Currently, for many applications, it is necessary to know the speed and
position of motors. This can be achieved using mechanical sensors coupled to
the motor shaft or using sensorless techniques. The sensorless techniques in
brushed dc motors can be classified into two types: 1) techniques based on the
dynamic brushed dc motor model and 2) techniques based on the ripple component
of the current. This paper presents a new method, based on the ripple
component, for speed and position estimation in brushed dc motors, using
support vector machines. The proposed method only measures the current and
detects the pulses in this signal. The motor speed is estimated by using the
inverse distance between the detected pulses, and the position is estimated by
counting all detected pulses. The ability to detect ghost pulses and to discard
false pulses is the main advantage of this method over other sensorless
methods. The performed tests on two fractional horsepower brushed dc motors
indicate that the method works correctly in a wide range of speeds and
situations, in which the speed is constant or varies dynamically.

本文介绍了一种基于脉动成分的支持向量机方法，用于无刷直流电机的速度和位置估计，该方法通过测量电流并检测该信号中的脉冲来估计电机速度，通过计算所有检测到的脉冲来估计位置，该方法较其他无刷传感器方法具有检测幽灵脉冲和丢弃虚假脉冲的主要优势。在两台分式马力无刷直流电机上进行的测试表明，该方法可以在速度恒定或动态变化的广泛速度和情况下正确工作。

无刷直流电机速度和位置的无传感器估计方法中的一种新方法

A New Method for Sensorless Estimation of the Speed and Position in  Brushed DC Motors Using Support Vector Machines

Clustering of urban traffic patterns is an essential task in many different
areas of traffic management and planning. In this paper, two significant
applications in the clustering of urban traffic patterns are described. The
first application estimates the missing speed values using the speed of road
segments with similar traffic patterns to colorify map tiles. The second one is
the estimation of essential road segments for generating addresses for a local
point on the map, using the similarity patterns of different road segments. The
speed time series extracts the traffic pattern in different road segments. In
this paper, we proposed the time series clustering algorithm based on K-Means
and Dynamic Time Warping. The case study of our proposed algorithm is based on
the Snapp application's driver speed time series data. The results of the two
applications illustrate that the proposed method can extract similar urban
traffic patterns.

在这篇论文中，描述了城市交通模式聚类的两个重要应用：第一个应用程序通过使用具有相似交通模式的道路段的速度来给地图块上色以估计缺失的速度值。第二个应用程序是使用不同道路段的相似模式来生成地图上某个当地点的地址所需的基本道路段的估计。本文提出了基于 K-Means 和动态时间规整的时间序列聚类算法。我们提出的算法的案例研究基于 Snapp 应用程序的司机速度时间序列数据。这两个应用程序的结果表明，所提出的方法能够提取相似的城市交通模式。

基于 K-Means 和动态时间规整的城市交通模式聚类：案例研究

Clustering of Urban Traffic Patterns by K-Means and Dynamic Time  Warping: Case Study

Accurate speed estimation of road vehicles is important for several reasons.
One is speed limit enforcement, which represents a crucial tool in decreasing
traffic accidents and fatalities. Compared with other research areas and
domains, the number of available datasets for vehicle speed estimation is still
very limited. We present a dataset of on-road audio-video recordings of single
vehicles passing by a camera at known speeds, maintained stable by the on-board
cruise control. The dataset contains thirteen vehicles, selected to be as
diverse as possible in terms of manufacturer, production year, engine type,
power and transmission, resulting in a total of $ 400 $ annotated audio-video
recordings. The dataset is fully available and intended as a public benchmark
to facilitate research in audio-video vehicle speed estimation. In addition to
the dataset, we propose a cross-validation strategy which can be used in a
machine learning model for vehicle speed estimation. Two approaches to
training-validation split of the dataset are proposed.

本文提出了一个包含 13 辆不同型号汽车的音视频数据集，可用于车辆速度估计的机器学习模型的训练和验证，以此降低交通事故和死亡率。