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关键词： Visualization   Feature extraction   Task analysis   Safety   Head   Informatics   Bicycles   Attention mechanism   big data analytics   digital twin (DT)   graph learning   scene graph generation (SSG)   visual scene recognition  

摘要： Digital twin is the innovation backbone of the smart manufacturing by delivering virtual representation of the real world. Aiming at constructing virtual representations of visual scenes, scene graph generation is a digital twin task that not only models objects but also infers their relationships. Existing works usually learn coarse global context when predicting relationships leading to excessive redundant information being considered. In this article, we first classify objects into different subgroups according to the degree of correlations with several hub objects. Then, we propose a multihub driven attention network (MHDANet) based on deep learning that drives the information to pass within the subgroups and forces objects to attend more to related objects. Consequently, MHDANet learns compact relation-aware features of visual scenes and predicts accurate and diverse relationships. Experimental results show that MHDANet achieves superb performance on scene graph generation on real-world datasets and especially alleviates the imbalance of predicted relationship categories.

关键词： Crete   Mediterranean   Landsat   Big Data   time series   long-term   land degradation   phenology   reproducibility   replicability   semi-arid   vegetation decomposition  

摘要： Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth's land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based

ISBN: (纸本)9787576309201

关键词： Cloud computing   Big data   Real-time processing   Load balancing   Distributed system   Scheduling   Resource allocation   Optimization  

摘要： Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. This paper proposes a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. Our proposed algorithm provides solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements.

关键词： Students  

摘要： As an educational concept based on learning output, OBE (Outcome-Based Education) is student-centered and emphasizes students' personal progress and learning achievement. Based on BD (big data) analysis, this paper proposes a new talent training mode for the electronic information science and technology specialty. This model employs BD analysis technology to examine the correlation index between social demand and talent cultivation, based on the employment situation of students in the country in previous years. The teaching reform was carried out under the OBE concept, and a new training scheme was formed. Training objectives, graduation requirements, curriculum system, and continuous improvement mechanism were all determined. This paper proposes an algorithm for determining the degree of similarity between the knowledge required by the organization and the knowledge held by its employees. The person with the highest similarity is identified as the training candidate by the algorithm, and the training candidate is then trained according to the knowledge that the organization requires. Talent training in the field of electronic information science and technology has yielded positive results.

关键词： Image enhancement  

摘要： In order to improve the effect of copying and recreation of painting works, this paper combines mobile digital multimedia big data technology to improve the image coding algorithm, identify the characteristics of existing works, apply the algorithm to the detailed analysis of painting works, and construct the main functional structure modules of the system. Moreover, this paper combines the existing hardware equipment to construct the painting works' recreation system and obtains the image processing module. After the system is constructed, the effect of copying and recreating painting works is analyzed through the mobile digital multimedia big data analysis technology. Finally, this paper constructs the system of this paper through simulation methods and uses experiments to calculate the feature recognition effect and copy effect of the painting works of the system. Through experimental analysis, it can be known that the copying and recreation system of painting works based on mobile digital multimedia big data analysis proposed in this paper can help painters effectively improve the effect of recreation.

关键词： 乡村振兴   产学研基地   振兴之路   产业结构   供电公司   电力公司   大数据分析   能源消费  

摘要： 2021年以来,国网山东省电力公司东营供电公司(以下简称"东营供电公司")结合东营当地资源、产业结构和能源消费特点,积极对接当地政府"稻乡蟹缘"乡村振兴示范项目,综合运用智慧物联、大数据分析以及现代传感器技术,在惠泽黄河口大闸蟹产学研基地打造了乡村电气化惠农富民项目示范点,铺就了电气化赋能智慧渔业的乡村振兴之路。

关键词： 大数据分析   电力负荷   数据预测   用电负荷   中长期负荷预测   统计数据   处理精度   数值预报  

摘要： 当前我国电力负荷统计数据量不断快速增长,大量负荷数据的处理过程中的效率不高,耗费的时间相对太长,以及电力长期负荷数据预测处理精度较低,得到的数据结果不能很好地进行计算和处理存储。基于此,本章着重探究了利用大数据分析在电力市场中长期用电负荷数值预报的问题,并想方设法予以保证进行科学合理的电力负荷运行预测研究。

关键词： 课程思政   教学质量评价   大数据分析   人工智能  

摘要： 本文以新时代军事教育方针为引领,遵循"数智融合"的评价思路,在对课程思政内涵剖析的基础上,建立了课程思政评价指标分类,提出了数据的采集与预处理建议、智能评价策略和动态反馈机制,并结合"信息交换与网络"课程思政建设、实施过程进行了应用.