基于大数据挖掘的电网企业风险防控体系研究与应用

随着电力体制改革的深入推进,售电侧产业格局正在发生根本性的改变,电网企业面临竞争与冲击已是不争的事实。为保证各级电网企业在新形式下的良好发展,提高市场竞争力,降低风险,迫切需要深化和完善目前的风险防控体系。本文结合电网企业应用实际,提出以大数据挖掘为技术手段,发掘各项指标间潜在的关联关系,据此建立风险防控体系。通过指标异动预判其关联指标的变化趋势,发现潜在风险、定位管理漏洞,使风险在异动发生前被干预处理,从而为企业辅助决策提供有效手段。文章使用相关分析、回归分析等数据挖掘分析技术对方案的正确性进行了验证,目前,此方案已在相关电网企业获得试点,并取得显著成效。此外,本文在挖掘指标关联关系、发现潜在风险、定位管理漏洞等方面进行了全新探索,得到一些有益的启示,可供相关电网企业参考与借鉴。     

孕妇尖锐湿疣50例临床分析

目的:针对分析孕妇尖锐湿疣的临床表现,探讨安全、有效的治疗方法以及新生儿妊娠结局的影响.方法:对我院50例孕妇尖锐湿疣患者与35例未妊娠尖锐湿疣患者进行回顾性分析.结果:妊娠期CA较非妊娠CA病程短(P＜0.01),易发展成巨块型疣体(P＜0.01);二氧化碳(CO2)激光治疗未出现流产、早产、死胎.结论:妊娠期与非妊娠CA的临床表现不完全相似;终止妊娠可以提高治愈率;剖腹分娩对婴儿比较安全.     

孕妇健康管理服务系统研究

随着21世纪科技和医学的进步,人们不再只满足于"生下孩子",而是更关注孕妇的生理和心理状态,更注重从饮食、运动、生活习惯、心理等方面对孕妇进行健康管理,使孕妇以健康的身体迎来健康的宝宝。这对减少孕妇妊娠合并症及并发症的发生、降低剖宫产率,并最终减少不良分娩、降低医疗支出具有重要意义。孕妇健康管理是一项服务内容,是从孕妇妊娠开始即伴随整个围生期的一套完整流程,以服务设计介入,提供一系列系统化、延续性的护理服务,是解决现阶段孕妇健康管理与传统医疗服务冲突的重要方法。     

妊娠母猪饲粮的粗纤维营养

妊娠母猪对粗纤维的消化能力好于生长猪 ,并且妊娠期间的采食量与饲粮能量浓度有很大关系 ,因此适于饲喂高纤维的低能饲粮。研究表明 ,妊娠至分娩以及断奶期间 ,向母猪饲粮中添加粗纤维 ,可明显增加仔猪头数及其成活率。妊娠期间饲喂粗纤维还能改善泌乳期母猪采食量 ,但同时降低了妊娠期母猪增重和初生仔猪重 ,减少妊娠期母猪的一些典型行为如啃栏等。向母猪饲粮中添加过量粗纤维的影响因素很多 ,但最重要的两个是 ,中性洗涤纤维的进食量和粗纤维的来源。因纤维性饲料原料的物理特性及其粪便产量大 ,而限制了其在饲粮中的应用。在妊娠期使用…     

基于电力市场环境下的电网企业风险预警体系的管理创新与应用实践

随着电力体制改革的深入推进,未来发电、输配电、售电产业格局将发生根本改变,对电网企业的监管方式也将转变。电网企业必须以更低的成本、更高的效率和更优质的服务来提升企业竞争力,这样才能在改革的浪潮中站稳脚步。利用大数据挖掘技术探寻基础数据价值,打破专业壁垒,促进部门联动,促使各项指标可控、能控、在控,在异动发生前干预,将事后监测向事前预警有效转变,将为电网企业科学管理及决策提供有力支撑。通过利用大数据挖掘技术发掘各指标间潜在的关联关系,据此建立风险预警网络,一旦发现指标异动,其关联的其他指标自动提前预警,达到牵一发而动全身的效果。同时,建立横向协同机制,打破专业壁垒,各部门及时有效应对,最终实现提升公司运营效益和效率的目的。     

一种潜在特征同步学习和偏好引导的推荐方法

根据用户的历史评分数据为用户提供推荐的商品列表，是目前推荐系统研究的主流.研究者发现，随着用户参与度的不断提高，将反映用户偏好的评论文本与评分数据结合，可以进一步提高推荐的质量.提出了基于潜在特征同步学习和偏好引导的商品推荐方法，将评论文本的主题与用户的"打分偏好"进行关联，同步学习用户评论文本的潜在主题、评分矩阵的用户潜在因子和商品潜在因子，并将潜在主题作为用户个人偏好引导来约束推荐方法对商品的预测打分.该方法对推荐质量的优化主要体现在两个方面：一是在评论文本的潜在主题和评分数据的两种潜在因子之间建立映射关系，同步求解主题模型和矩阵分解模型；二是将从评论文本中学习得到的潜在主题作为用户对商品的个性偏好引入到矩阵分解中，进一步优化推荐方法.在来自Amazon网站的28组真实数据集上进行实验，以均方误差为评价指标，与已有的模型进行了对比分析.实验结果表明，该方法有效减少了推荐误差，与已有的TopicMF方法相比，均方误差在数据子集上最大减少了3.32%，平均减少了0.92%.     

一种基于标签传播的数据分块算法

数据分块有助于降低大规模数据中实体分辨的计算复杂度,但现有算法存在效能和效率难以平衡的问题。为此,在标签传播的基础上设计数据分块算法,以实现两者的平衡。根据记录间相同词项的数量估计记录相似度,通过标签传播算法发现潜在相似重复记录,降低时间复杂度。在通用测试数据中的实验结果表明,该算法能有效提高F-Measure值,减少运行时间,实现大规模数据中的数据分块。     

基于EA-LDA算法的领域知识图谱潜在关系扩展

在知识互联的大数据环境下,初步构建的领域知识图谱可展示该领域知识的结构化信息,但实体之间隐含的潜在关系并未在图谱中得到充分表达。为解决领域知识图谱实体关系丰富和扩展问题,提出一种基于实体间关联规则分析与主题分析的关系发现方法。应用与领域实体相关的数据,通过实体间关联规则分析与实体相关数据集间主题分布相似度分析获取领域实体间潜在关系,将新发现的关系融合到初步构建的知识图谱中,实现领域知识图谱的潜在关系扩展。实验结果表明,该方法能够发现部门实体间的共性,挖掘出隐藏在领域实体间的关系,可有效地应用于领域实体间关系发现,丰富领域知识图谱。     

软件开发活动数据的数据质量问题

问题追踪系统和版本控制系统等软件开发支持工具已被广泛应用于开源和商业软件的开发中,产生了大量的数据,即软件开发活动数据.软件开发活动数据被广泛应用于科学研究和开发实践,为智能化开发提供支持.然而数据质量对相关的研究和实践有重大影响,却还没有得到足够的重视.为了能够更好地警示数据使用者潜在的数据质量问题,通过文献调研和访谈,并基于自有经验对数据进行分析,总结出了9种数据质量问题,覆盖了数据产生、数据收集和数据使用这3个不同的阶段.进一步地,提出了相应的方法以帮助发现和解决数据问题.发现问题是指加强对数据上下文的理解和通过统计分析及数据可视化发现潜在的数据质量问题,解决问题是指利用冗余数据或者挖掘用户行为模式进行修正.     

脐动脉血流信号非线性临床研究

针对孕妇脐动脉血流信号确定性与随机性相结合的复杂非线性特征,从一个新的角度研究正常与病态血流信号间的差异。首先以孕妇真实的临床诊断数据为研究对象,分析了超声脐动脉血流信号的搏动指数、阻力指数及S/D值,并分析了其健康状况,在此基础上引入分形维数对非线性脐动脉血流信号进行对比研究。通过分析孕妇脐动脉血流分形维数,分析异常脐血流信号的分形特征,结果表明:分形维数与孕周具有相关性,分形维可作为定量检测孕妇及胎儿健康状况的重要指标。     

Research on lower body shape of late pregnant women in Shanghai area of China

Anthropometric data, especially the data of lower body of late-pregnant women, has become an immediate need for ergonomic design of maternity products, such as clothing, related motherhood supplies, maternity support products, and so on. So 55 late pregnant women in Shanghai area of China were randomly selected for this lower body shape research. By anthropometric measurement method 18 dimensions were measured to characterize lower body shape of late pregnant women. Mean values, standard deviations (SD), coefficients of variation, and percentiles for each variable were estimated. It was found that the morphology of hip and abdomen mostly influenced the lower body shape. Then according to the characters of hip and abdomen, the late pregnant women's lower body shapes were divided into two types. Moreover this paper also analyzed the change trend of lower body shape along late pregnancy months (7 to 9th months). As a result of this research, the late pregnant women's lower body shapes were described from different aspects which were thought to provide a guide for the maternity products design.Relevance to industryThe anthropometric data is essential to design reasonable and professional maternity products for late pregnant women, such as maternity support products, maternity clothing and so on. Especially this study can benefit the maternity garment industry. Moreover utilization of the anthropometric data, product designers would be able to outfit to a wider range of target users.     

Exploring the risk factors of preterm birth using data mining

Preterm birth is the leading cause of perinatal morbidity and mortality, but a precise mechanism is still unknown. Hence, the goal of this study is to explore the risk factors of preterm using data mining with neural network and decision tree C5.0. The original medical data were collected from a prospective pregnancy cohort by a professional research group in National Taiwan University. Using the nest case-control study design, a total of 910 mother–child dyads were recruited from 14,551 in the original data. Thousands of variables are examined in this data including basic characteristics, medical history, environment, and occupation factors of parents, and variables related to infants. The results indicate that multiple birth, hemorrhage during pregnancy, age, disease, previous preterm history, body weight before pregnancy and height of pregnant women, and paternal life style risk factors related to drinking and smoking are the important risk factors of preterm birth. Hence, the findings of our study will be useful for parents, medical staff, and public health workers in attempting to detect high risk pregnant women and provide intervention early to reduce and prevent preterm birth.     

Comfort evaluation of maternity support garments in a wear trial

This study aims to evaluate the wear comfort of eight commercially available maternity support garments. The thermophysiological, sensory/tactile and movement comfort were assessed in a wear trial using a 19-item questionnaire. Fourteen pregnant Chinese women aged 32.3 +/- 4.2 years were recruited from a local obstetric clinic. The results show that the tested garments generally provided greater sensory comfort than thermophysiological comfort. The thermophysiological comfort was mainly influenced by the fibre contents and breathability. Significant linear relationships were found between material appearance and hand feel (r = 0.86, p < 0.001), and between non-itchiness and no red mark (r = 0.78, p < 0.001). Movement comfort was influenced by the garment type and style features. Overall, the soft, good-fit, cotton/elastane maternity brief was perceived as the best product. The findings of comfort needs in pregnant women and the effects of various garment attributes would be helpful for the development of maternity support garment design criteria that are required to satisfy critical ergonomic needs. Low back pain during pregnancy is a common and significant health problem. A maternity support garment is regarded as a convenient and safe device to stabilise the lumbar spine so as to relieve pain. However, patient compliance is likely to be affected by discomfort and inconvenience. The results of this study provide guidance for the optimal design of maternity support clothing.     

Comfort evaluation of maternity support garments in a wear trial

This study aims to evaluate the wear comfort of eight commercially available maternity support garments. The thermophysiological, sensory/tactile and movement comfort were assessed in a wear trial using a 19-item questionnaire. Fourteen pregnant Chinese women aged 32.3 ± 4.2 years were recruited from a local obstetric clinic. The results show that the tested garments generally provided greater sensory comfort than thermophysiological comfort. The thermophysiological comfort was mainly influenced by the fibre contents and breathability. Significant linear relationships were found between material appearance and hand feel (r = 0.86, p < 0.001), and between non-itchiness and no red mark (r = 0.78, p < 0.001). Movement comfort was influenced by the garment type and style features. Overall, the soft, good-fit, cotton/elastane maternity brief was perceived as the best product. The findings of comfort needs in pregnant women and the effects of various garment attributes would be helpful for the development of maternity support garment design criteria that are required to satisfy critical ergonomic needs. Low back pain during pregnancy is a common and significant health problem. A maternity support garment is regarded as a convenient and safe device to stabilise the lumbar spine so as to relieve pain. However, patient compliance is likely to be affected by discomfort and inconvenience. The results of this study provide guidance for the optimal design of maternity support clothing.     

Automatic detection of dimension errors in spreadsheets

We present a reasoning system for inferring dimension information in spreadsheets. This system can be used to check the consistency of spreadsheet formulas and thus is able to detect errors in spreadsheets.Our approach is based on three static analysis components. First, the spatial structure of the spreadsheet is analyzed to infer a labeling relationship among cells. Second, cells that are used as labels are lexically analyzed and mapped to potential dimensions. Finally, dimension information is propagated through spreadsheet formulas. An important aspect of the rule system defining dimension inference is that it works bi-directionally, that is, not only “downstream” from referenced arguments to the current cell, but also “upstream” in the reverse direction. This flexibility makes the system robust and turns out to be particularly useful in cases when the initial dimension information that can be inferred from headers is incomplete or ambiguous.We have implemented a prototype system as an add-in to Excel. In an evaluation of this implementation we were able to detect dimension errors in almost 50% of the investigated spreadsheets, which shows (i) that the system works reliably in practice and (ii) that dimension information can be well exploited to uncover errors in spreadsheets.     

Medical image detection and azithromycin in the treatment of pelvic inflammation based on intelligent hospital system

The pelvic infection and people genital tract inflammation, infertility, chronic pain, and reasons for detecting ectopic pregnancy. Diagnostic and management challenges, from different signs and symptoms and a variety of microbial etiology is not fully delineated largely responsible. Because of the potential for serious things of the network, it is recommended for the diagnosis and treatment of low barriers to entry. One of pelvic inflammatory infection of pregnant woman of childbearing age is not the most testing an important public pelvic problem remains. This is the main long-term internet of things, such as tubal factor infertility, ectopic pregnancy and chronic pelvic pain. Also, acute care and its complications lead to significant pelvic care costs. These long-term preventions of the internet of things depend on the microbial cause of acute knowledge of strategies based on treatment. All aware that it is a polymicrobic acute infection. Organisms, Chlamydia trachomatis and Neisseria gonorrhoeae sexually transmit presence, in many cases, including the endogenous vaginal and cervical flora often associated with microorganisms. It contains anaerobic and facultative bacteria similar to those associated with bacterial most importantly mycoplasma has also recently been involved in acute reasons. Therefore, a therapeutic regimen for acute should provide a wide spectral range that is effective against these microorganisms.     

A novel fog-computing-assisted architecture of E-healthcare system for pregnant women

Recently, there is a tremendous rise and adoption of smart wearable devices in smart healthcare applications. Moreover, the advancement in sensors and communication technology empowers to detect and analyse physiological data of an individual from the wearable device. At present, the smart wearable device based on internet of things is assisting the pregnancy woman to continuously monitor their health status for avoiding the severity. The physiological data analysis of wearable device is processed with the assistance of fog computing due to limited computational and energy capability in the wearable device. Additionally, fog computing overcomes the excess latency that is created by cloud computing during physiological data analysis. In this article, a smart health monitoring IoT and fog-assisted framework are proposed for obtaining and processing the temperature, blood pressure, ECG, and pulse oximeter parameters of the pregnant woman. Based on real time series data, the rule-based algorithm logged in the wearable device with fog computing to analyse the critical health conditions of pregnant women. The proposed wearable device is validated and tested on 80 pregnant women in real time, and wearable device is delivering the 98.75% accuracy in providing health recommendations.

     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

基于神经网络自适应滤波器的故障检测与诊断

在稳态过程的故障检测和诊断中 ,有大量反映故障状态的数据 ,在用测量方法来获取这些数据的情况下 ,异常数据能被快速、有效的检测出来就显得非常重要。用自适应滤波器无疑是一个可行的方法 ,但采用这种方法进行故障检测和诊断时 ,有计算速度慢 ,难以跟踪输入信号变化的缺点。据此 ,本文提出了用神经网络自适应滤波器来完成故障检测和诊断的方法 ,它具有速度快的特点。如能用硬件完成 ,并调整好参数 ,检测速度是极短的 (2× 10 -10 s) ,能很好地完成故障检测和诊断任务。