Search

Because of their wide detection range and rich functions, autonomous underwater vehicles (AUVs) are widely used for observing the marine environment, for exploring natural resources, for security and defense purposes, and in many other fields of interest. Compared with a single AUV, a multi-AUV formation can better perform various tasks and adapt to complex underwater environments. With changes in the mission or environment, a change in the UAV formation may also be required. In the last decade, much progress has been made in the transformation of multi-AUV formations. In this paper, we aim to analyze the core concepts of multi-AUV formation transformation; summarize the effects of the AUV model, underwater environment, and communication between AUVs within formations on formation transformation; and elaborate on basic theories and implementation approaches for multi-AUV formation transformation. Moreover, this overview includes a bibliometric analysis of the related literature from multiple perspectives. Finally, some challenging issues and future research directions for multi-AUV formation transformation are highlighted.

This article studies the data collection task planning for a fixed-wing unmanned aerial vehicle (UAV) in forest fire monitoring. Multiple wireless-based detection nodes (DNs) are distributed in high-risk areas of the forest to monitor the surrounding environment. The task of UAV is to circularly fly to them and collect the environmental data. Because of the kinematic constraints of UAV and the effective communication range between UAV and DN, this problem can be generally regarded as a Dubins traveling salesman problem with neighborhood (DTSPN). A bi-level hybridization-based metaheuristic algorithm (BLHMA) is proposed for solving this problem. At the first level, differential evolution (DE) optimizes the continuous-valued communication positions and UAV headings by the population-based search. For the asymmetric traveling salesman problem (ATSP) corresponding to the combination of the positions and headings generated by DE, a constructive heuristic based on self-organized multi-agent competition (SOMAC) is proposed to determine the discrete collection sequence. By competitive iterations in such a cooperative way in DE, a high-quality data collection tour can be generated. At the second level, a local search based on multistage approximate gradient is proposed to further refine the positions and headings, which accelerates the convergence of the BLHMA. Referring to a real-world scene of forest fire monitoring, the simulation experiments are designed, and comparative results show that BLHMA can find significantly shorter data collection tours in most cases over three state-of-the-art algorithms. The proposed UAV data collection planning algorithm is conducive to the efficient execution of the forest fire monitoring data collection mission and the energy saving of UAV.

We investigate the optical and structural properties of GaN and InGaN nanowires (NWs) fabricated by metal-assisted electroless etching in a hydrofluoric acid (HF) solution. The emission spectra of GaN and InGaN NWs exhibit a red shift compared to the as-grown samples resulting from an increase in the surface-to-volume ratio and stress relaxation in these nanostructures. The carrier lifetimes of GaN and InGaN NWs were measured. In addition, density functional theory (DFT) investigations were carried out on GaN and InGaN NWs using the generalized gradient approximation (GGA), including the Hubbard U parameter. The presence of compressive stress in the NWs was confirmed by the DFT calculations, which indicated that it induces a change in the lattice parameter along the c-direction. Formation energy calculations showed that In is a much more stable dopant in the GaN NWs compared to the native point defects, such as Ga and N vacancies. Moreover, electronic structure analysis revealed that the complex defects formed by the presence of In along with vacancy defects shifts the valence band maximum, thus changing the conducting properties of the NWs.

Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a decision maker with the guidance of his/her preferences which are provided progressively. During the process, the decision maker can adjust his/her preferences and explore only interested regions of the search space. In recent decades, IMO has gradually become a common interest of two distinct communities, namely, the multiple criteria decision making (MCDM) and the evolutionary multiobjective optimization (EMO). The IMO methods developed by the MCDM community usually use the mathematical programming methodology to search for a single preferred Pareto optimal solution, while those which are rooted in EMO often employ evolutionary algorithms to generate a representative set of solutions in the decision maker's preferred region. This paper aims to give a review of IMO research from both MCDM and EMO perspectives. Taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine (i.e., optimization algorithm), a taxonomy is established to identify important IMO factors and differentiate various IMO methods. According to the taxonomy, state-of-the-art IMO methods are categorized and reviewed and the design ideas behind them are summarized. A collection of important issues, e.g., the burdens, cognitive biases and preference inconsistency of decision makers, and the performance measures and metrics for evaluating IMO methods, are highlighted and discussed. Several promising directions worthy of future research are also presented.

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

The electric discharge machining (EDM) interelectrode gap directly determines the discharge state, which affects the machining efficiency, workpiece surface quality, and the tool wear rate. The measurement of the real-time varying interelectrode gap during machining is extremely difficult, and so obtaining an accurate mathematical model of the dynamic interelectrode gap will make EDM gap control possible. Based on p-type single-crystal silicon EDM, a flat-plate capacitance model is introduced to analyze the time-domain characteristics of the inter-electrode voltage in the breakdown delay phase. Further, we theoretically established a physical model of the interelectrode spacing d and the charging time constant τ of the plate capacitor. The least-squares fitting of the experimental data was used to determine the model coefficients, and in combination with the actual machining process, a minimum-variance self-tuning controller was designed to control the interelectrode gap in real time. The experimental verification results show that the established physical model can correctly predict the interelectrode gap in the actual machining process. The minimum-variance self-tuning controller improves machining stability, and eliminates the occurrence of the short-circuit state.

Traumatic optic neuropathy refers to optic nerve (ON) injury by trauma, including explosion and traffic accident. Retinal ganglion cell (RGC) death is the critical pathological cause of irreversible visual impairment and blindness in ON injury. We previously investigated the patterns of 11 modes of cell death in mouse retina post‐ON injury. Here we aimed to identify additional signalling pathways regulating RGC survival in rodents post‐ON injury. RNA sequencing analysis identified the upregulation of inflammation and cellular senescence‐related genes in retina post‐ON injury, which were confirmed by immunoblotting and immunofluorescence analyses. Increased expression of senescence‐associated β‐galactosidase (SA‐βgal) in RGCs and activation of microglia were also found. Transforming growth factor‐β receptor type II inhibitor (LY2109761) treatment suppressed p15Ink4b and p21Cip1 protein and SA‐βgal expression and promoted RGC survival post‐ON injury with decreasing the expression of cell death markers in retina. Consistently, senolytics (dasatinib and quercetin) treatments can promote RGC survival and alleviate the reduction of ganglion cell complex thickness and pattern electroretinography activity post‐ON injury with reducing SA‐βgal, p15Ink4b, p21Cip1, microglial activation and cell death marker expression. In summary, this study revealed the activation of cellular senescence in rodent retina post‐ON injury and contribute to RGC survival regulation. Targeting cellular senescence can promote RGC survival after ON injury, suggesting a potential treatment strategy for traumatic optic neuropathy. [ABSTRACT FROM AUTHOR]