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In view of problems that path planning of the coal mine rescue robot planned by A* algorithm has many path turning points and the path is not smooth enough, a path smoothing algorithm of coal mine rescue robot based on improved A* algorithm was proposed. Firstly, the Douglas-Peucker (D-P) algorithm is used to process the whole path generated by A* algorithm, and eliminate redundant nodes in the path, and extracts several path nodes as key nodes, which solves the problem that there are many redundant nodes and a large number of path turning points of the A* algorithm. Then, the whole path based on the key nodes is fitted by cubic path function, and a smooth path is obtained, which can effectively shorten the path length. The simulation results show that the algorithm has strong universality, although the planning time is slightly increased compared with the A* algorithm, but the planned path turns are few, the path length is short, and the path quality is relatively better than that of the genetic smoothing algorithm.

Cleaning is one of the most important steps in the harvesting process, and the prolonged and high-load operation of the vibrating sieve can decrease its reliability. To uncover the structural flaws of the cleaning sieve in the crawler combine harvester and establish a foundation for quality inspection, this paper proposes a method for durability testing and analysis using vibration and strain signals. Via the modal analysis of the cleaning sieve, the most susceptible areas for fault signals are identified. Subsequently, a specialized test rig exclusively designed for the examination of the durability of the cleaning sieve is constructed. After following 96 h of uninterrupted operation, the vibration plate of the cleaning sieve sustains damage, resulting in atypical noise. A signal analysis reveals that the primary vibration signal of the cleaning sieve primarily consists of a fundamental frequency of 5 Hz, corresponding to the driving speed, as well as a frequency doubling signal of 50 Hz. After the occurrence of damage, the peak amplitude of the received vibration signal increases by over 86.3%. Furthermore, the strain gauge sensor situated on the support plate of the rear sieve detects anomalous signals with frequencies exceeding 300 Hz, which are accompanied by a considerable rise in the power spectral density. This research has significant importance for enhancing the service life of the cleaning sieve and optimizing the overall machine efficiency. [ABSTRACT FROM AUTHOR]

Background: Oral Squamous Cell Carcinoma (OSCC) is one of the most severe cancers in the world, and its early detection is crucial for saving patients. There is an inevitable necessity to develop the automatic noninvasive OSCC diagnosis approach to identify the malignant tissues on Optical Coherence Tomography (OCT) images. Methods: This study presents a novel Multi‐Level Deep Residual Learning (MDRL) network to identify malignant and benign(normal) tissues from OCT images and trains the network in 460 OCT images captured from 37 patients. The diagnostic performances are compared with different methods in the image‐level and the resected patch‐level. Results: The MDRL system achieves the excellent diagnostic performance, with 91.2% sensitivity, 83.6% specificity, 87.5% accuracy, 85.3% PPV, and 90.2% NPV in image‐level, with 0.92 AUC value. Besides, it also implements 100% sensitivity, 86.7% specificity, 93.1% accuracy, 87.5% PPV, and 100% NPV in the resected patch‐level. Conclusion: The developed deep learning system expresses superior performance in noninvasive oral squamous cell carcinoma diagnosis, compared with traditional CNNs and a specialist. [ABSTRACT FROM AUTHOR]

In view of problem of low precision of parameter identification based on least square method, a parameter identification method of mine-used robot driving system based on whale optimization algorithm was proposed. Driving system model of mine-used robot is established to determine parameters to be identified which are considered as individual position of whale colony. Fitness function is used to measure the individual position of whale colony. The optimal identification parameters can be obtained by continuously updating the individual position of whale colony with whale foraging strategy until the optimal individual position of whale colony is obtained. The simulation and experimental results show that the method has higher identification accuracy than the parameter identification method based on least square method.