基于TARGET融合ARCS动机模型的应用统计学课程教学改革研究

学生的学习动机直接关系到课程教学目标的达成,激发学习动机是推动课程教学改革的应有之义。将TARGET和ARCS学习动机模型引入应用统计学课程教学,分析当前教学中存在的动机问题,提出有效激发学生动机的教学方案和实践策略,结果既可促进应用统计学课程教学质量提升,也可为其他相似课程教学设计提供参考借鉴。

A New RCS Statistical Model of Radar Target

In this paper, the drawbacks of conventional target fluctuation models used in radar target modeling are set out. It is usually difficult to statistically model a real target because there are very few parameters which can be used to approximate the probability density function (PDF) of a real target's radar cross section (RCS) in conventional target models. A new method of statistical modeling is suggested, according to which the first nth central moment of real target's RCS, combined with the Legendre orthogonal polynomials, is used to reconstruct the PDF of the target's RCS. The relationship between the coefficients of the Legendre polynomials and the central moments of RCS are deduced mathematically. Through a practical computing example, the error-of-fit is shown as a function of the orders of Legendre coefficients. By comparing the errors-of-fit caused by both the new model and the conventional models, it is concluded that the new nonparametric method for statistical modeling of radar targets is superior.

Analytical models for the penetration of semi-infinite targets by rigid,deformable and erosive long rods

A theoretical study is presented herein on the pen- etration of a semi-infinite target by a spherical-headed long rod for Yp 〉 S, where Yp is the penetrator strength and S is the static target resistance. For Yp 〉 S, depending upon initial impact velocity, there exist three types of penetration, namely, penetration by a rigid long rod, penetration by a deforming non-erosive long rod and penetration by an erosive long rod. If the impact velocity of the penetrator is higher than the hydrodynamic velocity （VH）, it will penetrate the target in an erosive mode; if the impact velocity lies between the hydrodynamic velocity （VH） and the rigid body velocity （VR）, it will penetrate the target in a deformable mode; if the impact velocity is less than the rigid body velocity （VR）, it will penetrate the target in a rigid mode. The critical conditions for the transition among these three penetration modes are proposed. It is demonstrated that the present model predictions correlate well with the experimental observations in terms of depth of penetration （DOP） and the critical transition conditions.

Establishing a rat model of spastic cerebral palsy by targeted ethanol injection

Spastic cerebral palsy is generally considered to result from cerebral cortical or pyramidal tract damage. Here, we precisely targeted the left pyramidal tract of 2-month-old Sprague-Dawley rats placed on a stereotaxic instrument under intraperitoneal anesthesia. Based on the rat brain stereotaxic map, a 1-mm hole was made 10 mm posterior to bregma and 0.8 mm left of sagittal suture. A microsyringe was inserted perpendicularly to the surface of the brain to a depth of 9.7 mm, and 15 wL of ethanol was slowly injected to establish a rat model of spastic cerebral palsy. After modeling, the rats appeared to have necrotic voids in the pyramidal tract and exhibited typical signs and symptoms of flexion spasms that lasted for a long period of time. These findings indicate that this is an effective and easy method of establishing a rat model of spastic cerebral palsy with good reproducibility. Ethanol as a chemical ablation agent specifically and thoroughly damages the py- ramidal tract, and therefore, the animals display flexion spasms, which are a typical symptom of the disease.

Design and comparison of minimal symmetric model-subset for maneuvering target tracking

Model-set is utilized in state estimation for maneuver- ing target tracking. Two minimal symmetric model-subsets are designed and investigated by moment matching method, which include hypersphere-symmetric model-subset and axis-symmetric model-subset, if system mode is a random variable and obeys certain probability distribution. They can be used as the fun- damental model-subset for multiple models estimation with fixed structure, variable structure and moving bank. The model-groups constructed by above designed subsets are given, which give the practical guidance for use of model-set in multiple models ap- proach with a variable structure. Simulation results show that the performances of two minimal model-set significantly outperform the corresponding model-sets with fixed spacing.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

根据在目标追踪以及澄清的在水下的调遣的即时性能和可靠性的要求，运动展示在水下目标，交往多重模型算法基于模糊逻辑，推理(FIMM ) 被建议。调遣目标的模式被模型集合代表，包括经常的速度模型(CA ) ，歌手模型，和将近经常的速度在 FIMM 技术的水平拐弯的模型(HT ) 。模拟结果与常规 IMM，可靠性和即时性能相比显示出那在水下目标追踪能被 FIMM 改进算法。

Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ~2-targets

To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar’s display and preventing targets from being obscured.This paper concerns with the detection analysis of the novel version of CFAR schemes(cell-averaging generalized trimmed-mean,CATM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ~2-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models(SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CATM detector is described briefly. Detection performances for optimal, CAM, CA, trimmed-mean(TM) and ordered-statistic(OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters,the novel model CAM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CAS and CAM can be treated as special cases of the CATM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CAcheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity. Moreover, the non-coherent integration of M pulses ameliorates the processor performance either it operates in homogeneous or multi-target environment.

Propofol Target-Controlled Infusion Modeling in Rabbits:Pharmacokinetic and Pharmacodynamic Analysis

This study aimed to establish a new propofol target-controlled infusion（TCI） model in animals so as to study the general anesthetic mechanism at multi-levels in vivo. Twenty Japanese white rabbits were enrolled and propofol（10 mg/kg） was administrated intravenously. Artery blood samples were collected at various time points after injection, and plasma concentrations of propofol were measured. Pharmacokinetic modeling was performed using Win Nonlin software. Propofol TCI within the acquired parameters integrated was conducted to achieve different anesthetic depths in rabbits, monitored by narcotrend. The pharmacodynamics was analyzed using a sigmoidal inhibitory maximal effect model for narcotrend index（NI） versus effect-site concentration. The results showed the pharmacokinetics of propofol in Japanese white rabbits was best described by a two-compartment model. The target plasma concentrations of propofol required at light anesthetic depth was 9.77±0.23 μg/m L, while 12.52±0.69 μg/m L at deep anesthetic depth. NI was 76.17±4.25 at light anesthetic depth, while 27.41±5.77 at deep anesthetic depth. The effect-site elimination rate constant（ke0） was 0.263/min, and the propofol dose required to achieve a 50% decrease in the NI value from baseline was 11.19 μg/m L（95% CI, 10.25–13.67）. Our results established a new propofol TCI animal model and proved the model controlled the anesthetic depth accurately and stably in rabbits. The study provides a powerful method for exploring general anesthetic mechanisms at different anesthetic depths in vivo.

Ore prospecting model and targets for the Dashuigou tellurium deposit, Sichuan Province, China

The Dashuigou tellurium(Te) deposit in Shimian city, Sichuan Province is the only known independent Te ore deposit in China. Samples were collected by1/50,000 stream sediment survey and analyzed by inductively coupled plasma–mass spectrometry, X-ray fluorescence spectrometry, emission spectrometry, and atomic absorption spectroscopy. An ore prospecting model for the Dashuigou Te deposit was then established. In the Dashuigou area, bismuth(Bi), Te, and gold(Au) concentrations in stream sediment samples displayed weak-positive anomalies, while silver(Ag) displayed a weaknegative anomaly. Bi, Te, Ag, and Au anomalies are regarded as indicators of Te deposits; the greater the ratio of Te+Bi/Au+Ag, the larger the possibility of an independent tellurobismuthite deposit. The ratio calculated from our samples is 7.288. Five locations were identified for prospecting for Te minerals by this model, including the northern part of the Dashuigou Te deposit, Majiagou,Tizigou, southeastern Miaoping, and northern Baishuihe.These five regions are within the Dashuigou dome anticline, the exposed strata of which are controlled by tracing the tensile shear fracture; the metallogenic geological conditions and geochemical characteristics are the same as those of the known Dashuigou Te deposit. Already, Te–Bi veins have been found in some of these areas.

A Fuzzy Adaptive Algorithm Based on“Current”Statistical Model for Maneuvering Target Tracking

The basic"current"statistical model and adaptive Kalman filter algorithm can not track a weakly maneuvering target precisely,though it has good estimate accuracy for strongly maneuvering target.In order to solve this problem,a novel nonlinear fuzzy membership function was presented to adjust the upper and lower limit of target acceleration adaptively,and then the validity of the new algorithm for feeblish maneuvering target was proved in theory.At last,the computer simulation experiments indicated that the new algorithm has a great advantage over the basic"current"statistical model and adaptive algorithm.

Research on Motion Attention Fusion Model-Based Video Target Detection and Extraction of Global Motion Scene

For target detection algorithm under global motion scene, this paper suggests a target detection algorithm based on motion attention fusion model. Firstly, the motion vector field is pre-processed by accumulation and median filter;Then, according to the temporal and spatial character of motion vector, the attention fusion model is defined, which is used to detect moving target;Lastly, the edge of video moving target is made exactly by morphologic operation and edge tracking algorithm. The experimental results of different global motion video sequences show the proposed algorithm has a better veracity and speedup than other algorithm.

Computer Model for Evaluating Multi-Target Tracking Algorithms

Public benchmark datasets have been widely used to evaluate multi-target tracking algorithms. Ideally, the benchmark datasets should include the video scenes of all scenarios that need to be tested. However, a limited amount of the currently available benchmark datasets does not comprehensively cover all necessary test scenarios. This limits the evaluation of multitarget tracking algorithms with various test scenarios. This paper introduced a computer simulation model that generates benchmark datasets for evaluating multi-target tracking algorithms with the complexity of multitarget tracking scenarios directly controlled by simulation inputs such as target birth and death rates, target movement, the rates of target merges and splits, target appearances, and image noise types and levels. The simulation model generated a simulated video and also provides the ground-truth target tracking for the simulated video, so the evaluation of multitarget tracking algorithms can be easily performed without any manual video annotation process. We demonstrated the use of the proposed simulation model for evaluating tracking-by-detection algorithms and filtering-based tracking algorithms.

Maneuvering target track-before-detect via multiple-model Bernoulli particle filter

Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.

Application of Incident Chain Model and Targeted Dissemination Technology in Early Warning System

In order to improve the accuracy and efficiency of early warning system, the incident chain model and the targeted dissemination technology are proposed in this paper. Firstly, the occurrence probability, affected area and duration of disaster are predicted with the incident chain model and GIS. According to prediction results, the early warning system can accurately deliver early warning information specifically to the affected areas through targeted dissemination. Moreover, dissemination performance can also be evaluated in real time after early warning information dissemination, so that everyone in the affected area can receive early warning information successfully. The incident chain model and the targeted dissemination technology presented in this study are of great significance for improving the information dissemination ability of early warning system.

Searching for Novel Targets to Control Wheat Head Blight Disease—I-Protein Identification, 3D Modeling and Virtual Screening

Fusarium head blight (FHB) is a destructive disease of wheat and other cereals. FHB occurs in Europe, North America and around the world causing significant losses in production and endangers human and animal health. In this article, we provide the strategic steps for the specific target selection for the phytopathogen system wheat-Fusarium graminearum. The economic impact of FHB leads to the need for innovation. Currently used fungicides have been shown to be effective over the years, but recently cereal infecting Fusaria have developed resistance. Our work presents a new perspective on target selection to allow the development of new fungicides. We developed an innovative approach combining both genomic analysis and molecular modeling to increase the discovery for new chemical compounds with both safety and low environmental impact. Our protein targets selection revealed 13 candidates with high specificity, essentiality and potentially assayable with a favorable accessibility to drug activity. Among them, three proteins: trichodiene synthase, endoglucanase-5 and ERG6 were selected for deeper structural analyses to identify new putative fungicides. Overall, the bioinformatics filtering for novel protein targets applied for agricultural purposes is a response to the demand for chemical crop protection. The availability of the genome, secretome and PHI-base allowed the enrichment of the search that combined experimental data in planta. The homology modeling and molecular dynamics simulations allowed the acquisition of three robust and stable conformers. From this step, approximately ten thousand compounds have been virtually screened against three candidates. Forty-five top-ranked compounds were selected from docking results as presenting better interactions and energy at the binding pockets and no toxicity. These compounds may act as inhibitors and lead to the development of new fungicides.

A NEW MODELING AND FILTERING APPROACH FOR TRACKING MANEUVERING TARGETS

A new modeling and filtering approach for tracking maneuvering targets is presented in thispaper.The approach,which makes optimal estimate for the model With the random variable possible,depends on random step modeling of target maneuvers.In the new model,the unknown targetacceleration is treated as a random variable and then estimated directly.A detector is designed tofind out the target maneuvers and the estimation algorithm will be restarted when the maneuvers oc-cur.Combination of three-dimention Kalman filter with a detector forms a tracker for maneuveringtargets.The new tracking scheme is easy to implement and its capability is illustrated in two trackingexamples in which the new approach is compared with Mooses’on the performance.

一种基于目标与背景特征分离模型的高光谱目标检测修正算法

高光谱图像立方体数据可以提供成像场景中地物在可见光和近红外波长范围内的空间信息和地物属性诊断的光谱特征信息,在目标检测与识别方面拥有得天独厚的天然优势。然而,基于高光谱图像数据的目标检测也存在一定缺陷,如经典的高光谱目标检测算法仅利用光谱维度信息检测目标,检测模型要么对背景高维特征矩阵构建的准确度不足,要么对背景先验光谱特征的完备性要求较高,导致算法对不同复杂度的检测场景适应性不强。因此,基于计算复杂度较低、参数需求量较少且检测性能较为优异的经典多目标检测算法—多目标约束能量最小化(MCEM),提出了一种基于目标与背景环境特征分离模型的高光谱目标检测修正算法(R-MCEM)。首先,设计了一个与目标形状、尺寸相近的逐像元移动运算窗口,依次计算窗口中的每个像元与窗口内其他像元的光谱距离之和D1,像元与各类目标的光谱距离之和D2。其次,采用获得D1/D2最小值的像元替换窗口内的所有像元值。然后,自左向右、自上而下逐像元移动窗口,重复窗口内每一个像元与目标、背景像元的光谱距离运算,并确定窗口内与背景相似度最高、与目标相似度最低的像元。直到移动运算窗口遍历整个高光谱图像,大幅提升了基于目标与背景环境特征分离的背景高维特征矩阵准确度。分别设计了基于实测高光谱图像数据和模拟图像数据的修正检测算法性能验证试验,并采用三维操作特征曲线(3D ROC)结合目标与背景分离度(SDBT)开展修正算法的检测精度评估。试验结果表明,提出的修正算法有效减少了虚警率,提高了检测精度。基于实测数据的检测精度、目标与背景分离度由MCEM算法的0.937 7、 0.57提升到R-MCEM的0.993 5、 0.67,基于模拟数据的亚像元检测能力由MCEM的20%丰度提升到R-MCEM的15%丰度。

低空轻量级红外弱小目标检测算法

精准的红外弱小目标检测是实时监控、追踪、制导的关键;红外弱小目标存在检测难度高、误检高、漏检严重的问题。为了提高红外弱小目标检测算法的实时性和检测精度,提出了一种超轻量红外弱小目标检测算法SL-YOLO。首先,重设计下采样方案,针对红外图像特征信息调节网络架构,解决红外弱小目标特征梯度降低和特征消失问题;然后设计网络模型剪枝算法,实现剪枝算法与网络结构的融合,去除冗余参数,实现检测速度的提高;最后设计Varifocal-SIoU损失函数,在均衡正负样本与重叠损失的同时,对正样本进行加权处理,解决背景干扰问题。实验结果表明,在SIRST和IDSAT数据集下检测精度分别提高至96.4%、98.1%,模型体积和计算量可压缩至190 kB、0.9 GFLOPs,推理速度降至3 ms以下。与主流算法进行对比,改进后算法在检测精度、模型体积、计算量等方面均取得了不错的成绩。能够满足实时性检测需求。

互动达标模式干预对缺血性心肌病患者负性情绪和健康行为的影响

目的:探究互动达标模式干预对缺血性心肌病患者负性情绪和健康行为的影响。方法:将2020年5月-2022年7月某院收治的120例缺血性心肌病患者随机分为两组。对照组60例予以常规临床干预,干预组60例在对照组基础上予以互动达标模式干预。比较两组干预前后的负性情绪、健康行为、自护能力,评价两组的治疗依从性及生活质量。结果:干预后,干预组的焦虑自评量表(SAS)、抑郁自评量表(SDS)评分均低于对照组(t=-7.231,-9.451;P<0.05);干预后,干预组的健康促进生活量表(HPLP-Ⅱ)中人际关系、健康责任、压力管理、营养、体育运动、精神成长评分均高于对照组(t=9.801,8.168,8.590,10.423,8.449,8.765;P<0.05);干预后,干预组的自我护理能力量表(ESCA)中护理技能、自护责任感、自护知识、自我概念评分均高于对照组(t=7.819,8.116,10.147,13.442;P<0.05);干预组的治疗依从率高于对照组(χ^(2)=6.536,P<0.05);干预后,干预组的生活质量量表(WHOQOL-BREF)各维度评分均高于对照组(t=9.086,9.056,6.451,9.755;P<0.05)。结论:互动达标模式干预对缺血性心肌病患者的干预效果显著,能有效缓解患者的负性情绪,改善其健康行为,提升其自护能力及治疗依从性,有助于提高患者的生活质量。