The identification and detection of microalgae are essential for the development and utilization of microalgae resources. Traditional methods for microalgae identification and detection have many limitations. Herein, a Feature-Enhanced YOLOv7 (FE-YOLO) model for microalgae cell identification and detection is proposed. Firstly, the feature extraction capability was enhanced by integrating the CAGS (Coordinate Attention Group Shuffle Convolution) attention module into the Neck section. Secondly, the SIoU (SCYLLA-IoU) algorithm was employed to replace the CIoU (Complete IoU) loss function in the original model, addressing the issues of unstable convergence. Finally, we captured and constructed a microalgae dataset containing 6300 images of seven species of microalgae, addressing the issue of a lack of microalgae cell datasets. Compared to the YOLOv7 model, the proposed method shows greatly improved average Precision, Recall, mAP@50, and mAP@95; our proposed algorithm achieved increases of 9.6%, 1.9%, 9.7%, and 6.9%, respectively. In addition, the average detection time of a single image was 0.0455 s, marking a 9.2% improvement.

Firstly, this paper defines the definition and attribute of shore power service charge, and combs the current situation of shore power service charge and electricity charge in China. At the same time, the paper analyses the cost of shore power service, electricity charge of the power supply department and the cost of ship's using fuel oil for self-generation, and makes a comprehensive comparison between the cost of ship's using shore power and the cost of ship's self-generation. Finally, according to the principle of ensuring that the cost of using shore power (shore power service charge plus electricity charge) is lower than the cost of self-generation for ships, the paper puts forward suggestions that the shore power service charge should be collected according to the power usage and the government should issue guidance prices.

The problem of ships’s ultra-draft is one of the main threats to the safety of inland navigation channel. Focus on the difficulty of draft detection, the first double-floating off-ship detection platform has been developed and applied successfully. This paper mainly introduces the general layout, major components and key technologies of the detection platform. A rigid-flexible supporting structure is designed to keep the stability of detection frame under two floating platforms. The optimum cross section of the detection frame is selected by analysis of loads and vortex-excited vibration simulation,. A draft detection system based on single-beam ultrasonic array is designed. The data processing techniques of interfering data and error compensation are explored. The detection platform can detect the draft depth of ships accurately and efficiently, and can monitor and manage the ships constantly. Once the shipping management department receives the warning information of ultra-draft, it immediately takes measures to restrict the passage of ultra-draft, thus ensuring the safety and smooth flow of inland river traffic.