Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

Design of Low-Power Data Logger of Deep Sea for Long-Term Field Observation

This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.

Analysis of Gestational Diabetes Mellitus (GDM) and Its Impact on Maternal and Fetal Health: A Comprehensive Dataset Study Using Data Analytic Tool Power BI

Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corroborate and expand upon the findings of this research. This underscores the ongoing need for continued investigation into GDM to refine our understanding and improve clinical management strategies.

数据可视化Power BI在职业院校学生管理中的应用

职业院校学生管理是一个重要而复杂的任务,需要学校管理人员的精心组织和有效执行。随着信息技术的发展和校园信息化建设的需求,部分职业院校的学生管理服务系统已初步建成。BI数据可视化可以有效地利用大量的系统数据,为院校管理者提高决策和治理水平提供依据。本文通过对BI数据可视化过程和结构进行分析,比较Power BI与传统型BI的差异,通过对Power BI数据可视化在职业院校学生管理中的应用分析和实践案例,验证数据可视化PowerBI在学生管理中的重要作用。

Attribute-based keyword search encryption for power data protection

To protect the privacy of power data,we usually encrypt data before outsourcing it to the cloud servers.However,it is challenging to search over the encrypted data.In addition,we need to ensure that only authorized users can retrieve the power data.The attribute-based searchable encryption is an advanced technology to solve these problems.However,many existing schemes do not support large universe,expressive access policies,and hidden access policies.In this paper,we propose an attributebased keyword search encryption scheme for power data protection.Firstly,our proposed scheme can support encrypted data retrieval and achieve fine-grained access control.Only authorized users whose attributes satisfy the access policies can search and decrypt the encrypted data.Secondly,to satisfy the requirement in the power grid environment,the proposed scheme can support large attribute universe and hidden access policies.The access policy in this scheme does not leak private information about users.Thirdly,the security analysis and performance analysis indicate that our scheme is efficient and practical.Furthermore,the comparisons with other schemes demonstrate the advantages of our proposed scheme.

基于Power BI的螺栓拧紧数据智能管理系统

为保证整车装配的安全和质量,需要采用大量带监控拧紧工具完成整车零部件的装配工作,并借助拧紧数据完成拧紧工作的日常管理。然而,该过程产生的数据量庞大,传统方式采用人工统计分析拧紧数据,存在统计效率低下、数据处理工作繁琐、可视化效果不佳等问题。为了解决该问题,文章基于智能分析软件Power BI对拧紧数据进行建模处理,并搭建螺栓拧紧数据智能管理系统,实现拧紧数据的自动化管理和分析。该系统包含了海量拧紧数据的自动加载、自动统计和自动生成可视化报表等功能,业务人员可以随时访问系统查询报表,大幅提升了现场拧紧管理的工作效率。

Power BI在我院药品数据可视化中的应用

目的加强医院药品精细化管理,提升药师对药品的数据分析能力,实现数据分析自动化及查询功能,为集采药品、麻精药品的动态监管提供数据支撑。方法使用数据可视化工具Power BI搭建服务,制作报表,能够查询药品使用情况和对比情况,实现各科室药品使用情况的可视化管理。结果通过该软件,药师可以精确查询任意时间维度下药品的使用数量、金额等情况,实现数据分析自动化。结论借助数据管理工具能够帮助药师提升数据分析能力和工作效率,有助于加强医院药品的精细化管控。

基于Power BI的DRGs数据分析方法

目的:探讨利用Power BI工具对DRGs数据进行有效分析,揭示潜在的数据特征和规律。方法:运用Power BI工具对2017年―2021年DRGs数据进行数据连接、数据清洗和处理等操作,提取和计算相关DRGs评价指标,最后通过可视化展示的方式对DRGs数据进行分析和呈现,从不同维度和角度深入研究DRGs数据特征和规律。结果:通过对2017年―2021年DRGs数据处理和可视化展示,能直观了解出院患者疾病构成、医疗资源利用、临床质量评估和医疗成本效益等总体情况,为提高医疗服务质量和资源利用效率等提供可行的解决方案。结论:基于Power BI的DRGs数据分析方法能够有效地帮助医院进行医疗费用控制和资源配置,并提高医疗服务的质量和效率。此方法为优化DRGs数据管理和医疗资源配置提供了重要的参考和借鉴价值。

面向幂律图的动态图存储结构Power-PCSR

图数据在现实生活中广泛存在,且不断发生变化。传统高效的静态图存储方式——压缩行/列(Compressed Sparse Row/Column,CSR/CSR)存储方式在更新图数据时需要大量的数据迁移,不适用于动态图数据。而能够高效更新图数据的邻接表(Adjacency List,AL)存储方式往往带有大量的指针,导致其图数据读取和分析效率低。Packed Compressed Sparse Row(PCSR)是一种基于CSR的动态图存储结构。该结构在存储边数据时并不是采用连续无空隙数组,而是采用留有空槽的压缩存储阵列(Packed Memory Arrays,PMA)结构,便于边数据的插入。因此,PCSR支持高效图更新和图分析。但是,PCSR在存储幂律图时,其性能容易受大度数顶点的影响。为此,基于PCSR提出一种支持可高效更新和分析动态幂律图的图存储结构Power-PCSR。该结构将幂律图中度数较大的顶点单独存储在一个独立的PMA中,其他所有小度数顶点与PCSR一样存储在原PMA中。小度顶点变化导致的数据迁移不会触及大度数顶点,从而大大减少了数据迁移数量;同样,大度数顶点更新导致的数据迁移只限制在每个大度数顶点的PMA内部,不会涉及小度数顶点和其他大度数顶点的数据迁移。实验显示,Power-PCSR在分析图数据时与PCSR具有相似的性能,而在更新图数据时比PCSR快2倍。

Estimating the Input Power of a Power Plant Using the Efficiency of the Inverter

The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation systems and proposes an approach based on the efficiency formula widely documented in the literature. In the absence of input data, this method makes it possible to estimate the plant’s input power using data extracted from the site, in particular that provided by the Ministry of the Environment. The importance of this study lies in the need to accurately determine the input power in order to assess the overall performance of the energy system.

Power BI Desktop在医院DIP精细化管理中的应用

目的利用Power BI Desktop对医院DIP分组明细进行多维度对比分析及数据可视化,寻找医院DIP亏损原因。方法提取某三甲肿瘤专科医院2022年度部分出院结算DIP入组明细数据,通过数据导入、数据类型转换、建立数据关系、数据建模、数据可视化等方法,建立全院、科室、病种等多个维度的可视化动态分析报表。结果Power BI Desktop能有效提升DIP精细化管理水平,提升工作效率,精准分析亏损原因,指导科室进行改进,有效减少医院DIP医保资金亏损。结论Power BI Desktop具有成本低廉,定制化、可视化、自动化程度高,用户界面友好等特点,值得在各医院尤其是信息化程度低、专科运营人员不足、资金预算不足的医院DIP精细化管理中进行推广。

Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics

This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].

基于Power BI的三级公立医院绩效考核病案首页指标数据分析与可视化展示

目的在三级公立医院绩效考核背景下,探寻一种基于Power BI的数据统计分析及可视化展示方法,提高医院统计人员数据统计及分析效率。通过可视化助力日常指标监测,推动医疗管理的效率和质量提升,辅助管理决策。方法提取某三甲医院2022年1月1日至2023年6月30日的病案首页数据,根据三级公立医院绩效考核病案首页指标数据统计口径,建立基于Power BI的三级公立医院绩效考核病案首页指标数据查询系统,进行数据分析及可视化展示。结果基于Power BI的三级公立医院绩效考核病案首页指标数据查询系统可快速查询多维度、多场景的指标数据,具有实用性及易操作性。结论Power BI可作为医院统计工作人员进行数据分析及可视化展示的工具,值得推广使用。医院统计工作人员可结合本研究将Power BI运用于实际工作中,提高工作效率,助力日常统计工作。

基于Power BI的数据分析在三级公立医院绩效考核管理中的研究与应用

介绍了在国家三级公立医院绩效考核的大背景下,针对考核指标从院级到临床科室的分解,以及科室快速迭代的数据分析需求,探寻了一种基于Power BI工具的数据分析方式,该方式对科室的管理决策起到辅助作用,并在国考指标的PDCA闭环管理上取得成效。实践证明,Power BI是医院数据分析工作的优选工具,在医院数据分析业务中具有广泛应用前景。

Distributed Storage System for Electric Power Data Based on HBase

Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status,debugging,and error records every single day.To guarantee the safety and sustainability of electric power systems,massive electric power data need to be processed and analyzed quickly to make real-time decisions.Traditional solutions typically use relational databases to manage electric power data.However,relational databases cannot efficiently process and analyze massive electric power data when the data size increases significantly.In this paper,we show how electric power data can be managed by using HBase,a distributed database maintained by Apache.Our system consists of clients,HBase database,status monitors,data migration modules,and data fragmentation modules.We evaluate the performance of our system through a series of experiments.We also show how HBase’s parameters can be tuned to improve the efficiency of our system.

Power Splitting Based SWIPT in Network-Coded Two-Way Networks with Data Rate Fairness:An Information-Theoretic Perspective

This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting(EH) relay. We present a power splitting(PS)-based two-way relaying(PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signalto-noise ratio(SNR), the performance loss is relatively small. Another observation is that, in relatively high SNR regime, PS-TWR outperforms time switching-based two-way relaying(TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.

Power Line Monitoring Data Transmission Using Wireless Sensor Network

The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.

Compliance verification and probabilistic analysis of state-wide power quality monitoring data

This paper introduces the implementation and data analysis associated with a state-wide power quality monitoring and analysis system in China. Corporation specifications on power quality monitors as well as on communication protocols are formulated for data transmission. Big data platform and related technologies are utilized for data storage and computation. Compliance verification analysis and a power quality performance assessment are conducted, and a visualization tool for result presentation is finally presented.

Economical Optimization of Grid Power Factor Using Predictive Data

We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an on-load tapchanger(OLTC) and transmission lines. The system power factor(PF) regulation and reactive power dispatching are indispensable to improve power quality. Our control method uses predictive weather and load data to decide engaging or tripping the shunt capacitor, or reactive power injection by the photovoltaic-inverter system, ultimately to keep the system PF in a good range. From the perspective of economics, the economical model is considered as a decision maker in our predictive data control method.Capacitor-only control strategy is a common photovoltaic(PV)regulation method, which is treated as a baseline case. Simulations with GridLAB-D on profiled loads and residential loads have been carried out. The comparison results with baseline control strategy and our predictive data control method show the appreciable economical benefit of our method.