Astrometry via Close Approach Events: Applications to Main-Belt Asteroid (702) Alauda

The release of Gaia catalog is revolutionary to the astronomy of solar system objects. After some effects such as atmospheric refraction and CCD geometric distortion have been taken into account, the astrometric precision for ground-based telescopes can reach the level of tens of milli-arcseconds. If an object approaches a reference star in a small relative angular distance (less than 100 arcseconds), which is called close approach event in this work, the relative positional precision between the object and reference star will be further improved since the systematic effects of atmospheric turbulence and local telescope optics can be reduced. To obtain the precise position of a main-belt asteroid in an close approach event, a second-order angular velocity model with time is supposed in the sky plane. By fitting the relationship between the relative angular distance and observed time, we can derive the time of maximum approximation and calculate the corresponding position of the asteroid. In practice, 5 nights' CCD observations including 15 close approach events of main-belt asteroid (702) Alauda are taken for testing by the 1m telescope at Yunnan Observatory, China. Compared with conventional solutions, our results show that the positional precision significantly improves, which reaches better than 4 milli-arcseconds, and 1 milli-arcsecond in the best case when referenced for JPL ephemeris in both right ascension and declination.Comment: 11 pages, 22 figure

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period

An Astrometric Approach to Measuring the Color of an Object

The color of a star is a critical feature to reflect its physical property such as the temperature. The color index is usually obtained via absolute photometry, which is demanding for weather conditions and instruments. In this work, we present an astrometric method to measure the catalog-matched color index of an object based on the effect of differential color refraction (DCR). Specifically, we can observe an object using only one filter or alternately using two different filters. Through the difference of the DCR effect compared with reference stars, the catalog-matched color index of an object can be conveniently derived. Hence, we can perform DCR calibration and obtain its accurate and precise positions even if observed with Null filter during a large range of zenith distances, by which the limiting magnitude and observational efficiency of the telescope can be significantly improved. This method takes advantage of the DCR effect and builds a link between astrometry and photometry. In practice, we measure the color indices and positions of Himalia (the sixth satellite of Jupiter) using 857 CCD frames over 8 nights by two telescopes. Totally, the mean color index BP-RP (Gaia photometric system) of Himalia is 0.750 \pm 0.004 magnitude. Through the rotational phased color index analysis, we find two places with their color indices exceeding the mean \pm 3 \sigma.Comment: 10 pages, 5 figures, 4 table

Contract-Based Interference Coordination in Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access networks (H-CRANs) are potential solutions for improving both spectral and energy efficiencies by embedding cloud computing into heterogeneous networks. The interference among remote radio heads (RRHs) can be suppressed with centralized cooperative processing in the base band unit (BBU) pool, while the inter-tier interference between RRHs and macro base stations (MBSs) is still challenging in H-CRANs. In this paper, to mitigate this inter-tier interference, a contract-based interference coordination framework is proposed, in which three scheduling schemes are involved, and the downlink transmission interval is divided into three phases accordingly. The core idea of the proposed framework is that the BBU pool covering all RRHs is selected as the principal that would offer a contract to the MBS, and the MBS as the agent decides whether to accept the contract or not according to an individual rational constraint. An optimal contract design that maximizes the rate-based utility is derived when perfect channel state information (CSI) is acquired at both principal and agent. Furthermore, contract optimization under the situation in which only partial CSI can be obtained from practical channel estimation is addressed as well. Monte Carlo simulations are provided to confirm the analysis, and simulation results show that the proposed framework can significantly increase the transmission data rates over baselines, thus demonstrating the effectiveness of the proposed contract-based solution

ACO-RR: Ant Colony Optimization Ridge Regression in Reuse of Smart City System

© 2019, Springer Nature Switzerland AG. With the rapid development of artificial intelligence, governments of different countries have been focusing on building smart cities. To build a smart city is a system construction process which not only requires a lot of human and material resources, but also takes a long period of time. Due to the lack of enough human and material resources, it is a key challenge for lots of small and medium-sized cities to develop the intelligent construction, compared with the large cities with abundant resources. Reusing the existing smart city system to assist the intelligent construction of the small and medium-sizes cities is a reasonable way to solve this challenge. Following this idea, we propose a model of Ant Colony Optimization Ridge Regression (ACO-RR), which is a smart city evaluation method based on the ridge regression. The model helps small and medium-sized cities to select and reuse the existing smart city systems according to their personalized characteristics from different successful stories. Furthermore, the proposed model tackles the limitation of ridge parameters’ selection affecting the stability and generalization ability, because the parameters of the traditional ridge regression is manually random selected. To evaluate our model performance, we conduct experiments on real-world smart city data set. The experimental results demonstrate that our model outperforms the baseline methods, such as support vector machine and neural network