提升车辆集成导航系统定位精度的混合预测方法

研究论文
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本文主要探讨了一种针对车辆集成导航系统（Strapdown Inertial Navigation System/Global Positioning System, 简称INS/GPS）的混合预测方法。随着自动驾驶和导航技术的发展，当GPS信号受到干扰或丢失时，提高车辆定位精度变得至关重要。因此，研究人员Di Zhao、Huaming Qian 和 Dingjie Xu提出了一种创新的策略。 该混合预测方法结合了径向基函数神经网络（Radial Basis Function Neural Network, RBFNN）、时间序列分析和无迹卡尔曼滤波（Unscented Kalman Filter, UKF）算法。其核心在于建立两种工作模式：在GPS信号正常时，利用RBFNN和时间序列分析对INS和GPS之间的误差进行训练，以捕捉它们之间的关系。这种方法旨在利用INS在GPS失锁时的惯性数据作为辅助，通过RBFNN的预测能力和时间序列分析的趋势估计来改善定位精度。 在GPS信号不可用的情况下，混合预测方法将RBFNN和时间序列分析的预测结果整合到无迹卡尔曼滤波器的测量更新过程中。无迹卡尔曼滤波是一种用于处理非线性和不确定性的高效算法，它能够融合来自多个传感器的数据，提供更精确的状态估计。 为了验证这个混合预测方法的有效性，研究者进行了计算机模拟实验。模拟结果显示，与传统的单一依赖GPS或仅使用惯性测量单元（Inertial Measurement Unit, IMU）的方法相比，该混合方法显著提高了车辆在GPS信号丢失情况下的定位精度和鲁棒性。此外，它还展示了在复杂道路环境和动态条件下，混合预测策略如何有效地降低导航系统的误差累积，从而确保车辆的行驶安全和路线规划准确性。 这项研究对于提升车辆集成导航系统在GPS受限环境下的性能具有重要意义，为未来智能交通系统的设计提供了理论支持和技术参考。

Datasheet Driven Switching Loss, Turn-ON/OFF Overvoltage, di/dt, and dv/dt Prediction Method for SiC MOSFET这个题目呈现什么研究内容（话题-方法-结论-范围）成分？

2023-06-13 上传
方法：Datasheet Driven Switching Loss, Turn-ON/OFF Overvoltage, di/dt, and dv/dt Prediction Method； 结论：能够预测SiC MOSFET的开关损耗、开关过压、di/dt和dv/dt； 范围：研究SiC MOSFET的开关特性和性能。

You have trained a TensorFlow model using Dropout layers for regularization. Now, youwant to use Monte Carlo Dropout for making predictions. What do you need to dodifferently during the prediction stage?

2023-06-03 上传
This can be done by creating a custom Dropout layer that overrides the `call()` method to apply the Dropout probability only during training, and to deactivate the Dropout layer during prediction....

精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

2023-02-27 上传
本文提出了一种基于PPI网络和语义相似性，加上蛋白质分层功能的蛋白质功能预测算法框架，对酒葡萄酵母细胞的蛋白质数据表现出良好的效果，但其他数据的效果如何仍有待验证。此外，该框架中的功能特征选择的数量以及...

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2023-04-09 上传
For example, the University of California, San Diego developed a model that uses neural networks and decision trees to predict parking demand based on a variety of factors, including weather, day of ...

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2023-04-02 上传
It is a measure of the accuracy of a prediction model and can be used to assess the reliability of predictions made by the model. The coverage probability is typically set at a certain level, such as...

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2023-03-01 上传
以下是与“Prediction and risk assessment of extreme weather events based on gumbel copula function”类似的文献推荐： 1. "Multivariate Extreme Value Theory for Risk Assessment" by Alexander McNeil, R...

wirte a literature review about parking demand prediction in 1000 words

2023-04-09 上传
It involves the forecasting of the number of vehicles that need to be parked in a certain area, at a given time. This is often a challenging task, as parking demand can be influenced by a wide ...

WIDE bandgap devices, such as silicon carbide (SiC) metal–oxide–semiconductor field-effect transis- tors (MOSFETs) present superior performance compared to their silicon counterparts [1]. Their lower ON-state resistance and faster switching capability attract lots of interest in high-power- density applications [2]. Faster switching speed enables lower switching loss and higher switching frequency, which is benefi- cial to high-efficiency and high power density. However, severe electromagnetic interference (EMI) and transient overvoltage issues caused by fast switching speed jeopardize the power quality and reliability of converters [3], [4]. Therefore, there is a tradeoff between efficiency and reliability in the choice of switching speed. An optimized design should ensure theoperation within both safe-operation-area and EMI limits, and switching loss should be as small as possible. A prediction method of switching performance is important and helpful for designer to evaluate and optimize converter design. The most concerned switching characteristics are switching loss, dv/dt, di/dt, and turn-ON/OFF overvoltage generally. These characteristics are crucial for the design of heatsink, filter, and gate driver. Related discussions have been presented in many existing research articles as following.请将这一段进行以下要求，Move analysis 语步（内容成分）分析； Language devices和实现该功能的语言手段（某些关键专有名词提供汉语翻译）

2023-06-13 上传
- 使用了比较性的语言手段，如superior performance、lower ON-state resistance、faster switching capability等，突出了宽禁带器件相对于硅器件的优越性能。 - 使用了并列的语言手段，如lower switching loss和...

[train_x,test_x]=predict(train_x,train_y_scale,test_x,test_y_scale,selected_variables,A_max,fold,method);

2023-04-04 上传
- method: a string indicating the type of predictive modeling method to use The function is expected to return two output arguments: train_x and test_x, which are matrices of predicted values for the...

score threhold

2023-05-04 上传
Score threshold refers to a minimum score that must be achieved by a prediction or result in order to be considered valid or useful. In machine learning, score thresholds are often used in ...
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