Flying vertebrates exhibit sophisticated wingbeat kinematics. Their specialized forelimbs allow for the wing morphing motion to couple with the flapping motion during their level flight, Previous flyable bionic platforms have successfully applied bio-inspired wing morphing but cannot yet be propelled by the morphing-coupled wingbeat pattern. Spurred by this, we develop a bio-inspired flapping-wing aerial vehicle (FWAV) entitled RoboFalcon, which is equipped with a novel mechanism to drive the bat-style morphing wings, performs a morphing-coupled wingbeat pattern, and overall manages an appealing flight. The novel mechanism of RoboFalcon allows coupling the morphing and flapping during level flight and decoupling these when maneuvering is required, producing a bilateral asymmetric downstroke affording high rolling agility. The bat-style morphing wing is designed with a tilted mounting angle around the radius at the wrist joint to mimic the wrist supination and pronation effect of flying vertebrates' forelimbs. The agility of RoboFalcon is assessed through several rolling maneuver flight tests, and we demonstrate its well-performing agility capability compared to flying creatures and current flapping-wing platforms. Wind tunnel tests indicate that the roll moment of the asymmetric downstroke is correlated with the flapping frequency, and the wrist mounting angle can be used for tuning the angle of attack and lift-thrust configuration of the equilibrium flight state. We believe that this work yields a well-performing bionic platform and provides a new actuation strategy for the morphing-coupled flapping flight.

The fast search and find of density peaks clustering (FDP) is an algorithm that can gain satisfactory clustering results with manual selection of the cluster centres. However, this manual selection is difficult for larger and more complex datasets, and it is easy to split a cluster into multiple subclusters. We propose an automatic determination of cluster centres algorithm (A-FDP). On the one hand, a new decision threshold is designed in A-FDP combined with the InterQuartile Range and standard deviation. We select the points larger than the decision threshold as the cluster centres. On the other hand, these cluster centres are made as nodes to construct the connected graphs. These subclusters are merged by finding the connected components of the connected graph. The results show that the A-FDP can obtain better clustering results and have higher accuracy than other classical clustering algorithms on synthetic and UCI datasets.

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the testing cases from different medical centers have not been evaluated. To comprehensively benchmark abdominal organ segmentation methods, we organized the first Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE) challenge, where the segmentation methods were encouraged to achieve high accuracy on the testing cases from different medical centers, fast inference speed, and low GPU memory consumption, simultaneously. The winning method surpassed the existing state-of-the-art method, achieving a 19× faster inference speed and reducing the GPU memory consumption by 60% with comparable accuracy. We provide a summary of the top methods, make their code and Docker containers publicly available, and give practical suggestions on building accurate and efficient abdominal organ segmentation models. The FLARE challenge remains open for future submissions through a live platform for benchmarking further methodology developments at https://flare.grand-challenge.org/.

The find of density peak clustering algorithm (FDP) has poor performance on high‐dimensional data. This problem occurs because the clustering algorithm ignores the feature selection. All features are evaluated and calculated under the same weight, without distinguishing. This will lead to the final clustering effect which cannot achieve the expected. Aiming at this problem, we propose a new method to solve it. We calculate the importance value of all features of high‐dimensional data and calculate the mean value by constructing random forest. The features whose importance value is less than 10% of the mean value are removed. At this time, we extract the important features to form a new dataset. At this time, improved t‐SNE is used for dimension reduction, and better performance will be obtained. This method uses t‐SNE that is improved by the idea of random forest to reduce the dimension of the original data and combines with improved FDP to compose the new clustering method. Through experiments, we find that the evaluation index NMI of the improved algorithm proposed in this paper is 23% higher than that of the original FDP algorithm, and 9.1% higher than that of other clustering algorithms (K‐means, DBSCAN, and spectral clustering). It has good performance in high‐dimensional datasets that are verified by experiments on UCI datasets and wireless sensor networks.

In order to study the variation law of the influence degree of air quality factors under time series, this article selects the hourly concentration values of NO2and O3from November 2017 to October 2018 in Lanzhou city as the research object, and based on the coarse granulation method, NO2and O3. The correlation value of hourly concentration value is transformed into the influence degree symbol, and the sequence of effect degree variation under time series is obtained. The network model is constructed by using complex network theory to analyze the time series of NO2and O3influence degree series, and the differences and changes in network topology characteristics in overall and different season effect degree symbols of Lanzhou are analyzed. The results show the following. First, from the network analysis, the number of negative correlations of NO2to O3is far greater than that of positive correlation and basic noncorrelation effect degree, indicating that NO2and O3are negatively correlated. From the factor of season, the law of negative correlation is summer > autumn > spring > winter, indicating that light and temperature are the main factors for the conversion of NO2to O3. Second, through the analysis of Lanzhou's overall and seasonal related network topological features, such as node degree distribution, node intensity distribution, and network betweenness, the change sequence consisting of the influence degree symbols h, H, and m plays an important role in each network. The sequence of changes consisting of M, H, and S has the lowest effect in each network, indicating that in the day of the time series, the conversion of NO2to O3in Lanzhou is mainly dominated by different degrees of negative correlation. Third, through the analysis of network cluster degree, it is found that most of the changes in the degree of change have a transformation and a high degree of transformation, indicating that the effect of NO2on O3in the original sequence is less stable. Fourth, through the network average shortest path and diameter analysis, it is found that the conversion between the changes in the degree of influence requires at least one day and at most three days. That is to say, the influence of NO2on O3can be maintained for up to three days. From the network, the complexity of the influence of NO2on O3in the original sequence of four seasons was founded.