999 resultados para Vehicle tests.


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A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

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Counterions present at the surface of polyelectrolyte multilayers were utilized for the introduction of charged species into the multilayer via ion exchange. A typical polyelectrolyte multilayer film with Na+ counterions in the outermost layer was immersed in an AgNO3 aqueous solution and the rapid ion-exchange process was complete within 1 min. The silver ions thus introduced were then reduced in situ and silver nanoparticles were produced at the surface of the multilayer assembly. This example demonstrates that the counterions naturally occurring in every polyelectrolyte multilayer film can be a convenient vehicle for the introduction of various functionalities to the film.

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以主推加舵控制的小型自治水下机器人为研究对象,建立了水下机器人的数学模型并进行了分析。根据机器人结构的特点,对模型进行了必要的简化。设计了机器人的运动控制系统。通过湖试验正控制器的性能。

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针对以测距声纳为避碰传感器的一类欠驱动型AUV,提出了一种水平面和垂直面相结合的三维实时避碰方法。根据测距声纳和欠驱动AUV 的特殊性,首先从运动规划和路径规划2 个层次提出了AUV 混合型实时避碰结构,并分别设计了基于事件反馈监控的避碰自动机和基于免疫遗传的局部路径规划算法。多种典型障碍场景的半物理仿真实验表明,论文所提方法能够实现AUV 安全、稳定的三维避碰过程。

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针对母船的升沉运动影响有缆水下机器人的释放与回收的工程实际需求,提出了利用液压绞车降低中继器的升沉速度来实现水下机器人主动升沉补偿控制的方法,来提高水下机器人释放与回收的安全性能。建立液压绞车的数学模型并设计主动升沉补偿前馈控制器。水下机器人主动升沉补偿实验表明液压系统的非线性降低了液压绞车主动升沉补偿前馈控制的升沉补偿效率。针对液压绞车的非线性特性,设计了液压绞车主动升沉补偿预测控制算法,仿真实验表明基于液压系统参数辨识的液压绞车主动升沉补偿预测控制可以得到较高的升沉补偿效率。

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为提高自治水下机器人在垂直面跨越坡型、台阶型障碍的能力,提出了一种基于模糊控制的垂直面避碰规划方法。该方法以避碰声纳的输出作为输入,垂直面的深度调节量作为输出,直接建立了从障碍感知到避碰行为的映射。半物理仿真实验表明,该方法有效提高了某型自治水下机器人垂直面操纵性能,具有反应迅速、稳定性好、便于工程实现的优点。

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本文介绍了一种用于载人潜水器的导航传感器的数据采集及信息融合技术。航行控制计算机通过基于工业以太网的数据通信系统对各传感器进行数据采集,采用卡尔曼滤波器完成对各传感器数据信息的融合,以便提高数据的精度和控制系统的性能,并将结果送给监控计算机,用于载人潜水器的姿态显示。

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We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and the small number of training examples render most standard convergence bounds too loose to yield a meaningful guarantee of the generalization ability of the classifier. Instead, we estimate statistical significance of the observed classification accuracy, or the likelihood of observing such accuracy by chance due to spurious correlations of the high-dimensional data patterns with the class labels in the given training set. We adopt permutation testing, a non-parametric technique previously developed in classical statistics for hypothesis testing in the generative setting (i.e., comparing two probability distributions). We demonstrate the method on real examples from neuroimaging studies and DNA microarray analysis and suggest a theoretical analysis of the procedure that relates the asymptotic behavior of the test to the existing convergence bounds.

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This thesis describes two programs for generating tests for digital circuits that exploit several kinds of expert knowledge not used by previous approaches. First, many test generation problems can be solved efficiently using operation relations, a novel representation of circuit behavior that connects internal component operations with directly executable circuit operations. Operation relations can be computed efficiently by searching traces of simulated circuit behavior. Second, experts write test programs rather than test vectors because programs are more readable and compact. Test programs can be constructed automatically by merging program fragments using expert-supplied goal-refinement rules and domain-independent planning techniques.

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The vehicle navigation problem studied in Bell (2009) is revisited and a time-dependent reverse Hyperstar algorithm is presented. This minimises the expected time of arrival at the destination, and all intermediate nodes, where expectation is based on a pessimistic (or risk-averse) view of unknown link delays. This may also be regarded as a hyperpath version of the Chabini and Lan (2002) algorithm, which itself is a time-dependent A* algorithm. Links are assigned undelayed travel times and maximum delays, both of which are potentially functions of the time of arrival at the respective link. The driver seeks probabilities for link use that minimise his/her maximum exposure to delay on the approach to each node, leading to the determination of the pessimistic expected time of arrival. Since the context considered is vehicle navigation where the driver is not making repeated trips, the probability of link use may be interpreted as a measure of link attractiveness, so a link with a zero probability of use is unattractive while a link with a probability of use equal to one will have no attractive alternatives. A solution algorithm is presented and proven to solve the problem provided the node potentials are feasible and a FIFO condition applies for undelayed link travel times. The paper concludes with a numerical example.

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In practice, piles are most often modelled as "Beams on Non-Linear Winkler Foundation" (also known as “p-y spring” approach) where the soil is idealised as p-y springs. These p-y springs are obtained through semi-empirical approach using element test results of the soil. For liquefied soil, a reduction factor (often termed as p-multiplier approach) is applied on a standard p-y curve for the non-liquefied condition to obtain the p-y curve liquefied soil condition. This paper presents a methodology to obtain p-y curves for liquefied soil based on element testing of liquefied soil considering physically plausible mechanisms. Validation of the proposed p-y curves is carried out through the back analysis of physical model tests.