Rotating machinery plays a key role in mechanical equipment, and the fault diagnosis of rotating machinery is a popular research topic. To overcome the dependency on expert knowledge regarding conventional time-frequency analysis diagnosis methods, machine learning (ML) and artificial intelligence (AI)-based methods are commonly studied. Although these methods can achieve high-accuracy diagnosis results, they are based on a large number of training samples. A generative adversarial network (GAN) is an algorithm with the capability of generating realistic samples that are similar to the real samples, and it can be applied to solve fault diagnosis problems with insufficient training data, which is called the small sample size condition in this study. However, a single-GAN model cannot achieve a good diagnostic result. To achieve adaptive feature extraction and high diagnosis accuracy, this study proposes an intelligent fault diagnosis method for rotating machinery based on GANs under small sample size conditions. The effectiveness and performance of the proposed method are validated using rolling bearing and gearbox datasets. In these datasets, only 10% and 20% of the samples are selected as the training data. Samples associated with different health conditions and various working conditions are included in the datasets. Compared with those of other diagnosis methods, the high-accuracy and low-volatility diagnosis results indicate that the proposed method can stably distinguish fault modes under different working conditions in an adaptive way, even though few training samples are available.

Trajectory clustering has played an essential role in trajectory mining tasks. It serves in a wide range of real-life applications, including transportation, location-based services, behavioral study, and so on. To support trajectory clustering analytics, a plethora of trajectory clustering methods have been proposed, which mainly extend traditional clustering algorithms by using spatio-temporal characteristics of trajectories. However, existing traditional trajectory clustering approaches based on raw trajectory representation highly rely on hand-craft similarity metrics, and can not capture hidden spatial dependencies in trajectory data, which is inefficient and inflexible for clustering analysis. To this end, we propose an end-to-end deep trajectory clustering framework via self-training, termed as E ² DTC, inspired by the data-driven capabilities of deep neural networks. E ² DTC does not require any additional manual feature extraction operations, and can be easily adapted for trajectory clustering analytics on any trajectory dataset. Extensive experimental evaluations on three real-life datasets show that our framework E2DTC achieves superior accuracy and efficiency, compared with classical clustering methods (i.e., K-Medoids) and state-of-the-art neural-network based approaches (i.e., t2vec).

As combat missions become increasingly complex in both space and time, cross-regional joint operations (CRJO) is becoming an overwhelming trend in modern air warfare. How to allocate resources and missions prior to the operation becomes a central issue to improve the combat efficiency. In this paper, we focus on the cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) in a CRJO. A multi-objective optimization problem is presented with the aim of minimizing the makespan while maximizing the value expectation obtained. Moreover, it is not mandatory for each UAV to return exactly to the base which it takes off. Furthermore, in addition to the constraints commonly found in UAV mission assignment problems, the ammunition inventory at each base is also taken into account. To solve such a problem, we developed an improved genetic algorithm (IGA) with a novel chromosome encoding format. It can determine the number of attacks on a given target based on the expectations obtained, rather than being predetermined. Specifically, an efficient logic-based unlocking mechanism is designed for the crossover and mutation operations in the algorithm. Simulation results show that the developed IGA can efficiently solve the considered problem. Through numerical experimental comparisons, the algorithm proposed in this work is superior to other existing IGA-like algorithms in terms of computational efficiency.