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Combining actual conditions, power demand forecasting is affected by various uncertain factors such as meteorological factors, economic factors, and diversity of forecasting models, which increase the complexity of forecasting. In response to this problem, taking into account that different time step states will have different effects on the output, the attention mechanism is introduced into the method proposed in this paper, which improves the deep learning model. Improved models of convolutional neural networks (CNN) and long short-term memory (LSTM) that combine the attention mechanism are proposed respectively. Finally, according to the verification results of actual examples, it is proved that the proposed method can obtain a smaller error and the prediction performance are better compared with other models.

Chlorophyll-a (Chla) concentration is an important indicator to monitor eutrophication, which is a persistent problem that affects the ecological health of coastal water or shallow lakes. We have applied a Gaussian-like fuzzy function model for the estimation of Chla concentration in Hong Kong Coastal Waters, based on the spectral signature using the HJ-1A /1B CCD images and measured data. The method of this paper is as follows. Firstly, reflectance peak and fluorescence band were used to figure out the bands or bands combination which impact the Chla concentration significantly; and then calculated the value of all the pixels without measured data according to their similarity degree with the measured points; finally, the full Chla concentration maps in the study area were produced by GIS spatial interpolation. It is concluded in this paper that the method can retrieve the Chla concentration accurately and the result of changes detected coincides with the observed result extremely, what’s more, the maps generated are continuous and smooth which are quite different from traditional RS maps that can only accurate to pixel size.

The Alpha Magnetic Spectrometer (AMS) is a particle physics experiment installed and operating aboard the International Space Station (ISS) from May 2011 and expected to last through 2028 and beyond. The AMS offline software is used for data reconstruction, Monte-Carlo simulation and physics analysis. This paper presents how we manage the offline software, including the version control, automatic integration, automatic deployment, and the distribution.

The Alpha Magnetic Spectrometer (AMS-02) is a particle physics experiment installed on board the International Space Station (ISS). It has been operating since May 2011 and is expected to continue through 2028 or beyond. The AMS collaboration seeks to store, manage and present its research results as well as details about the detector and operations. An open-source content management framework is utilized as the platform to build the website. This platform allows management of a variety of information, such as institutes in the collaboration, physics results, publications, academic events, etc. This note discusses the motivation, strategy, infrastructure, web design techniques, custom modules, data sharing plan, and day-to-day operation. The resulting website is located at https://ams02.space.

Through the research on the RFID’s User tag memory and its coding mode, a double-label management mode based on the combination of RFID tag and barcode is proposed, which realizes the binding of information in the warehouse logistics process, the convenient transmission and traceability between the upstream and downstream departments. The interface between the scheduling system and the equipment control system is designed, and the scheduling algorithm based on priority ranking is put forward to realize material outbound in order in warehouse logistics system. This method is an extension of the warehouse and logistics information system, which can solve the problem of the accuracy of the material outbound in sequence to the downstream departments.

The Alpha Magnetic Spectrometer [1] (AMS) is a high energy physics experiment installed and operating on board of the International Space Station (ISS) from May 2011 and expected to last through year 2024 and beyond. More than 50 million of CPU hours has been delivered for AMS Monte Carlo simulations using NERSC and ALCF facilities in 2017. The details of porting of the AMS software to the 2nd Generation Intel Xeon Phi Knights Landing architecture are discussed, including the MPI emulation module to allow the AMS offline software to be run as multiple-node batch jobs. The performance of the AMS simulation software at NERSC Cori (KNL 7250), ALCF Theta (KNL 7230), and Mira (IBM BG/Q) farms is also discussed.

The Alpha Magnetic Spectrometer (AMS) is a high energy physics experiment installed and operating on board of the International Space Station (ISS) from May 2011 and expected to last through year 2024 and beyond. The Science Operation Centre (SOC) is in charge of the offline computing for the AMS experiment, including flight data production, Monte-Carlo simulation, data management, data backup, etc. This paper introduces the design and implementation for the new monitoring system of AMS SOC, from the monitoring data collection at the backend, to the visualisation at the frontend.