Adaptive fuzzy C-means clustering integrated with local outlier factor

She, Chunyan; Zeng, Shaohua; Wang, Qi; Wang, Shuai; Xu, Yu

doi:10.3233/ida-216266

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…In the FAUKF algorithm, the input of the fuzzy control is the difference between the actual and theoretical value of the residual covariance and its change rate, whereas the output of the fuzzy control is the adjustment factor. The input and output fuzzy sets of the system are [37,38]:…”

Section: Faukf Algorithmmentioning

confidence: 99%

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

Liu,

Li,

Zheng

et al. 2024

Meas. Sci. Technol.

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Monorail cranes have always played an important role in mine auxiliary transportation systems owing to their excellent transportation performance and are therefore a desirable area in which to apply driverless technologies. However, the low-accuracy recognition of monorail track slopes and the poor reliability of recognition results make it difficult and dangerous to implement fully driverless monorail cranes. Aiming to solve these problems, a method for the accurate identification of longitudinal monorail slopes based on the use of a dynamic forgetting factor for recursive least squares (DFFRLS) and a fuzzy adaptive unscented Kalman filter (FAUKF) is proposed. First, acquired acceleration and velocity data are pre-processed using a rolling window. Second, the real-time longitudinal track-curvature value is calculated using the DFFRLS algorithm with the processed data and an established track-curvature model. Finally, based on existing track-curvature values, dynamic recognition of the monorail track slope is realised using the FAUKF algorithm with a fuzzy control factor, improving the accuracy of track gradient recognition. Experiments show that the DFFRLS-FAUKF algorithm improves the accuracy of track-slope recognition by up to 21.26% and 33.93% on average compared with that of DFFRLS with an adaptive extended Kalman filter (DFFRLS-AEKF) or an adaptive unscented Kalman filter (DFFRLS-AUKF).

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Section: Faukf Algorithmmentioning

confidence: 99%

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

Liu,

Li,

Zheng

et al. 2024

Meas. Sci. Technol.

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Incremental density clustering framework based on dynamic microlocal clusters

Zhang,

Li,

Dong

et al. 2023

IDA

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With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors’ streaming data analysis.

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Adaptive fuzzy C-means clustering integrated with local outlier factor

Cited by 2 publications

References 25 publications

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

Incremental density clustering framework based on dynamic microlocal clusters

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