Gaussian Mixture Model and Self-Organizing Map Neural-Network-Based Coverage for Target Search in Curve-Shape Area

Grid-Based Clustering Using Boundary Detection

Clustering can be divided into five categories: partitioning, hierarchical, model-based, density-based, and grid-based algorithms. Among them, grid-based clustering is highly efficient in handling spatial data. However, the traditional grid-based clustering algorithms still face many problems: (1) Parameter tuning: density thresholds are difficult to adjust; (2) Data challenge: clusters with overlapping regions and varying densities are not well handled. We propose a new grid-based clustering algorithm named GCBD that can solve the above problems. Firstly, the density estimation of nodes is defined using the standard grid structure. Secondly, GCBD uses an iterative boundary detection strategy to distinguish core nodes from boundary nodes. Finally, two clustering strategies are combined to group core nodes and assign boundary nodes. Experiments on 18 datasets demonstrate that the proposed algorithm outperforms 6 grid-based competitors.

Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model

Multirotors play an important role in electric power inspection, border control, modern agriculture, forest fire fighting, flood control, disaster prevention, etc. Multirotor failures, such as a communication fault, a sensor failure, or a power system anomaly, may well lead to mission interruption, multirotor crashes, and even casualties. To ensure flight safety, a multirotor decision module should be established to prevent or reduce the adverse effects caused by failure. Therefore, this paper proposes a real-time safety decision-making method for multirotor flight strategies based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Firstly, the flight of the multirotor was simulated based on the Rflysim UAV flight simulation platform, and a fault-injection module was constructed to simulate different types of faults, so as to realize real-time monitoring of the flight status of the multirotor, and to collect flight data under various faults to establish condition assessment information sources. Then, based on the random forest algorithm, a failure level classification model of the multirotor was constructed, the model was trained and verified by inputting flight data of three types of safety level failures, and the model effectively classified the failure levels of the multirotor. Under this framework, a real-time safety decision-making model for the multirotor based on the TOPSIS model was constructed to realize the flight safety decision-making of the multirotor under different faults. This method can effectively realize the real-time decision-making for the flight strategy of a multirotor. By comparison with other models, the classification accuracy of the failure level classification model is higher, and the consideration of flight decision-making is more comprehensive and accurate, thus effectively ensuring the flight safety of the multirotor.

Vibration Prediction of Flying IoT Based on LSTM and GRU

Drones, flying Internet of Things (IoT), have been widely used in several industrial fields, including rescue, delivery, military, and agriculture. Motors connected to a drone’s propellers play a crucial role in its movement. However, once the motor is damaged, the drone is at risk of falling. Thus, to prevent the drone from falling, an accurate and reliable prediction of motor vibration is necessary. In this study, four types of time series vibration data collected in the time domain from motors are predicted using long short-term memory (LSTM) and gated recurrent unit (GRU), and the accuracy and time efficiency of the predicted and actual vibration waveforms are compared and examined. According to the simulation results, the coefficient of determination, R2, and the root mean square error values obtained from the actual and predicted vibrations by the LSTM and GRU are similar. Furthermore, both the LSTM and GRU show excellent performance in forecasting future motor vibration, but GRU can predict future vibration about 22.79% faster than LSTM.

Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

Smart cultural tourism is the development trend of the future tourism industry. Virtual reality is an important tool to realize smart tourism. The reality of virtual reality mainly comes from human-computer interaction, which is closely related to human action recognition technology. Therefore, the research takes human action recognition as the research direction, uses a self-organizing mapping network (SOM) neural network to extract the key frame of action video, combines it with multi-feature vector method to recognize human action, and compares the recognition rate and user satisfaction of different recognition methods. The results show that the recognition rate of multi-feature voting human action recognition algorithm based on SOM neural network is 93.68% on UT-Kinect action, 59.06% on MSRDailyActivity3D, and the overall action recognition time is only 3.59 s. Within six months, the total profit of human-computer interactive virtual reality tourism project with SOM neural network multi-eigenvector as the core algorithm reached 422,000 yuan, and 88% of users expressed satisfaction after use. It shows that the proposed method has a good recognition rate and can give users effective feedback in time. It is hoped that this research has a certain reference value in promoting the development of human motion recognition technology.

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.