Time Series Clustering Model based on DTW for Classifying Car Parks

Li, Taoying; Wu, Xu; Zhang, Junhe

doi:10.3390/a13030057

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…The model performs better clustering results compared to the dynamic timewarping model [95] To To extract the main features underlying the time-series data in historical driving memory…”

Section: K-medoids Clustering (K-means-based)mentioning

confidence: 99%

“…In V2X communication, all four types of ML are applied. Supervised learning could be used to detect the occupancy of a parking lot by using labeled data to solve classification and regression tasks [ 95 ]. Unsupervised learning is more suitable for data grouping (clustering) tasks and could be used to group various types of vehicles according to their shape [ 82 ] or similar tasks.…”

Section: Technologies In Vehicles-to-everything Communicationmentioning

confidence: 99%

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Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

Ušinskis,

Makulavičius,

Petkevičius

et al. 2024

Sensors

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Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented.

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“…The model performs better clustering results compared to the dynamic timewarping model [95] To To extract the main features underlying the time-series data in historical driving memory…”

Section: K-medoids Clustering (K-means-based)mentioning

confidence: 99%

Section: Technologies In Vehicles-to-everything Communicationmentioning

confidence: 99%

Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

Ušinskis,

Makulavičius,

Petkevičius

et al. 2024

Sensors

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show abstract

“…Dynamic Time Warping (DTW) is used to warp two feature vector sequences in time. DTW is well known in speech recognition to cope with different speaking speeds [18].DTW is used as feature matching with the deployment of the technique of minimum Euclidean distance or another distancebased approach [19]. DTW measures the similarity between the two temporal sequences which may vary in time [17].…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

“…To represent the parameters of measurement for the duration of syllable pronunciation, DTW of minimum path cost parameters coefficient is used. DTW calculates the local stretch on the time axis for two-series objects to optimally map one (query) to the other (reference) by calculating the distance between unequal sequences of length [19]. Minimum path cost alignment calculation uses the cosine distance [17] to determine the optimal warp path from the start point of syllable utterance until the end of an utterance.…”

Section: Features Extraction Design For Harakaatmentioning

confidence: 99%

Dynamic Time Warping Features Extraction Design for Quranic Syllable-based Harakaat Assessment

Shafie¹,

Azizan²,

Adam³

et al. 2022

IJACSA

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The use of technological speech recognition systems with a variety of approaches and techniques has grown exponentially in varieties of human-machine interaction applications. The assessment for Qur'anic recitation errors based on syllables utterance is used to meet the Tajweed rules which generally consist of Harakaat (prolonging). The digital transformation of Quranic voice signals with identification of Tajweed-based recitation errors of Harakaat is the main research work in this paper. The study focused on speech processing implemented using the representation of Quranic Recitation Speech Signals (QRSS) in the best digital format based on Al-Quran syllables and feature extraction design to reveal similarities or differences in recitation (based on Al-Quran syllables) between experts and student. The method of Dynamic Time Warping (DTW) is used as Short Time Frequency Transform (STFT) of QRSS syllable feature for Harakaat measurement. Findings from this paper include an approach based on human-guidance threshold classification that is used specifically to evaluate Harakaat based on the syllables of the Qur'an. The threshold classification performance obtained for Harakaat is above 80% in the training and testing stages. The results of the analysis at the end of the experiment have concluded that the threshold classification method for Minimum Path Cost (MPC) feature parameters can be used as an important feature to evaluate the rules of Tajwid Harakaat embedded in syllables.

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“…Many researchers have used DTW in their works. In [31], DTW is used in clustering the time-series data for classifying car parks.…”

mentioning

confidence: 99%

A Robust Clustering-based Lossless Data Compression Technique for Air Quality Monitoring System

Ghose

Rehena

2022

Preprint

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With the advent of the IoT and Big data, there is an enormous supply of datasets, which are captured by the sensors installed in the relevant places. For the air pollution monitoring system, several sensors provide a huge dataset. They continuously capture pollutant concentration as well as the meteorological factors to form the voluminous, multivariate time-series dataset, which includes both. Those sensors are paired with low-capacity batteries and limited memory, transmission, and computational components. Transmitting the high volume data to the monitoring system deployed in the cloud demands high energy dissipation and higher bandwidth. The single solution for all these constraints is data compression. Data compression reduces the volumes of the datasets. The reduced volume of data saves energy and it is easier to transmit the compressed data through the limited bandwidth. In this paper, a lossless data compression algorithm is proposed. On successful implementation of the algorithm, the data is compressed with absolutely no data loss. To evaluate the efficiency of the algorithm, its performance is compared with some classical and several state-of-the-art compression schemes. The experimental results show that the proposed compression algorithm outperforms the classical as well as state-of-the-art models concerning the performance evaluating parameters like Compression Ratio, Compression Factor, and most importantly Power Saving.

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Time Series Clustering Model based on DTW for Classifying Car Parks

Cited by 13 publications

References 29 publications

Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication

Dynamic Time Warping Features Extraction Design for Quranic Syllable-based Harakaat Assessment

A Robust Clustering-based Lossless Data Compression Technique for Air Quality Monitoring System

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