Barley leaves mediated biosynthesis of Au nanomaterials as a potential contrast agent for computed tomography imaging SCIENCE CHINA Technological Sciences Volumetric imaging of flame refractive index, density, and temperature using background-oriented Schlieren tomography SCIENCE CHINA Technological Sciences Coronary artery plaque imaging: Comparison of black-blood MRI and 64-multidetector computed tomography Chronic Diseases and Translational Medicine 2, 159 (2016); Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering SCIENCE CHINA Information Sciences 63, 132101 (2020); Production prediction for fracture-vug carbonate reservoirs using electric imaging logging data Petroleum Exploration and Development 45, 369 (2018);. RESEARCH PAPER. SCIENCE CHINA Information Sciences

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The hydrothermal scheduling is complex issue of nonlinear optimization consisting of several constraints that plays a critical role in the operations of power system. In order to meet the safe operation of hydropower stations, how to reasonably dispatch them to achieve the best comprehensive benefits is one of the main problems in the hydropower industry. Artificial bee colony algorithm has the advantages of simple structure and strong robustness. It is widely used in many engineering fields. However, the algorithm itself still has many shortcomings. Based on the current research, an improved artificial colony algorithm based on standard artificial bee colony algorithm is proposed, and the performance of the algorithm is verified in three benchmark functions and three cec213 test functions. Compared with many well-known improved algorithms, it is proved that the improved procedure has greatly enhances the final solution accuracy and convergence outcome. The experimental outcomes observed by improved artificial algorithm are compared with adaptable artificial bee colony procedure and chaotic artificial bee colony procedure and with other existing works in the literature. It is observed from the experimentation that the proposed algorithm performs better in comparison with established optimization algorithms.Povzetek: Raziskava obravnava optimizacijo razporeditve vodne energije s pomočjo izboljšanega algoritma umetne čebelje kolonije, kar izboljša natančnost in konvergenčne rezultate.

C-means clustering algorithms have proven effective for image segmentation, but are limited by the following aspects: 1) the determination of a priori number of clusters. If the number of clusters can be incorrectly determined, a good-quality segmented image cannot be assured; 2) the poor real-time performances due to great time-consuming, and 3) the poor typicality of each cluster represented by the clustering prototype. In this paper, a grid-based C-means algorithm is applied to image segmentation, whose advantages over the existing C-means algorithm have demonstrated in some typical datasets. The convergence domain of the grid-based C-means algorithm has further been analyzed as well. Experiments show that the gridbased C-means algorithm outperforms the original C-means algorithm in some typical image segmentation applications.

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