Parameters estimation and application of generalized exponential distribution under grouped and right-censored data

Generalized exponential distribution is a class of important distribution in lifedata analysis,especially in some skewed lifedata.The Parameter estimation problem for generalized exponential distribution model with grouped and right-censored data is considered.The maximum likelihood estimators are obtained using the EM algorithm.Some simulations are carried out to illustrate that the proposed algorithm is effective for the model.Finally,a set of medicine data is analyzed by generalized exponential distribution.

STATISTICAL INFERENCE FOR A BIVARIATE EXPONENTIAL DISTRIBUTION BASED ON GROUPED DATA

Consider the bivariate exponential distribution due to Marshall and Olkin[2], whose survival function is F(x, g) = exp[-λ1x-λ2y-λ12 max(x, y)] (x 0,y 0)with unknown Parameters λ1 > 0, λ2 > 0 and λ12 0.Based on grouped data, a newestimator for λ1, λ2 and λ12 is derived and its asymptotic properties are discussed.Besides, some test procedures of equal marginals and independence are given. Asimulation result is given, too.

Novel Grouped Probability Data Association Algorithm for MIMO Detection

To bridge the performance gap between original probability data association （PDA） algorithm and the optimum maximum a posterior （MAP） algorithm for multi-input multi-output （MIMO） detection, a grouped PDA （GP-PDA） detection algorithm is proposed. The proposed GP-PDA method divides all the transmit antennas into groups, and then updates the symbol probabilities group by group using PDA computations. In each group, joint a posterior probability （APP） is computed to obtain the APP of a single symbol in this group, like the MAP algorithm. Such new algorithm combines the characters of MAP and PDA. MAP and original PDA algorithm can be regarded as a special case of the proposed GP-PDA. Simulations show that the proposed GP-PDA provides a performance and complexity trade, off between original PDA and MAP algorithm.

On the Power Performance of Test Statistics for the Generalized Rayleigh Interval Grouped Data

In this paper, the weighted Kolmogrov-Smirnov, Cramer von-Miss and the Anderson Darling test statistics are considered as goodness of fit tests for the generalized Rayleigh interval grouped data. An extensive simulation process is conducted to evaluate their controlling of type 1 error and their power functions. Generally, the weighted Kolmogrov-Smirnov test statistics show a relatively better performance than both, the Cramer von-Miss and the Anderson Darling test statistics. For large sample values, the Anderson Darling test statistics cannot control type 1 error but for relatively small sample values it indicates a better performance than the Cramer von-Miss test statistics. Best selection of the test statistics and highlights for future studies are also explored.

Location Data Fusion Based on Group Consensus

A new method of multi sensor location data fusion is proposed.The method is based on group consensus approach, which constructs group utility function (or its density) based on uncertainty of each sensor, and the location estimation is obtained based on the group utility function (or its density). The simulation results show that the method is better than those of mean and median estimation, and outlier and sensor failure can not affect the location estimation.

Development of a 1200 fine group nuclear data library for advanced nuclear systems

Accurate and reliable nuclear data libraries are essential for calculation and design of advanced nuclea systems. A 1200 fine group nuclear data library Hybrid Evaluated Nuclear Data Library/Fine Group(HENDL/FG with neutrons of up to 150 Me V has been developed to improve the accuracy of neutronics calculations and anal ysis. Corrections of Doppler, resonance self-shielding, and thermal upscatter effects were done for HENDL/FG Shielding and critical safety benchmarks were performed to test the accuracy and reliability of the library. The dis crepancy between calculated and measured nuclea parameters fell into a reasonable range.

Influence of Data Clouds Fusion From 3D RealTime Vision System on Robotic Group Dead Reckoning in Unknown Terrain

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain.The navigation scheme uses the benefits of the original real-time technical vision system(TVS)based on a dynamic triangulation principle.The method uses TVS output data with fuzzy logic rules processing for resolution stabilization.Based on previous researches,the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group.According to the comparative analysis of approximation methods,in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory.The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.

Energy-Efficient MTC Data Offloading in Wireless Networks Based on K-Means Grouping Technique

Machine-type communication (MTC) devices provide a broad range of data collection especially on the massive data generated environments such as urban, industrials and event-enabled areas. In dense deployments, the data collected at the closest locations between the MTC devices are spatially correlated. In this paper, we propose a k-means grouping technique to combine all MTC devices based on spatially correlated. The MTC devices collect the data on the event-based area and then transmit to the centralized aggregator for processing and computing. With the limitation of computational resources at the centralized aggregator, some grouped MTC devices data offloaded to the nearby base station collocated with the mobile edge-computing server. As a sensing capability adopted on MTC devices, we use a power exponential function model to compute a correlation coefficient existing between the MTC devices. Based on this framework, we compare the energy consumption when all data processed locally at centralized aggregator or offloaded at mobile edge computing server with optimal solution obtained by the brute force method. Then, the simulation results revealed that the proposed k-means grouping technique reduce the energy consumption at centralized aggregator while satisfying the required completion time.

Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.

Group Method of Data Handling for Modeling Magnetorheological Dampers

This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons that offers an effective solution to modeling non-linear systems. As such, we propose the use of GMDH to approximate the forward and inverse dynamic behaviors of MR dampers. We also introduce two enhanced GMDH-based solutions. Firstly, a two-tier architecture is proposed whereby an enhanced GMD model is generated by the aid of a feedback scheme. Secondly, stepwise regression is used as a feature selection method prior to GMDH modeling. The proposed enhancements to GMDH are found to offer improved prediction results in terms of reducing the root-mean-squared error by around 40%.

Data Anonymous Authentication for BIoMT with Proxy Group Signature

Along with the increase of wearable medical device,the privacy leakage problem in the process of transmission between these edge medical devices.The blockchain-enabled Internet of Medical Things(BIoMT)has been developed to reform traditional centralized medical system in recent years.This paper first introduces a data anonymous authentication model to protect user privacy and medical data in BIoMT.Then,a proxy group signature(PGS)scheme has been proposed based on lattice assumption.This scheme can well satisfy the anonymous authentication demand for the proposed model,and provide anti-quantum attack security for BIoMT in the future general quantum computer age.Moreover,the security analysis shows this PGS scheme is secure against the dynamical-almost-full anonymous and traceability.The efficiency comparison shows the proposed model and PGS scheme is more efficient and practical.

英国高校科研数据共享实践及其启示——以罗素大学集团为例

文章以英国罗素大学集团为研究对象,通过网站调研梳理其成员高校的科研数据共享实践情况、服务内容及效果,总结其服务特点,并探讨其对我国高校科研数据共享服务的启示,即制定数据共享服务政策,推动校内部门协同合作;提升服务理念,渐进式扩展服务内容;培育馆员数字素养,构建专业化服务团队;拓展服务方式,提高科研人员数据共享能力。

一种基于区块链的医疗数据隐私保护方法

为解决医疗数据的泄露或恶意被窜改以及医疗纠纷问题,提出一种基于区块链的医疗数据隐私保护方法。利用哈希算法加密患者的身份信息,治疗结果通过AES(advanced encryption standard)算法加密,而AES的密钥使用ECC(ellipse curve ctyptography)算法加密,所有的加密密钥、治疗结果、患者身份信息存储到联盟链上。采用群签名技术追溯签名医院,群管理员可以解密医疗数据,将其作为重要依据协助第三方解决医疗纠纷。效率分析表明,在安全性相同的情况下,该方法的加/解密效率比对比方案分别提高了14%和46%,同时分析了群签名各类算法的时间开销。通过与同类方法对比,该方法既可实现患者身份、医疗数据的分类隐私保护,又可保证交易存储开销是合理的,在医疗数据隐私保护领域具有一定的应用价值。

基于生态安全格局的云南世居民族所在地生态韧性评价研究

云南世居民族所在地是基于传统生态智慧世代延续,以自然系统与社会系统相互嵌套为特点的自然–文化遗产,而现代性和社会经济的快速发展使得世居民族所在地不断被蚕食和消解.研究基于韧性理念,利用多源数据,从稳定性、适宜性、冗余度、多样性4个层面构建云南世居民族所在地生态安全格局和生态韧性耦合框架,对藏、彝、傣族所在地2000—2020年生态韧性进行评价研究.结果表明,近20 a来,世居民族所在地总体生态韧性水平为中等,呈现逐年降低的趋势.高水平韧性区多分布于林地,存在连续但细碎化的分布特征.低水平生态韧性多分布于道路缓冲区2 km以内,路网的社会扰动内涵很大程度上影响生态韧性强度.20 a来平均生态韧性指数傣族所在地(0.6420)>彝族所在地(0.5602)>藏族所在地(0.5344).影响各世居民族所在地生态韧性的原因存在差异,地质灾害防护格局和水生环境安全格局是影响生态韧性的主要因素.

基于社交媒体数据的应急决策大群体群智知识挖掘及其价值测度方法研究

本文针对突发事件下的社交媒体数据体量庞大,但能够辅助应急决策的大群体群智知识分布无序且价值难以测度的问题,提出了一种应急决策大群体群智知识挖掘及其价值测度方法。首先,基于社交媒体博文数据,依据特定应急决策知识框架,采用TF-IDF(term frequency-inverse document trequency)法和改进后的词汇链算法构建了应急决策大群体群智知识挖掘方法;然后,提出了博文热度计算方法,在此基础上根据知识的语义重要性计算博文包含的群智知识总价值,并使用Shapley函数对群智知识价值进行分配;最后,以“新冠疫情”事件为案例对方法进行应用,并通过对比分析说明了方法的有效性和优越性。

“多元基础、分路径培养”的物流大数据应用型人才培养创新与实践

该文从人才培养模式创新、产教融合提升人才培养质量视角,对大数据本科专业应用型人才培养的多元学科专业基础和培养路径做出分析,进而以物流大数据专业群应用型人才培养为例,通过实施文理文工融合改革思路构建物流大数据复合知识体系,建设“多专业+多课程+多层次+多要求”的大数据实践实训平台、多学科专业融合互促的泛在化教学体系和深化产教研融合,形成多元基础分路径培养的人才培养思路并应用实践。通过物流大数据人才培养改革创新与实践,2021—2023年文理文工融合人才培养模式获得认可,教学成果突出,促进学科专业资源整合,形成“资源-名师-成果”互促上升的发展路径,人才培养质量显著提升,社会认可度提高。

某集团化医院数据集成平台的安全建设

互联互通、大数据人工、智能、云计算、互联网医院、移动物联网等新技术在医疗行业的广泛应用,有效提高了各项诊疗业务的方便性、快捷性。同时,各类医疗业务系统数量不断增加,各系统间的数据交互越来越频繁且复杂,数据安全性要求不断提高。该研究以某集团化医院数据集成平台的安全建设情况为例,从数据集成平台的安全性出发,分析平台的运行安全、数据安全、访问安全方面存在的问题,并根据以上问题从应用层面、网络层面、主机层面、制度建设层面完善防护措施,通过实践应用并跟踪,有效降低了平台运行的安全风险。

基于分组遗传算法的数据中心虚拟机节能映射

近年来,随着人们对云计算业务需求持续增长,数据中心能耗日益增加,由此不仅增加了运营成本,巨大的碳排放对生态环境也产生严重的影响,数据中心节能已成为当前亟须解决的重要难题。对云数据中心的虚拟机放置(Virtual Machine Placement,VMP)进行优化能有效地提高资源利用率,同时,VMP也是减少数据中心能耗的重要技术之一;针对数据中心的能耗感知VMP问题,提出一种基于分组遗传算法的节能算法EEGGA(Energy-Efficient Grouping Genetic Algorithm),算法将节能VMP问题视为装箱问题(Bin Packing Problem,BPP),并应用基于分组编码的遗传算法对其进行求解,通过减少活动物理主机的数量(装箱数量)以实现降低数据中心能耗的目标;在算法迭代过程的交叉和变异等阶段,设计了多种启发优化策略提升子代染色体的适应度,从而提高算法的节能性能和加快迭代收敛的速度;通过仿真实验,在收敛速度和求解性能等方面将提出的算法与传统的节能遗传算法进行对比,实验结果表明:提出的算法能够有效地减少数据中心的能耗,在节能性能和求解收敛速度方面均优于其他算法。

“双碳”背景下钢铁新产线选址的环境结合度评价——以中国宝武为例

为准确测度“双碳”(即碳达峰、碳中和的合称)背景下具体工业门类选址与环境体系的结合发展实效,通过研究钢铁新产线的选址取向及环境关切转变,提出新产线选址的环境结合度评价框架。通过数据包络分析(Data Envelopment Analysis,DEA)模型计算环境-经济效率,通过动态综合评价模型揭示时间序列上效率的变化特征,以构建一套钢铁新产线选址的环境结合度评价方法,进而对中国宝武钢铁集团部分生产基地开展实证分析。评价结果为上海宝山基地的环境结合度最高。研究表明:环境结合度是“双碳”背景下钢铁新产线向既有基地选址时主要的环境关切,而环境结合度的评价框架和评价方法可有效表征连续时间序列上既有基地与所在城市环境体系的互促共进态势,对大型钢企未来选址新产线具有一定指导作用。