crop production and it is necessary to take early steps to reduce the risk. This paper presents semantic segmentation of canola field images collected under natural conditions. The dataset contains four unbalanced classes; background, crop, weeds, and damages in the crop. The damages to the crop leaves are small round shaped and share the same texture and colour as whitish stones from the background. We propose, DeepVeg, a deep learning segmentation model that focuses on the smallest (damage) class without affecting other classes to solve the class imbalance issue. Early stage canola field image dataset is utilized for training and testing the proposed model. Evaluation results show that the proposed method outperforms the benchmark deep learning models and effectively addresses the weed and damaged canola plants segmentation problem. The DeepVeg model demonstrates a superior mean intersection over union score greater than 0.76 and $accuracy$ above 0.97 for four class segmentation. The model also shows robustness in detecting unlabelled, newly grown weeds and canola and is also able to distinguish the similar rounded structured canola plant and weed with small amounts of data for model training, which is suitable for early stage damage and weed segmentation.

In this paper, a hybrid Discrete Fractional Cosine Transform (DFrCT) with Tikhonov regularization based Turbo Minimum mean square error (MMSE) equalization (DFrCT-Turbo) is presented to suppress inter-carrier interference (ICI) over underwater acoustic channels (UWA). The scheme is based on Orthogonal Frequency Division Multiplex (OFDM) scenario. In addition, an optimal order selecting method for DFrCT is developed by maximizing carrier to interference ratio (CIR) to UWA channel character. Simulation results show that BER improvement of up to 5dBs over traditional orthogonal based methods with moderate complexity.