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To encourage distributed energy storage systems (ESS) in automatic generation control (AGC), energy storage aggregator (ESA) which aggregates a large number of disordered, autonomously operating, and weakly connected distributed ESS is applied in current power system control area. In this paper, an AGC strategy for ESA based on consensus algorithm is proposed to enable distributed ESS responding to AGC signal. In detail, the strategy picks up the high frequency component from AGC signals for ESA by low-pass filter at the beginning. Furthermore, on the basis of the mathematical model of distributed ESS in frequency regulation, the consensus algorithm is used to allocate the control responsibility to each ESS by considering SoC balance and plug - play function. Also, the consensus algorithm can save computing time and communication cost with strong anti-interference ability. Finally, the promising results of the proposed AGC strategy are simulated by Matlab/Simulink. Those results verify the control strategy can improve the frequency performance of the power system control area, and allocate the control responsibility of ESS accurately.

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Finally, some new trends of DL-based machine health monitoring methods are discussed., Comment: 14 pages, 9 figures, submitted to IEEE Transactions on Neural Networks and Learning Systems

Somatic meiosis-like reduction was observed in some cells of the embryogenic callus of Arabidopsis thaliana. Two types were identified. One type was somatic chromosome reductional grouping, in wich the chromesomes in a cell were separated direetly at either prophase or metaphase. Chromosome reductional grouping happened more frequently in polyploid cells, and the morphology of the chromosomes did not show the role of the spindle fibers. The other type was somatic meiosis which was analogous to the process of gametogenesis, characterized by the pairing and synapsis of homologous chromosomes. The roles of somatic meiosis-like reduction in somatic embryogenesis and somaclonal variations are discussed

Precision agriculture needs a mass of data, therefore data availability is a vital obstacle factor for precision agriculture technology widely use. The methods of acquisition data for precision agriculture change from field survey to sensor acquisition gradually. Remote Sensing plays an important role in the large-scale, real-time, multi-spectral data acquisition. But there are still some problems when Remote Sensing data are applied in the actual farm production and management, which result in that precision agriculture's advantages are limited, so finding the solution of data acquisition for precision agriculture based on remote sensing becomes very necessary. In this paper, the NPP, NDVI, LAI etc are retrieved from remote sensing data, the field investigation data are also obtained. We set up a decision-making system based on CNCS (central neural control system), which is composed of several module such as data updating, data editing, decision-making supporting. The system is made for the farmers to decide cultivation strategies on when, where and how much irrigation, fertilizer and pesticide should be applied on each plot.

There are three characteristics for ecosystem services, zonal characteristics, complexity and conventional civilization, which affect ecological valuation directly. Considering regional environment and anthropogenic influence, grassland's ecosystem services in Gansu Province are concluded 11 types, including gas regulation, climate regulation, disturbance regulation, wind erosion control and sand fixture, water resource provision, soil erosion control, waste treatment, gene resistance, food production, raw materials, recreation and culture. Landsat TM data are collected for manual photointerpretation. The grassland type distribution map is obtained. In this paper, we use stockbreeding income as benchmark, the other ecosystem services values which do not enter market to exchange are decided by experts questionary in this field. The relative weights can help to calculate the non-market ecological service value. The results' spatial distribution are displayed and analyzed by using Geographic Information Systems software. The results indicate: the total Gansu province grassland ecosystem value reaches 283.67times108 yuan (RMB) in 1980s. The grassland in the low zone along Hexi corridor and Longnan have the highest ecosystem service value, the grassland in Gannan take second place, and the western of Qingyang and counties around Lanzhou take third place, the other places value in the province are low. The Gansu Province is a severe erosion region in arid and semiarid area, therefore wind erosion control and sand fixture, soil erosion control are the most valuable services.