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All-sky images derived from ground-based imaging equipment have become an important means of recognizing and quantifying cloud information. Accurate cloud detection is a prerequisite for obtaining important cloud information from an all-sky image. Existing cloud segmentation algorithms can achieve high accuracy. However, for different scenes, such as completely cloudy with obscured sun and partly cloudy with unobscured sun, the use of specific algorithms can further improve segmentation. In this study, a hybrid cloud detection algorithm based on intelligent scene recognition (HCD-ISR) is proposed. It uses suitable cloud segmentation algorithms for images in different scenes recognized by ISR, so as to utilize the various algorithms to their full potential. First, we developed an ISR method to automatically classify the all-sky images into three scenes. In scene A, the sky is completely clear; in scene B, the sky is partly cloudy with unobscured sun; and in scene C, the sun is completely obscured by clouds. The experimental results show that the ISR method can correctly identify 93% of the images. The most suitable cloud detection algorithm was selected for each scene based on the relevant features of the images in that scene. A fixed thresholding (FT) method was used for the images in scene C. For the most complicated scene, that is, scene B, the clear-sky background difference (CSBD) method was used to identify cloud pixels based on a clear-sky library (CSL). The images in the CSL were automatically filtered by ISR. Compared to FT, adaptive thresholding (AT), and CSBD methods, the proposed HCD-ISR method has the highest accuracy (95.62%). The quantitative evaluation and visualization results show that the proposed HCD-ISR algorithm makes full use of the advantages of different cloud detection methods, and is more flexible and robust. [ABSTRACT FROM AUTHOR]

The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems. [ABSTRACT FROM AUTHOR]

A hierarchical optimization method is presented for an assessment of the potential maximum energy output of the Three Gorges Project and Gezhouba cascade hydropower stations in China. The optimization incorporates a detailed description of daily load dispatching among different types of hydropower units. The optimization method is divided into three levels to render feasible the computation of the global optimization problem. In levels 1 and 2, a genetic algorithm is applied to optimize discharge and water head distribution between the upstream and downstream reservoirs. Level 1 selects the water levels at the beginning of each month in a year. Level 2 selects the water levels at the beginning of each day in a month. Level 3 solves a linear programming problem for allocation of water among the heterogeneous types of hydropower generation. Hydrological data of the years 2004 and 2005 are used to perform an optimized power generation process within the operating rules. The maximal annual energy production could be approximately 61 and 87 MWh higher than the actual recorded energy production in 2004 and 2005, respectively. [ABSTRACT FROM AUTHOR]

According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients' health. However, in clinical practice, identifying malignant degree requires biopsy and pathological examination which is time costly. Furthermore, in many areas, due to the severe shortage of dermatologists, it's inconvenient for patients to go to hospital for examination. Therefore, an easy to access screening method of malignant skin tumors is needed urgently. Firstly, we spend 5 years to build a dataset which includes 4,500 images of 10 kinds of skin tumors. All instances are verified pathologically thus trustworthy; Secondly, we label each instance to be either low-risk, high-risk or dangerous in which Junctional nevus, Intradermal nevus, Dermatofibroma, Lipoma and Seborrheic keratosis are low-risk, Basal cell carcinoma, Bowen's disease and Actinic keratosis are high-risk, Squamous cell carcinoma and Malignant melanoma are dangerous; Thirdly, we apply the Xception architecture to build the risk degree classifier. The area under the curve (AUC) for three risk degrees reach 0.959, 0.919 and 0.947 respectively. To further evaluate the validity of the proposed risk degree classifier, we conduct a competition with 20 professional dermatologists. The results showed the proposed classifier outperforms dermatologists. Our system is helpful to patients in preliminary screening. It can identify the patients who are at risk and alert them to go to hospital for further examination. [ABSTRACT FROM AUTHOR]

• Flow rising process stimulating spawning is identified by proposed Algorithm (FIA). • Fish-oriented indicators are defined to describe the rising processes features quantitatively. • Applied on the upper reaches of the Yellow River. • 4˜6 times of flow rising occur in spawning season; each lasts for about 11 days. • Daily average flow in rising process should be about 30% higher than daily mean. Various studies have proven that the flow rising processes play a significant role in the fish spawning stimulus. Instead of seizing the flow rise as an integrated process consisting of a flow rising edge and falling edge, the current publication focus on the daily flow. It is necessary to identify the effective flow rising process to learn the flow feature and guide the water resources management. In this study, a Fluctuation Identification Algorithm (FIA) is proposed based on the identification of flow rising and falling edges with only three parameters in total. On the strength of those identified flow rising processes, a group of fish-oriented indicators are defined to describe the features of the rising, including the number of the rising processes in the whole spawning season, the average duration of each rising process, the daily average flow in the flow rising processes, the average flow rising ratio, the average flow increment, and the average growth rate of the flow rising processes in the spawning season. The application on the upper reaches of the Yellow river over recent 10 years verifies the validity of the proposed Fluctuation Identification Algorithm. The statistics of those indicators indicates the regularities of the flow rising processes in the fish spawning seasons in different hydrological years, which helps the ecological protection in the water resources development. [ABSTRACT FROM AUTHOR]

Node importance evaluation is one of the important network analyses in complex network and data mining. However most of the existing methods are complex and slow for large networks. In this paper, a practical method based on resistive network for fast node importance evaluation was proposed. Node importance was evaluated based on its influence on the average power dissipation in a resistance network, a node was important because it reduced the average energy dissipation of the network. The time complexity of the algorithm is O(n³) in the worst situation, and is O(n) in the distributed computing environment. Experimental results testify that the algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]