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The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

Traditional scenic route planning only considers the shortest path, which ignores the information of scenic road conditions. As the most effective direct search method to solve the shortest path in static road network, A* algorithm can plan the optimal scenic route by comprehensively evaluating the weights of each expanded node in the gridded scenic area. However, A* algorithm has the problem of traversing more nodes and ignoring the cost of road in the route planning. In order to bring better travel experience to the travelers, the above factors are taken into account. This paper presents a path planning method based on the improved A* algorithm. Firstly, the heuristic function of the A* algorithm is weighted by exponential decay to improve the calculation efficiency of the algorithm. Secondly, in order to increase the practicality of the A* algorithm, the impact factors that road conditions is introduced to the evaluation function. Finally, the feasibility of the improved A* algorithm is verified through simulation experiments. Experimental results show that the improved A* algorithm can effectively reduce the calculation time and road cost. [ABSTRACT FROM AUTHOR]

In order to improve the course control accuracy of unmanned surface vehicle (USV), a course control system based on improved active disturbance rejection control (ADRC) is proposed in this paper. The course controller designed in this paper can realize automatic course maintenance and achieve good stability of USV. The extended state observer of ADRC is used to estimate the unknown disturbance in real time and give appropriate compensation. It is difficult to set the ADRC parameters, so firefly algorithm (FA) is introduced into the ADRC algorithm to realize the self-setting of nonlinear feedback control law parameters. The simulation results show that the course control system designed in this paper can achieve precise control of the USV's course and the system is robust. [ABSTRACT FROM AUTHOR]

Cloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on 10 types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2000 images per second. In actual application, the algorithm should consider the processing speed, because the images are on the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2000 images per second. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics. [ABSTRACT FROM AUTHOR]