This paper proposes the theory and application of a new designing method of the Fractional Order PID (FOPID) controller for boost converters. FOPID is a PID where the derivation and integration orders are of fractional order rather than integer. A FOPID is an extension to classic integer order PID controllers, that potentially promises better results. The method is based on the use of a Multi-Objective optimization evolutionary algorithm called Strength Pareto Evolutionary Algorithm (SPEA). Other controller methods cannot guarantee a good start-up response but the proposed method can provide an excellent start-up response besides desired dynamic response. The optimum fractional order PID coefficients for the desired control objectives are included and designers can implement each of them based on objective functions priority. A comparison between the optimum integer order PID controller and optimum fractional order PID controller is presented in the paper. The simulation and some experimental results prove the superiority of the fractional controllers over the integer controllers.

The ability to differentiate between various fish species plays an essential role in aquaculture. It helps to protect their populations and monitor their health situations and their nutrient systems. However, old machine learning methods are unable to detect objects in images with complex backgrounds and especially in low-light conditions. This paper aims to improve the performance of a YOLO v5 model for fish recognition and classification. In the context of transfer learning, our improved model FishDETECT uses the pre-trained FishMask model. Then it is tested in various complex scenes. The experimental results show that FishDETECT is more effective than a simple YOLO v5 model. Using the evaluation metrics Precision, Recall, and mAP50, our new model achieved accuracy rates of 0.962, 0.978, and 0.995, respectively.

Fish play a major role in the human nutritional system, and farmers need to know the accurate prediction of fish weight in order to optimize the production process and reduce costs. However, existing prediction methods are not efficient. The formulas for calculating fish weight are generally designed for a single species of fish or for species of a similar shape. In this paper, a new hybrid method called SFI-XGBoost is proposed. It combines the VIF (variance inflation factor), PCC (Pearson’s correlation coefficient), and XGBoost methods, and it covers different fish species. By applying GridSearchCV validation, normalization, augmentation, and encoding techniques, the obtained results show that SFI-XGBoost is more efficient than simple XGBoost. The model generated by our approach is more generalized, achieving accurate results with a wide variety of species. Using the r2_score evaluation metric, SFI-XGBoost achieves an accuracy rate of 99.94%.

The conservation and protection of fish species are crucial tasks for aquaculture and marine biology. Recognizing fish in underwater environments is highly challenging due to poor lighting and the visual similarity between fish and the background. Conventional recognition methods are extremely time-consuming and often yield unsatisfactory accuracy. This paper proposes a new method called DeepFishNET+. First, an Underwater Image Enhancement module was implemented for image correction. Second, Global CNN Stream (RestNet50) and a Local Transformer Stream were implemented to generate the Feature Map and Feature Vector. Next, a feature fusion operation was performed in the Cross-Attention Feature Fusion module. Finally, Yolov8 was used for fish detection and localization. Softmax was applied for species recognition. This new approach achieved a classification precision of 98.28% and a detection precision of 92.74%.