Summary With the rapid expansion of global offshore wind power market, the research on improving the full life cycle income and reducing the construction and operation and maintenance costs has attracted the attention of scholars in the industry. In view of the different aging degree and maintenance cycle of wind turbines, this paper studies the optimized design of patrol path for offshore wind farms based on genetic algorithm (GA) and particle swarm optimization (PSO) with traveling salesman problem (TSP). Firstly, the problem of patrol routing planning in offshore wind farms is described as the traveling salesman problem of shortest route optimization. Secondly, the GA and PSO algorithms are simulated and verified separately, and the patrol path distance is taken as the objective function. Finally, through simulation experiments, the optimized patrol path performances of PSO and GA are compared, which can help to find a shortest route and reduce the operation and maintenance costs.

Summary The experienced doctors can alleviate symptoms such as headaches, insomnia, anxiety, and depression by observing the patient's ears and massaging specific areas. In order to achieve remote ear condition diagnosis and guide patients to massage their ears independently through the network, patients can use their mobile phones to take and send photos of ears to doctors. However, due to significant differences in the clarity of photos taken by different mobile phones, as well as susceptibility to haze, lighting, jitter, and low pixels, the quality of photos is poor, which affects the accuracy of remote diagnosis by doctors. This study adopted an image preprocessing method based on He Kaiming's dark channel prior dehazing method to enhance the original ear images captured by mobile phones. The dehazing algorithm was used to remove the haze effect of the ear images, improving image quality and contrast, making the wrinkles, protrusions, pigmentation and other areas of the ear more obvious. The experiment has showed the comparison by adjusting weight from 15% to 95% between two methods—dark channel prior method and the dark channel prior method after preprocessing, which has proven the effectiveness of dehazing method in human ear images taken by mobile phones. The image quality after preprocessing and dehazing is widely recognized and accepted by doctors at hospitals in Hangzhou, China.

Capsule endoscopic robot holds great promise for the early diagnosis of gastrointestinal diseases without causing discomfort to patients. However, currently available active capsule endoscopic robots suffer from issues such as complex structure, poor mobility, large size, and high cost, which have hindered their widespread adoption and resulted in a lower screening rate for gastrointestinal diseases. To address these challenges, this paper proposes a highly integrated propeller-driven capsule endoscopic robot (PCER) system that integrates STM32 processor, magnetic sensor, IMU, RF communication unit, and motor drive. The micro propeller of the PCER has been analyzed through finite element simulation to ensure its efficiency. FLUENT software has been utilized to simulate the fluid force acting on the PCER as it moves through a liquid medium. The results of the simulation are then used to determine the optimal pitch angle for the robot's movement. The thrust generated by the capsule robot propellers has been measured using a lever mechanism to investigate the relationship between the thrust and voltage applied to the motors. The experiments confirmed that the PCER is capable of performing flexible motions within fluid environments, such as changing pitch angle during movement, passing circular obstacles, horizontal motion, and spiral ascent. These findings demonstrate the feasibility of the proposed PCER as an effective tool for non-invasive early screening of gastrointestinal diseases.

Summary In the field of ear acupoint therapy in traditional Chinese medicine, many diseases can be treated through ear acupoint acupuncture, buried needle, bleeding, ear acupoint compression, massage, electric stimulation, and other programs. To achieve this goal, the positioning of the earhole feature point region is required. Based on the active appearance model, this article proposes an automatic identification feature point scheme through 168 datasets of data (including data collected based on standard equipment and ordinary mobile phones) collected from Hangzhou Normal University, China. The experimental results show that the average distance error of 91 feature points is less than 6 pixels, which can replace the identification work of TCM ear acupoint doctors.

Abstract A convolutional neural network‐regional long Short‐Term memory (CNN‐RLSTM) is proposed, which is a convolutional neural network‐regional long short‐term memory (CNN‐RLSTM) that combines CNN and regional LSTM. The model can effectively distinguish the affective polarity of different targets through a regional LSTM while reducing the training time of the model. In addition, the model can retain the sentiment information of the whole sentence through a CNN network at the sentence level. Experimental results on different data sets show that the CNN‐RLSTM model is better than the traditional model and the deep network model.

Summary The dynamic multi‐objective optimization has important applications in many fields, especially for the multi‐issues about trading‐off problems. Herein the dynamic multi‐objective optimization problem is applied in the stepping downstairs motions of humanoid robot in which three sub‐optimization problems are proposed. First, the motion analysis of the robot's stepping downstairs motion is processed and the dynamic multi‐objective optimization algorithm is introduced as well. The optimization algorithm is changed by the mutation mode of the traditional genetic algorithm. Second, the motion analysis of the robot's stepping downstairs motion is carried out. Finally, the walking speed function, walking stability function, and electrode cost function are obtained. The effects were validated through simulating experiments.

Cloud computing is the next generation of computation and is regarded as the fifth utility service. A core issue of cloud computing is the deployment of virtual machine (VM). A new method had been constructed for VM cluster based on graph theory in this study. The authors first described VM cluster by energy minimisation. Then, they changed deployment of VM cluster into maximum flow minimum cut problem, added sink and source point, and found activity node. Finally, activity tail‐nodes were connected and cut formed for VM cluster. Experimental results show that the segmentation method can effectively realise the VM deployment clusters, can reduce the overall bandwidth requirements after deployment.