Bayesian Estimation for the Extended t-process Regression Models with Independent Errors

The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with independent errors.A Monte Carlo EM method is built to estimate parameters involved in the model.Simulation studies and real examples show the proposed method performs well against outliers.

Regression Modeling of Individual-Patient Correlated Discrete Outcomes with Applications to Cancer Pain Ratings

Purpose: To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Methods: Three alternatives for modeling of outcome probabilities are considered. Multinomial probabilities are based on different intercepts and slopes for probabilities of different outcome values. Ordinal probabilities are based on different intercepts and the same slope for probabilities of different outcome values. Censored Poisson probabilities are based on the same intercept and slope for probabilities of different outcome values. Parameters are estimated with extended linear mixed modeling maximizing a likelihood-like function based on the multivariate normal density that accounts for within-patient correlation. Formulas are provided for gradient vectors and Hessian matrices for estimating model parameters. The likelihood-like function is also used to compute cross-validation scores for alternative models and to control an adaptive modeling process for identifying possibly nonlinear functional relationships in predictors for probabilities and dispersions. Example analyses are provided of daily pain ratings for a cancer patient over a period of 97 days. Results: The censored Poisson approach is preferable for modeling these data, and presumably other data sets of this kind, because it generates a competitive model with fewer parameters in less time than the other two approaches. The generated probabilities for this model are distinctly nonlinear in time while the dispersions are distinctly nonconstant over time, demonstrating the need for adaptive modeling of such data. The analyses also address the dependence of these daily pain ratings on time and the daily numbers of pain flares. Probabilities and dispersions change differently over time for different numbers of pain flares. Conclusions: Adaptive modeling of daily pain ratings for individual cancer patients is an effective way to identify nonlinear relationships in time as well as in other predictors such as the number of pain flares.

Test Selection on Extended Finite State Machines with Provable Guarantees

Building high confidence regression test suites to validate new system versions is a challenging problem. A modelbased approach to build a regression test suite from a given test suite is described. The generated test suite includes every test that will traverse a change performed to produce the new version, and consists of only such tests to reduce the testing costs. Finite state machines extended with typed variables (EFSMs) are used to model systems and system changes are mapped to EFSM transition changes adding/deleting/replacing EFSM transitions and states. Tests are a sequence of input and expected output messages with concrete parameter values over the supported data types. An invariant is formulated to characterize tests whose runtime behavior can be accurately predicted by analyzing their descriptions along with the model. Incremental procedures to efficiently evaluate the invariant and to select tests for regression are developed. Overlaps among the test descriptions are exploited to extend the approach to simultaneously select multiple tests to reduce the test selection costs. Effectiveness of the approach is demonstrated by applying it to several protocols, Web services, and model programs extracted from a popular testing benchmark. Our experimental results show that the proposed approach is economical for regression test selection in all these examples. For all these examples, the proposed approach is able to identify all tests exercising changes more efficiently than brute-force symbolic evaluation.

基于扩展决策场的机场落客区车辆—旅客行为建模分析

近年来,随着航空旅客运输的发展,机场陆侧交通资源愈发紧张。机场落客区作为衔接机场航站楼内部与外部交通之间的交互面,行人和车辆交互冲突严重,落客区运行压力大。本文以某国内机场为研究对象,基于实地调查的视频数据,建立基于Logistic回归模型的车辆让行模型;发布调查问卷,从效率、安全和舒适这3种属性收集该机场落客区场景下旅客做出的选择,最终建立落客区环境下的人车冲突仿真模型,利用仿真模型探究落客区内人车冲突情景。仿真结果表明:在多幅路结构的落客区内,旅客乱穿马路的选择与交通密度和过街次数有关,交通密度越大,旅客需要过街的次数越多,旅客乱穿马路的频率越低,在中幅路落客的旅客最高违规过街率为6.8%,在外幅路落客的旅客最高违规过街率为2.1%;当人行道和入站口相互间距扩大1倍,外幅路落客的旅客平均违规过街率提升1.6%,中幅路落客的旅客平均违规过街率提升0.9%;此外,通过限制旅客乱穿马路,可以有效减少非高峰时期车辆让行次数,提升落客区车辆通行效率。

Composite T-Process Regression Models

Process regression models,such as Gaussian process regression model(GPR),have been widely applied to analyze kinds of functional data.This paper introduces a composite of two T-process(CT),where the first one captures the smooth global trend and the second one models local details.TheCThas an advantage in the local variability compared to general T-process.Furthermore,a composite T-process regression(CTP)model is developed,based on the composite T-process.It inherits many nice properties as GPR,while it is more robust against outliers than GPR.Numerical studies including simulation and real data application show that CTP performs well in prediction.

适用于无人水下潜航器电池管理系统的SOC-SOH联合估计

为了提高无人水下潜航器(UUV)电池管理系统状态的估计精度,提出荷电状态-健康状态(SOC-SOH)联合估计方法.搭建测试台架,采用4组锂离子电池进行全寿命周期下的充放电测试,获取不同老化程度下的特性数据.经理论推导和实验分析设计四维表征因子,建立基于改进支持向量回归(SVR)的SOH估计模型.探究电池状态的耦合关系,建立基于扩展卡尔曼滤波(EKF)的SOC估计模型,采用遗忘因子递推最小二乘算法(RLS)更新模型参数,利用SOH对SOC估计结果进行修正.通过不同工况的实验进行验证,结果表明:四维表征因子和电池容量相关性好,SOH估计模型精度高,SOC估计模型精度在联合修正后得到提升.所提的联合估计方法具有较高的通用性和可靠性,可以作为有效的嵌入式电池管理系统状态估计算法.

Environmental factors influencing the distribution of endangered endemic species Hedysarum criniferum Boiss in arid and semi-arid rangelands,Iran

Understanding the factors influencing the distribution of plant species is crucial for enhancing the management of endangered ecosystems. This study investigated the response of Hedysarum criniferum Boiss, an endemic and endangered species to 25 environmental variables within its habitats with an area of 2.95×10^(5) km^(2) in arid and semi-arid rangelands of Iran. The purpose of this research is to identify the key environmental factors affecting the distribution and habitat preferences of H. criniferum for further conservation and restoration of the species. To predict the occurrence of H. criniferum and explore its relationship with environmental factors, we employed the best subset regression analysis, the hierarchical classification, and the extended Huisman-Olf-Fresco(eHOF) model. The results showed that four environmental variables, i.e., gravel content, pH, annual minimum temperature, and mean annual temperature showed significant correlations with the canopy cover of H. criniferum(P<0.05). The probability of H. criniferum occurrence increased with higher precipitation and elevation, while it decreased with higher mean annual temperature, annual minimum temperature, and gravel content. The species' response curves and their optimal values, as assessed by the eHOF model, indicated that the response to mean annual temperature, ranging from 12℃ to 16℃, was optimal at 13℃. The response to mean annual precipitation, within a range of 150–650 mm, was optimal at 650 mm. Elevation responses, spanning from 1546 to 2450 m, showed an optimum at 2450 m. Regarding soil characteristics, the response to gravel content, ranging from 13.0%–48.0%, demonstrated an optimal value at 20.0%. The pH levels, varying from 7.5 to 8.2, prompted a sine-shaped response with an optimal pH of 8.0. These findings provide valuable insights for predicting species occurrence and identifying suitable locations for restoration programs. Our study underscores the importance of considering multiple environmental variables in habitat suitability assessments. By incorporating these broader considerations, we can further refine predictive models and enhance conservation efforts aimed at restoring habitats conducive to the luxuriance of endangered species like H. criniferum.

基于覆盖准则的多轮测试用例优先排序方法

针对测试用例优先排序中存在的覆盖准则选择困难、确定最优准则序列难等问题,提出了一种基于扩展有限状态机模型的最优覆盖准则序列排序方法(CWTCP)。该方法首先运用鲸鱼优化算法挑选出最优覆盖准则序列,为后续的排序提供了有力支持;其次将覆盖准则序列依次应用在多轮排序中,以应对可能出现的平局问题。为了验证CWTCP方法的有效性,在5个扩展有限状态机模型上进行了实验,采用平均故障检测百分比(APFD)作为度量标准。实验结果表明,CWTCP的平均APFD值为0.918,比随机算法的相应值高了19.8%。

Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

西安市路网通达度及其对住宅价格的影响分析

城市路网通达度对住宅价格具有直接影响,基于城市路网开展住宅价格影响因素研究,可为城市住宅价格调控提供科学依据。本研究以西安市中心城区为例,以2017—2020年城市路网、住宅价格及兴趣点(POI)数据为基础,利用扩展空间句法软件sDNA中接近度与穿行度指数划定城市路网等级,并结合时空地理加权回归模型(GTWR)开展住宅价格影响因素研究。结果表明路网通达度对房价具有显著影响,可通过改变住宅周边路网结构达到调控房价之目的。

基于STIRPAT扩展模型的辽宁省能源碳排放影响因素研究

研究某地区人类活动对碳排放的影响并制定相应的碳减排政策。基于相关统计数据,使用IPCC法估算辽宁省2000~2022年能源碳排放量;选取总人口数、人均GDP、能源强度、城镇化率、煤炭消费量占比和第二产业占比6个变量建立STIRPAT扩展模型,并对STIRPAT扩展模型进行多重共线性检验和岭回归分析。结果表明,除煤炭消费量占比和能源强度对能源碳排放量的增加产生负向影响外,其他因素均对能源碳排放量的增加产生正向影响,如控制人口规模、保持经济平稳健康发展、加速优化能源结构和提高能源效率等,进而控制辽宁省能源碳排放量。本研究对实现碳减排目标具有重要的意义。

京津冀地区碳排放峰值模拟与低碳韧性评估

对京津冀碳排放现状及未来碳减排潜力进行了研究。首先,通过扩展STIRPAT模型及岭回归模型得到各驱动因素对碳排放量的影响程度;其次,分别模拟不同情景下京津冀碳排放变化趋势;最后,确定低碳韧性评估指标,评估京津冀不同情景下低碳韧性强弱。研究发现:不同地区的碳排放驱动因素产生的驱动效果存在差异;京津冀地区在低增速情景下更容易实现碳达峰,建议将发展模式的方向转向低增速情景;北京市在三种情景下更具备低碳韧性,天津市和河北省在低增速情景下的低碳韧性较强。

人口与消费对碳排放影响的分析模型与实证

从消费压力人口视角探讨碳排放问题,有利于正确判断和把握气候变化压力的人文因素,以及人口与消费可持续发展的动力学机制。本文通过对STIRPAT模型的扩展,应用岭回归方法计量分析人口、消费及技术因素对碳排放的影响。对我国1980至2007年碳排放情况的统计实证结果表明,扩展的STIRPAT模型对中国国情有较高的解释力。居民消费水平、人口城市化率、人口规模三个因素对我国碳排放总量的变化影响明显;现阶段我国居民消费水平与人口结构变化对碳排放的影响力已高于人口规模变化的影响力,居民消费水平与消费模式等人文因素的变化有可能成为我国碳排放的新的增长点;技术进步因素在此模型中对我国该阶段碳排放的解释力有限,表明我国未来通过技术进步减缓碳排放的潜力巨大。

自回归统计模型在延伸期预报中的应用

主要参考美国Jones等的统计预报模型,结合本地区研究特点,建立5-10月的夏季预报模型。利用1998—2007年的NCEP逐日700 hPa风场资料,使用向量场的自然正交分解所得的主成分,结合自回归及多元回归等统计方法,建立低频（30-50天）统计模型,用最近5候的前五个主成分来预报未来5候的时间主成分,最后将空间部分与所得的未来时间部分还原为低频风场。结果表明,低频经向风预报与低频纬向风相比较好,该模型对未来3-5候长江中下游的预报结果有参考价值。

基于GPS测量的弹道落点快速算法

将 GPS接收机应用于弹道修正引信的关键技术之一是根据 GPS测量的一部分弹道数据预测弹道的落点。文中采用正交多项式拟合弹道和误差补偿的方法预报弹道的落点 ,大大降低了预报落点的偏差 ,对

产超广谱β-内酰胺酶细菌感染的危险因素研究

目的探讨产超广谱β-内酰胺酶(ESBLs)细菌引起小儿感染的危险因素。方法用回顾性研究方法,对该院儿科住院的29例ESBLs阳性细菌感染病例和同期住院的89例ESBLs阴性细菌感染的对照病例进行调查,并应用非条件Logistic回归分析对其危险因素进行分析。结果年龄、联合应用抗生素的天数、插管、免疫抑制剂的使用和住院前3月内住院次数是产ESBLs阳性细菌感染的独立危险因素。结论 产ESBLs阳性细菌感染与多种因素有关,需加强对易感因素的认识,采取避免滥用抗生素及免疫抑制剂,避免不必要的插管,强调无菌操作等防治措施,以降低ESBLs阳性细菌感染的发生。

基于日常片段充电数据的锂电池健康状态实时评估方法研究

实时评估电动汽车动力锂电池的健康状态(stateof health,SOH)对电动汽车的维护至关重要。针对实际应用中电动汽车电池具有放电容量测量不稳定、测试负载重,操作不方便等问题。该文首先研究基于充电容量计算电池健康状态的可行性。然后,建立充电容量SOH模型将电池充电容量的估算转换为电池全充所需时间的估算。由于锂电池实际充电时的数据是片段的,提出基于扩展卡尔曼滤波和高斯过程回归的全充时间估算算法,解决了片段充电数据预测电池实时全充时间的问题。最后,通过实验仿真,验证了高斯过程扩展卡尔曼滤波在锂电池健康状态评估中的针对性、有效性和实时性。

基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法

该文针对传统扩展卡尔曼滤波(EKF)算法对电池数学模型精确的高度依赖与动态电池模型难以精确获得之间的矛盾问题,提出一种完全数据驱动的基于改进EKF算法的动力电池全生命周期荷电状态(SOC)估计方法。该方法为数据驱动的SOC估计方法和基于模型的SOC估计方法的良好结合,其优点在于:一方面抑制数据驱动方法存在累积误差的问题,并保留其良好的动态特性;另一方面改善基于模型的算法过度依赖电池模型的缺点,并保留其很好的鲁棒特性。该方法的创新之处在于将等效电路中难以获知的一部分视为以电池电流为输入,以内部电压为输出,以电池内部阻抗为时变参数的黑箱系统,并加以动态在线辨识,获得实时的动力电池真实状态,从而保证电池模型的准确性和动态性,真正实现动力电池全生命周期的SOC估算。仿真结果表明,该方法具有较好的鲁棒性和实用性。

基于改进信息熵的抢修操作过程复杂性测度模型

抢修操作过程复杂性既是抢修人员准确衡量抢修操作难易程度及不确定性的重要指标,又是辅助指挥员进行抢修任务分配及抢修资源调度的重要参考,还是进行抢修时间估计的重要依据。然而,目前没有合适的抢修操作过程复杂性测度方法。考虑到普通信息熵极大值公理与抢修操作过程复杂性的特征不相符,利用改进的信息熵建立抢修操作过程的复杂性测度模型,并利用回归分析法通过实例验证,模型测度结果可以客观地反映抢修操作过程的复杂性。

多变量时滞回归模型在江南地区初夏降水低频分量延伸期预报中的应用

利用江南地区77个台站的日降水资料及NCEP/NCAR再分析资料,基于不同时间尺度的江南地区降水低频分量和东亚地区850 h Pa低频经向风主成分,建立了多变量时滞回归(Multivariable Lagged Regression,MLR)模型,并对2011年5—7月江南降水低频分量进行延伸期逐日预报试验。结果表明,50~70 d时间尺度的江南低频降水的平均预报技巧高达0.92,可准确预报持续性强降水过程和降水低频位相的正负转换。对利用2001—2012年资料分别构建的MLR模型的历史回报预测试验表明,在50~70 d振荡较强和正常的年份,模型能提前30 d做出初夏江南低频降水分量预报。模型结果也表明,850 h Pa低频经向风的发展和演变是影响初夏江南低频降水未来30 d变化的显著信号,可作为延伸期强降水预报的关键因子。