基于Sentinel-2的青铜峡灌区春小麦和苜蓿早期识别

【目的】基于决策树分类算法对青铜峡灌区春小麦和苜蓿进行早期识别。【方法】在实地调研的基础上,结合Sentinel-2遥感影像,构建能够不断融入遥感数据的决策树分类算法,对青铜峡灌区春小麦和苜蓿进行早期识别。【结果】4月上旬,由于春小麦和苜蓿生长特征较为相似,春小麦和苜蓿的总体分类精度分别为69%和75%。此后,随着观测数据的不断融入,分类精度逐步提升,5月14日二者的分类精度即可达到90%以上。5月31日利用全部5期卫星影像数据时,春小麦的总体精度可达到94%,Kappa系数为0.75,苜蓿的总体精度可达到97%,Kappa为0.86。2023年青铜峡灌区春小麦种植面积为24 000 hm^(2),苜蓿种植面积为2 000 hm^(2)。其中,春小麦种植结构较为复杂,既存在集中连片的大规模种植,也存在大量面积较小的碎片化种植带。【结论】基于Sentinel-2遥感数据的决策树分类方法,可在4月上旬获取初步分类结果,在5月中旬苜蓿第1次收割之前对青铜峡灌区春小麦和苜蓿进行较为准确的识别。

Dynamic Time and Location Information in Ciphertext-Policy Attribute-Based Encryption with Multi-Authorization

Due to the mobility of users in an organization,inclusion of dynamic attributes such as time and location becomes the major challenge in Ciphertext-Policy Attribute-Based Encryption(CP-ABE).By considering this challenge;we focus to present dynamic time and location information in CP-ABE with mul-ti-authorization.Atfirst,along with the set of attributes of the users,their corre-sponding location is also embedded.Geohash is used to encode the latitude and longitude of the user’s position.Then,decrypt time period and access time period of users are defined using the new time tree(NTT)structure.The NTT sets the encrypted duration of the encrypted data and the valid access time of the private key on the data user’s private key.Besides,single authorization of attribute authority(AA)is extended as multi authorization for enhancing the effectiveness of key generation.Simulation results depict that the proposed CP-ABE achieves better encryption time,decryption time,security level and memory usage.Namely,encryption time and decryption time of the proposed CP-ABE are reduced to 19%and 16%than that of existing CP-ABE scheme.

On-line linear time construction of sequential binary suffix trees

Suffix trees are the key data structure for text string matching, and are used in wide application areas such as bioinformatics and data compression. Ukkonen algorithm is deeply investigated and a new algorithm, which decreases the number of memory operations in construction and keeps the result tree sequential, is proposed. The experiment result shows that both the construction and the matching procedure are more efficient than Ukkonen algorithm.

Aspect-Oriented Design Method for Embedded Systems Based on Timed Statecharts

The formal modelling and verification method has become an effective way of improving the reliability and correctness of complex,safety-critical embedded systems.Statecharts are widely used to formally model embedded applications,but they do not realise the reasonable separation of system concerns,which would result in code scattering and tangling.Aspect-Oriented Software Development(AOSD)technology could separate crosscutting concerns from core concerns and identify potential problems in the early phase of the software development life cycle.Therefore,the paper proposes aspect-oriented timed statecharts(extended timed statecharts with AOSD)to separately model base functional requirements and other requirements(e.g.,scheduling,error handling),thereby improving the modularity and development efficiency of embedded systems.Furthermore,the dynamic behaviours of embedded systems are simulated and analysed to determine whether the model satisfies certain properties(e.g.,liveness,safety)described by computation tree logic formulae.Finally,a given case demonstrates some desired properties processed with respect to the aspect-oriented timed statecharts model.

Timed-Automata Based Model-Checking of a Multi-Agent System: A Case Study

A multi-agent based transport system is modeled by timed automata model extended with clock variables. The correctness properties of safety and liveness of this model are verified by timed automata based UPPAAL. Agents have a degree of control on their own actions, have their own threads of control, and under some circumstances they are also able to take decisions. Therefore they are autonomous. The multi-agent system is modeled as a network of timed automata based agents supported by clock variables. The representation of agent requirements based on mathematics is helpful in precise and unambiguous specifications, thereby ensuring correctness. This formal representation of requirements provides a way for logical reasoning about the artifacts produced. We can be systematic and precise in assessing correctness by rigorously specifying the functional requirements.

基于T-S动态故障树的桁架机器人系统可靠性分析

为了提高桁架机器人系统的动态可靠性,并且在发生故障时提升排查故障的效率,提出一种对桁架机器人系统动态可靠性分析的方法。首先,对桁架机器人系统建立T-S动态故障树;然后,把T-S动态故障树和T-S动态门规则分别转化为离散时间贝叶斯网络(DTBN)与相应网络节点的条件概率表,根据DTBN正反向推理分别计算得到各时间段以及任务时间内系统失效概率和根节点后验概率,并获得各根节点的T-S概率重要度、T-S关键重要度和灵敏度;最后,应用Monte Carlo仿真法进行验证,结果显示相对误差为0.29%,证明所提方法可行。给出当系统发生故障时应优先排查的零部件,并确定了系统薄弱环节,得出应优先提高可靠度的零部件,为桁架机器人系统的动态可靠性分析提供了理论依据。

叠氮丙啶-实时荧光聚合酶链式反应-高通量测序技术鉴定冷链食品中多种病原菌

目的 应用叠氮丙啶-实时荧光聚合酶链式反应-高通量测序技术鉴定冷链食品中多种病原菌,构建病原菌分子进化树。方法 以冷链食品中的沙门氏菌、金黄色葡萄球菌、蜡样芽胞杆菌、副溶血性弧菌、单核细胞增生李斯特氏菌5种病原菌作为研究对象,应用叠氮丙啶-实时荧光聚合酶链式反应技术作为冷链食品中病原菌检测初筛手段,应用微生物培养法以及生化鉴定仪器法进行方法比对与结果验证,运用高通量测序以及分子进化树构建作为冷链食品中所分离病原菌的种属地位确证方法。结果 叠氮丙啶-实时荧光聚合酶链式反应技术成功扩增了冷链食品中生活状态病原菌的特征性核酸片段,排除了死亡细菌以及阴性对照菌的干扰,病原菌检出限可达到1×10^(3)CFU/mL,一次反应可检测42份试样,可以在18 h内完成检测工作。在冷链食品中病原菌抽样检测调查中,随机采集的751份冷链食品,共检出62株病原菌,病原菌总体检出率为8.3%(62/751)。通过后续的16S rRNA测序以及葡萄球菌属、弧菌属以及李斯特菌属分子进化树的构建,成功溯源了金黄色葡萄球菌的污染来源并完成病原菌种属定位。结论 本方法特异性好、灵敏度高、检测通量高,为冷链食品及相关食品中病原菌的精确检测与溯源分析提供新的思路与方法。

红树林共生真菌Paecilomyces sp· Tree 1-7代谢产物的研究

用硅胶柱色谱及重结晶等方法,并根据理化性质和光谱数据从一株红树林共生真菌Paecilomycessp.Tree 1-7中分离鉴定了四个化合物,分别为secalon ic ac id A(1),tenellic ac id A(2),大黄素(3),大黄酚(4)。并首次对化合物1和2进行了抗人的肝癌细胞hepG2的活性测试。结果显示,化合物1有很强的抗肿瘤活性,IC50=2.0μg/m l,化合物2的抗肿瘤活性IC50=62.1μg/m l。

基于3D^+-TPR-tree的点目标全时段移动索引设计

在经典3D R-tree基础上提出新的3D+R-tree索引,通过改变待索引数据项的结构并重新设计查询处理算法,减少包容矩形死区,提高查询效率;为了满足全时段查询要求,设计一种称为3D+-TPR-tree的联合索引结构,并对其中TPR-tree的参数包容矩形的调整算法进行优化。通过测试,证明3D+R-tree的查询效率明显高于普通3D R-tree;此外,测试结果也表明经过优化的参数包容矩形的调整算法也部分提升了TPR-tree的查询性能。

基于space-time的二叉树表示动态环境中的路径规划

研究具有空间和时间的三维动态环境下的机器人路径规划分析了四叉树表示二维空间的搜索算法,在此基础上,提出采space-time,用二叉树表示二维空间的方法时间信息中增加加速度利用二叉树遍历方法和算法设计一个在动态障碍物环境下进行路径规划的新算法并,,A*,,在足球机器人系统中进行仿真,实现了较好的路径规划。

基于Sentinel-2破碎化地块灌区作物种植结构的提取

【目的】探究基于Sentinel-2遥感影像的决策树分类模型提取破碎化地块灌区作物种植结构的适用性。【方法】选取新疆阿拉沟灌区为研究区,以2021年覆盖作物全生育期的Sentinel-2遥感影像为数据源,结合田间调查和Google高清影像目视解译采样,基于主要作物物候信息、NDVI时序特征等分析确定作物识别的关键期阈值,构建决策树模型进行灌区主要作物分类,并对分类结果精度验证。【结果】基于Sentinel-2提取的灌区种植结构分布图地块纹理清晰,能够满足灌区用水管理需要;构建的决策树分类模型可在灌区尺度实现作物分类,方法简便易行,总体精度达到81.56%,Kappa系数为0.716 6。【结论】采用Sentinel-2遥感影像和决策树分类方法识别破碎化地块灌区复杂作物分类是可行的,可为灌区输配水决策和农业用水精细化管理提供基础信息。

基于B^+-tree索引的多元时间序列相似查询

为提高多元时间序列相似查询执行效率,采用了基于距离索引结构的相似查询算法。利用主成分分析方法对多元时间序列数据降维并在此基础上进行聚类,以聚类质心为参考点,将各类变换到一维空间,利用B+-tree结构进行索引查询,找到与查询序列最相似的k个MTS序列。实验表明查询效率和准确性都有比较大的提高。

一种基于B^+-tree索引的有效相似查询算法

提出了一种多元时间序列相似查询算法.在距离索引结构相似查询算法的基础上,利用主成分分析方法对多元时间序列进行降维,并对主成分进行聚类,在聚类质心与各类之间的范数所构成的一维空间上,对聚类建立B+-tree索引结构,然后利用k近邻查询算法查找出与查询序列最相似的k个MTS序列.实验结果表明,文中算法的候选比率与查询时间明显低于Muse算法,且候选比率与查询时间受聚类个数影响不大,说明文中算法具有一定的优越性.

实时分布系统中Out-Tree任务的调度与检查点策略

针对实时分布系统中的Out-Tree任务,提出了一种启发式的调度算法(HSA—OT),并开发了一种多处理机上的最优检查点策略。该调度算法能够保证任务的调度长度最小,所需处理器数目尽量少,没有处理机间通信开销。该检查点策略没有检查点全局一致性开销,可保证各处理机的失效率最低。

Photodissociation Dynamics of 2-1odotoluene Investigated by Femtosecond Time-Resolved Mass Spectrometry

The photodissociation dynamics of 2-iodotoluene following excitation at 266 nm have been investigated employing femtosecond time-resolved mass spectrometry. The photofragments are detected by multiphoton ionization using an intense laser field centered at 800 nm. A dissociation time of 3804-50 fs was measured from the rising time of the co-fragments of toluene radical （C7H7） and iodine atom （I）, which is attributed to the averaged time needed for the C-I bond breaking for the simultaneously excited nσ and ππ＊ states by 266 nm pump light. In addition, a probe light centered at 298.23 nm corresponding to resonance wavelength of ground-state iodine atom is used to selectively ionize ground-state iodine atoms generated from the dissociation of initially populated hσ＊ and ππ＊ states. And a rise time of 4004-50 fs is extracted from the fitting of time-dependent I＋ transient, which is in agreement with the dissociation time obtained by multiphoton ionization with 800 nm, suggesting that the main dissociative products are ground-state iodine atoms.

转换时间数据流的加权FP-Tree挖掘算法

数据流中的数据分布随着时间动态变化.为了解决传统基于事务的挖掘算法较难建立时间特征和关系特征的相关性,事务和时间作为不同维度的基本单元耗费较高数据存储量的问题,基于事务的FP-Tree挖掘算法,提出了时间间断度的概念.引入了FP-Tree结点权重的概念,将时间数据进行动态转换,设计了基于加权FP-Tree的挖掘算法,对时间数据流关联规则挖掘.在实际数据集上完成了挖掘试验.结果表明,该算法相对传统FP-Growth算法能在计算效率降低约20%的情况下,提高10%以上的平均查全率和5%以上的平均查准率.

基于DTBN与T-S故障树的电池热管理系统可靠性分析

提出一种评估电动汽车动力电池热管理系统的可靠性分析算法。构建了电池热管理系统T-S动态故障树,转化为离散时间Bayes网络(DTBN)模型。同时将T-S动态门规则转化为网络节点的条件概率表。依据所建可靠性模型和部件的故障率,计算得到动力电池热管理系统在任务时间内的故障概率值为0.453,并获得各部件的后验概率、概率重要度和关键重要度。结果表明:对比Monte Carlo仿真方法,本方法的故障概率计算值误差小于5%,概率重要度靠前的是单体电池温度传感器、电池冷却液泵、电池冷却液管路等部件。该方法能克服传统故障树分析难以构建Bayes网络条件概率表等问题。

Canadian children’s and youth’s adherence to the 24-h movement guidelines during the COVID-19 pandemic: A decision tree analysis

Purpose:The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more and less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease-2019(COVID-19)outbreak.Methods:Data for this study were from a nationally representative sample of 1472 Canadian parents(Meanage=45.12,SD=7.55)of children(511 years old)or youth(1217 years old).Data were collected in April 2020 via an online survey.Survey items assessed demographic,behavioral,social,micro-environmental,and macro-environmental characteristics.Four decision trees of adherence and non-adherence to all movement recommendations combined and each individual movement recommendation(physical activity(PA),screen time,and sleep)were generated.Results:Results revealed specific combinations of adherence and non-adherence characteristics.Characteristics associated with adherence to the recommendation(s)included high parental perceived capability to restrict screen time,annual household income ofCAD 100,000,increases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,being a boy,having parents younger than 43 years old,and small increases in children’s and youth’s sleep duration since the COVID-19 outbreak began.Characteristics associated with non-adherence to the recommendation(s)included low parental perceived capability to restrict screen time,youth aged 1217 years,decreases in children’s and youth’s outdoor PA/sport since the COVID-19 outbreak began,primary residences located in all provinces except Quebec,low parental perceived capability to support children’s and youth’s sleep and PA,and annual household income ofCAD 99,999.Conclusion:Our results show that specific characteristics interact to contribute to(non)adherence to the movement behavior recommendations.Results highlight the importance of targeting parents’perceived capability for the promotion of children’s and youth’s movement behaviors during challenging times of the COVID-19 pandemic,paying particular attention to enhancing parental perceived capability to restrict screen time.

Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

Impact of time lags on diurnal estimates of canopy transpiration and canopy conductance from sap-flow measurements of Populus cathayana in the Qinghai–Tibetan Plateau

Recently, canopy transpiration （Ec） has been often estimated by xylem sap-flow measurements. However, there is a significant time lag between sap flow measured at the base of the stem and canopy transpiration due to the capacitive exchange between the transpiration stream and stem water storage. Significant errors will be introduced in canopy conductance （gc） and canopy transpiration estimation if the time lag is neglected. In this study, a cross-correlation analysis was used to quantify the time lag, and the sap flowbased transpiration was measured to pararneterize Jarvistype models of gc and thus to simulate Ec of Populus cathayana using the Penman-Monteith equation. The results indicate that solar radiation （Rs） and vapor pressure deficit （VPD） are not fully coincident with sap flow and have an obvious lag effect; the sap flow lags behind Rs and precedes VPD, and there is a 1-h time shift between Eo and sap flow in the 30-min interval data set. A parameterized Jarvis-type gc model is suitable to predict P. cathayana transpiration and explains more than 80% of the variation observed in go, and the relative error was less than 25%, which shows a preferable simulation effect. The root mean square error （RMSEs） between the predicted and measured Ec were 1.91×10^-3 （with the time lag） and 3.12×10^-3cm h^-1 （without the time lag）. More importantly, Ec simulation precision that incorporates time lag is improved by 6% compared to the results without the time lag, with the mean relative error （MRE） of only 8.32% and the mean absolute error （MAE） of 1.48 × 10^-3 cm h^-1.