The blade is the key component for energy capture in horizontal axis tidal current turbine, and the blade’s design is directly related to the efficiency of the power generation device. The seawater flow velocity is a crucial parameter for blade design. Since the seawater velocity changes constantly, it is extremely important to determine the design velocity of the blade. This article proposes a calculation method for the design velocity of the blade so that the designed blade can achieve better energy collection efficiency under a variable flow velocity. This study performed the following investigations: (1) The author tested and analyzed the seawater velocity variation at the experimental site, determining the designed flow velocity via calculations; (2) The author combined the blade element momentum and Wilson’s optimization design method using the MATLAB software to calculate the blade shape parameters and predict the performance under a number of different flow velocity forecasts; (3) When testing the device under sea conditions, the experimental results showed that the performance capture devices and digital projections are basically the same, which verifies the validity and effectiveness of the design method.

Wave and current energy can be harnessed in the East China Sea and South China Sea; however, both areas are subject to high frequencies of typhoon events. To improve the safety of the ocean energy conversion device, a Floating Ocean Energy Conversion Device (FOECD) with a single mooring system is proposed, which can be towed to avoid severe ocean conditions or for regular maintenance. In this paper, the structure of the FOECD is introduced, and it includes a catamaran platform, an oscillating buoy part, a current turbine blade, hydraulic energy storage and an electrical generation part. The numerical study models the large catamaran platform as a single, large buoy, while the four floating buoys were modeled simply as small buoys. Theoretical models on wave energy power capture and efficiency were established. To improve the suitability of the buoy for use in the FOECD and its power harvesting capability, a numerical simulation of the four buoy geometries was undertaken. The shape profiles examined in this paper are cylindrical, turbinate (V-shaped and U-shaped cone with cylinder), and combined cylinder-hemisphere buoys. Simulation results reveal that the suitability of a turbinate buoy is the best of the four types. Further simulation models were carried out by adjusting the tip radius of the turbinate buoy. Three performance criteria including suitability, power harvesting capability and energy capture efficiency were analyzed. It reveals that the turbinate buoy has almost the same power harvesting capabilities and energy capture efficiency, while its suitability is far better than that of a cylindrical buoy.

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and the failure to detect quality issues promptly in accordance with manufacturing settings, which may trigger the propagation of downstream defects. Currently, most inspection quality methods consist of direct measurements followed by manual judgment. The integration of deep learning methods provides a feasible way in which to identify defects on time, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model, built around discrete manufacturing characteristics, and uses fuzzy theory to evaluate the quality levels of production stages. Furthermore, a multivariate long- and short-term memory sequence model is proposed in order to explore the qualitative information from time domain features. The data regarding the produced water dispensers are validated using three evaluation indices, namely, RMSE, MAE, and MAPE. The results indicate that the multivariate long- and short-term memory model exhibits stronger prediction performance.

Arthroscopic surgery is a primary technique for treating joint-related diseases, widely embraced in clinical practice for its minimally invasive and precise nature. However, intraoperative bleeding often generates blood mist, significantly impairing the surgeon’s field of view and necessitating prompt high-flow drainage for clearance. Therefore, accurate bleeding detection and localization is a prerequisite for blood mist removal. This paper introduces a pixel-based feature extraction scheme aimed at detecting bleeding frames in arthroscopic videos. In contrast to previous bleeding detection methods, this approach utilizes statistical features based on composite color to analyze arthroscopic images and extract features. Then, a feature selection strategy is proposed to select the best features from the extracted features.Subsequently, the selected features are fused and then classified using an improved KNN classifier to differentiate between bleeding and non-bleeding images. In addition to this, a post-processing scheme is introduced to enhance bleed frame detection performance by exploiting temporal variations across consecutive frames in arthroscopic videos. Lastly, a region-based detection algorithm is proposed for identifying bleeding zones within images depicting bleeding. By conducting extensive experimental analysis on the arthroscopic image and video dataset. The proposed method achieves accuracies of 95.8%, 97.3%, and 95.3% for bleed frame detection in terms of accuracy, sensitivity, and specificity respectively. The results demonstrate that the proposed algorithm effectively detects bleeding frames and bleeding zones in arthroscopic videos.

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and failure to find quality issues in a timely manner within manufacturing settings may trigger the propagation of defects downstream. Currently, most of the inspection quality methods are direct measurement followed by manual judgment. The integration of deep learning methods provides a feasible way to identify defects in a timely manner, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model around discrete manufacturing characteristics, and use fuzzy theory to evaluate the quality level of production stages. Building Multivariate Long and short-term memory learning hidden quality representations to extract predictions from multi-level inspection data in manufacturing systems. Finally, by validating the data of actual produced water dispensers according to three evaluation indexes, RMSE, MAE, MAPE, the results show that Multivariate Long and short-term memory has better prediction performance.