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AAOISE, the Antarctic Astronomical Observations Intelligent Support Equipment, is an autonomous control equipment serving for energy support and environment thermal preservation, which is used for astronomical science observations in the Antarctic “Dome A”. It was deployed to “Dome A” and had an unattended run until now. The AAOISE stressed on the ways to adapt to adverse circumstances of “Dome A” and to have as little influence on the environment as possible. Its shape and structure are fully qualified for transportation and thermal insulation demands. The power generation and control systems are designed to provide continuous power and heat. Its communication system can support high-reliability data transmission and communications. It offers a possibility for developing “Dome A” scientific activities and remote monitoring of the running situation of the science instruments. This paper presents a detailed description of the power generation, power control, thermal management, instrument interface, and communications systems for AAOISE.

Graph-based methods have been widely adopted to detect salient objects in images. However, there are two limitations of these methods. First, only one kind of query is employed for saliency propagation on the graph. Second, these methods only represent pairwise relations between vertices and thus give an incomplete representation of the relationships between image regions. In this paper, we propose a foreground- and background-queries-based hypergraph optimization framework for salient region detection. In this framework, both foreground queries and background queries are explicitly exploited to uniformly highlight the salient foreground and suppress the non-salient background. Furthermore, to include both the pairwise and the higher-order relations among two or more vertices, a probabilistic hypergraph is constructed based on local spatial correlation, global spatial correlation, and color correlation to represent the relations among image regions from different views. Extensive experimental results demonstrate the effectiveness of the proposed framework.

In recent years, many graph-based methods have been introduced to detect saliency. These methods represent image regions and their similarity as vertices and edges in a graph. However, since they only represent pairwise relations between vertices, they give an incomplete representation of the relationships between regions. In this work, we propose a hypergraph based optimization framework for salient object detection to include not only the pairwise but also the higher-order relations among two or more vertices. In this framework, besides the relations among vertices, both the foreground and the background queries are explicitly exploited to uniformly highlight the salient objects and suppress the background. Furthermore, a probabilistic hypergraph is constructed based on local spatial correlation, global spatial correlation, and color correlation to represent the relations among vertices from different views. Extensive experiments demonstrate the effectiveness of the proposed method.

The development of instrument technology enables total sky imagers with temporal and spatial high-resolution sky observations, which makes cloud classification based on ground-based sky images possible. However, it is difficult to classify clouds accurately just from a single image according to the International Cloud Classification System. Here, a method of cloud classification is proposed viewed the attenuation rate of solar direct normal radiation (DNI) as the judgment standard, based on a set of mainly statistical features describing the color and the texture of a sky image. To calculate the attenuation rate of DNI,a clear-sky model is firstly developed. And then, a neural network classifier is constructed to separate clouds into two classes: cloud with effect to DNI and cloud with no effect to DNI. The optimal architecture of the neural network is determined by means of two groups of training experiments. With the data from NREL Solar Radiation Research Laboratory, a test is carried out to evaluate the performance of the classifier. Results show that the proposed method can yield a success rate of about 91.67%. The method of cloud classification based on DNI is able to be applied in the photovoltaic system and may improve the accuracy of the short solar power forecasting.