Hierarchical Bayesian Calibration and On-line Updating Method for Influence Coefficient of Automatic Dynamic Balancing Machine

Measurement error of unbalance＇s vibration response plays a crucial role in calibration and on-line updating of influence coefficient（IC）. Focusing on the two problems that the moment estimator of data used in calibration process cannot fulfill the accuracy requirement under small sample and the disturbance of measurement error cannot be effectively suppressed in updating process, an IC calibration and on-line updating method based on hierarchical Bayesian method for automatic dynamic balancing machine was proposed. During calibration process, for the repeatedly-measured data obtained from experiments with different trial weights, according to the fact that measurement error of each sensor had the same statistical characteristics, the joint posterior distribution model for the true values of the vibration response under all trial weights and measurement error was established. During the updating process, information obtained from calibration was regarded as prior information, which was utilized to update the posterior distribution of IC combined with the real-time reference information to implement online updating. Moreover, Gibbs sampling method of Markov Chain Monte Carlo（MCMC） was adopted to obtain the maximum posterior estimation of parameters to be estimated. On the independent developed dynamic balancing testbed, prediction was carried out for multiple groups of data through the proposed method and the traditional method respectively, the result indicated that estimator of influence coefficient obtained through the proposed method had higher accuracy; the proposed updating method more effectively guaranteed the measurement accuracy during the whole producing process, and meantime more reasonably compromised between the sensitivity of IC change and suppression of randomness of vibration response.

STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

Finite element model updating for structural damage detection using transmissibility data

This paper presents a new finite element model updating method for estimating structural parameters and detecting structural damage location and severity based on the structural responses(output-only data).The method uses the sensitivity relation of transmissibility data through a least-squares algorithm and appropriate normalization of the extracted equations.The proposed transmissibility-based sensitivity equation produces a more significant number of equations than the sensitivity equations based on the frequency response function(FRF),which can estimate the structural parameters with higher accuracy.The abilities of the proposed method are assessed by using numerical data of a two-story two-bay frame model and a plate structure model.In evaluating different damage cases,the number,location,and stiffness reduction of the damaged elements and the severity of the simulated damage have been accurately identified.The reliability and stability of the presented method against measurement and modeling errors are examined using error-contaminated data.The parameter estimation results prove the method’s capabilities as an accurate model updating algorithm.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

One-step Eco-friendly Fabrication of Antibacterial Polyester Via On-line Amination Reaction by Melt Coextrusion

The work is dedicated to develop a one-step eco-friendly method to prepare antibacterial polyethylene terephthalate(PET).We report a one-step eco-friendly method to manufacture antibacterial PET via on-line amination reaction by melt coextrusion.Beside evenly mixing of poly(hexamethylene guanidine)(PHMG)and PET in the melt coextrusion procedure,the amination reaction also occurred between PHMG and PET under high temperature(230-270℃).The antibacterial ability of composite PET showed obvious PHMG concentration dependence,and antibacterial activity reached more than 99%when PHMG content was 2.5 wt%.Moreover,LIVE/DEAD fluorescence test further confirmed that the composite PET could kill bacteria quickly and efiectively(within 30 min);while negligible cytotoxicity was observed to HSF and HUVEC cells.Onestep eco-friendly fabrication of composite antibacterial PET was accomplished by on-line melt coextrusion.The composite antibacterial PET has potential use in multiple fields to combat with pathogenic including textiles,packaging materials,decoration materials and biomedical devices,etc.

Dynamic Update Scheme of Spectrum Information Based on Spectrum Opportunity Incentive in the Database-Assisted Dynamic Spectrum Management

To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

On-line血液透析滤过对移植肾功能延迟恢复的影响

目的 研究On -line血液透析滤过 (HDF)在移植肾功能延迟恢复患者中的应用。方法 我院2 0 0 2年 1月至 2 0 0 3年 7月肾移植术后发生移植肾功能延迟恢复患者 38例 ,2 9例采用Freseniuson -lineHDF透析机 ,F6 0透析器 ,透析滤过时间 3～ 4h ,血流量在 180～ 2 5 0ml/min ,置换液采取前稀释法 ,9例行HD治疗 ,采用GambroAK2 0 0透析机 ,F7透析器。结果 38例移植肾功能延迟恢复患者 ,2 9例用联机血液透析滤过 ,平均肾功能恢复时间为 ( 11.2± 5 .6 )天 ,治疗期间收缩压变化在 110～ 170mmHg,透析期间低血压发生率为 2 .6 % ,9例HD治疗者平均肾功能恢复时间为 ( 2 2 .3± 15 .7)天 ,透析期间低血压发生率为 13.2 %。结论 与HD比较 ,On -lineHDF对血压影响较小 ,可缩短移植肾功能恢复的时间。

平行机的实时到达on-line算法下界的改进

本文考虑了平行机实时到达的在线问题．模型中，工件是陆续到达的，工件的个数、到达时间是事先未知的，而且只有当工件到达，才知其加工时间，目标是使所有工件都加工完的时间达到最小．Ｃｈｅｎ与Ｖｅｓｔｊｅｎｓ（１９９６年）证明了该在线问题不存在性能比小于１．３４７３的ｏｎ－ｌｉｎｅ算法．本文将此界改进为（５－５）／２．

On-line血液透析滤过对维持性血液透析患者心率变异性的影响

目的探讨On-line血液透析滤过技术对维持性血液透析患者心率变异性(HRV)的影响。方法 50例维持性血液透析(MHD)患者随机分为常规血液透析治疗组(HD组)和On-line血液透析滤过治疗组(OL-HDF组),每组各25例。患者每周行3次、每次4 h的血液透析治疗或OL-HDF,治疗24个月。比较两组常规的实验室检查和24h动态心电图监测结果。结果治疗24个月后,OL-HDF组的β2-微球蛋白(β2-MG)由基线值30.1±7.2 mg/dl降至21.7±6.1 mg/dl(P<0.05),HD组的β2-MG则无明显下降。OL-HDF组HRV的时域性和频域性指标均值高于基线值(P<0.05),HD组的HRV指标则无显著性变化(P>0.05)。结论与普通血液透析比较,OL-HDF能更好地改善MHD患者的HRV,改善自主神经功能。

On-line置换液与生理盐水在血液透析滤过回血中的应用效果比较

[目的]探讨On-line置换液与生理盐水在血液透析滤过(HDF)病人回血中的应用效果,以期为临床护理工作提供理论依据。[方法]选取30例规律HDF病人为研究对象,采用自身对照方法,每例病人每周依次采用On-line置换液和生理盐水回血,比较回血过程中残余血量、静脉壶液体温度、护士的工作量及经济效益。[结果]采用On-line置换液回血法残余血量及回血后静脉壶温度分别为(1.89±0.91)mL,(33.36±0.15)℃,而采用生理盐水法为(2.88±0.23)mL、(27.13±0.14)℃,差异均有统计学意义(P<0.01);而透析器凝血状况比较差异无统计学意义(P=0.08);采用On-line置换液回血时护士工作量减少,回血液体成本降低。[结论]应用接近生理温度的On-line置换液回血能稀释血液、降低血液黏稠度,减少血路管残余血量,增加病人舒适感,提高工作效率。

On-lineHDF的临床观察及护理

目的 探讨应用On -lineHDF治疗慢性肾衰尿毒症患者的临床效果及护理对策。方法 回顾性分析 15例维持性HD患者应用On -lineHDF的治疗及护理过程。结果 15例患者经每 1～ 2周 1次的On -lineHDF治疗后 ,病情稳定 ,部分患者心功能、血压、食欲、体力及睡眠不同成度得到改善 ,在促红细胞生成素使用量不变的情况下 ,4例病人的血红蛋白有所增加。结论 On -lineHDF是当今最佳的肾脏替代疗法技术之一。

On-line HDF治疗维持性透析患者并发症的护理

对20例维持性透析并发难治性高血压、顽固性瘙痒、不安腿综合征等患者,采用每周2次血液透析+1次on-line HDF,加强观察和护理。患者血压得到良好的控制,皮肤瘙痒得到明显改善,不安腿症状有所好转,贫血、食欲减退、精神和神经症状得到改善,生活质量明显提高。

透析机自产回输液行血液透析滤过On-line HDF的临床研究

目的探讨On-lineHDF的安全性、病人耐受性及临床疗效。方法设置On-lineHDF治疗组(I组)和常规HD对照组(II组),观察治疗前、中、后病人状况,溶质变化和溶质下降率,包括Cr、BUN、β2-MG、PTH、KT/V。结果HDF治疗中、治疗后病人无发热,6例顽固性血压易于控制,5例在HD中易出现低血压症状好转,3例皮肤瘙痒严重者明显好转,HDF组治疗前后Cr,β2-MG,PTH显著下降;HD组治疗前后β2-MG、PTH相差不显著。治疗前及2月后溶质变化,两组Cr,β2-MG相差不显著;HDF组PTH前后有显著性差异;KT/V值比较,HDF为1.68±0.34,HD为1.21±0.23,两组有显著性差异(P<0.05)。结论On-lineHDF较传统HDF操作简单,自产回输液安全,病人有较好的耐受性和心血管稳定性,其对小分子、中分子物质的清除能力优于常规HD。

On-line血液透析滤过护理15例

目的 探讨应用On -line血液透析滤过治疗慢性肾衰尿毒症患者的临床效果及护理措施。方法 回顾性分析 1 5例维持性血液透析患者应用On -line血液透析滤过的治疗及护理过程。结果 1 5例患者经每 1～ 2周 1次的On -line血液透析滤过治疗后 ,病情稳定 ,部分患者心功能、血压、食欲、体力及睡眠不同程度得到改善 ,在促红细胞生成素使用量不变的情况下 ,4例病人的血红蛋白有所增加。结论 On -line血液透析滤过是当今最佳的肾脏替代疗法技术之一 。

Stylistic Analysis on English On-line Chat

Based on the fact that on-line chat has become the most developing language form in the information age, this article point out the stylistic features of on-line English chat. Though in written language form, such language is spoken language in nature, thus it is worthwhile to analyze this special phaenomenon in lexical and grammatical level.

On-line血液透析滤过的临床应用及护理

①目的观察On-line血液透析滤过治疗尿毒症的临床疗效及护理措施。②方法对25例维持性血液透析患者分别进行常规血液透析406次和On-linehHDF180次,同时作组内自身对照,分别观察其治疗并发症和不良反应发生率。③结果On-lineHDF的疗效优于HD,并发症和不良反应发生率低于HD。④结论On-lineHDF通过其对尿毒症大、中分子物质的有效清除和对酸中毒的有效纠正,以及具有稳定的血流动力学,提高了患者对该疗法的耐受性,是伴有HD并发症及不良反应尿毒症患者的良好选择。