Application of K-Means Clustering Algorithm in Online English Learning Effect Evaluation

Cui, Ran

doi:10.46719/dsa202029526

Cited by 22 publications

(26 citation statements)

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“…In order to determine the location of obstacles in the image, the k-means mean clustering algorithm is used to cluster and analyze the obstacle feature point dataset

. The specific methodological procedure is as follows [ 30 , 31 ]: MSER algorithm is used to extract the maximum stable extreme value region

. Regional range difference

is calculated for the two frames of images collected in the experiment.…”

Section: Methodsmentioning

confidence: 99%

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Ding

Zhang³

et al. 2023

Sensors

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Road obstacle detection is an important component of intelligent assisted driving technology. Existing obstacle detection methods ignore the important direction of generalized obstacle detection. This paper proposes an obstacle detection method based on the fusion of roadside units and vehicle mounted cameras and illustrates the feasibility of a combined monocular camera inertial measurement unit (IMU) and roadside unit (RSU) detection method. A generalized obstacle detection method based on vision IMU is combined with a roadside unit obstacle detection method based on a background difference method to achieve generalized obstacle classification while reducing the spatial complexity of the detection area. In the generalized obstacle recognition stage, a VIDAR (Vision-IMU based identification and ranging) -based generalized obstacle recognition method is proposed. The problem of the low accuracy of obstacle information acquisition in the driving environment where generalized obstacles exist is solved. For generalized obstacles that cannot be detected by the roadside unit, VIDAR obstacle detection is performed on the target generalized obstacles through the vehicle terminal camera, and the detection result information is transmitted to the roadside device terminal through the UDP (User Data Protocol) protocol to achieve obstacle recognition and pseudo-obstacle removal, thereby reducing the error recognition rate of generalized obstacles. In this paper, pseudo-obstacles, obstacles with a certain height less than the maximum passing height of the vehicle, and obstacles with a height greater than the maximum passing height of the vehicle are defined as generalized obstacles. Pseudo-obstacles refer to non-height objects that appear to be “patches” on the imaging interface obtained by visual sensors and obstacles with a height less than the maximum passing height of the vehicle. VIDAR is a vision-IMU-based detection and ranging method. IMU is used to obtain the distance and pose of the camera movement, and through the inverse perspective transformation, it can calculate the height of the object in the image. The VIDAR-based obstacle detection method, the roadside unit-based obstacle detection method, YOLOv5 (You Only Look Once version 5), and the method proposed in this paper were applied to outdoor comparison experiments. The results show that the accuracy of the method is improved by 2.3%, 17.4%, and 1.8%, respectively, compared with the other four methods. Compared with the roadside unit obstacle detection method, the speed of obstacle detection is improved by 1.1%. The experimental results show that the method can expand the detection range of road vehicles based on the vehicle obstacle detection method and can quickly and effectively eliminate false obstacle information on the road.

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“…In order to determine the location of obstacles in the image, the k-means mean clustering algorithm is used to cluster and analyze the obstacle feature point dataset

. The specific methodological procedure is as follows [ 30 , 31 ]: MSER algorithm is used to extract the maximum stable extreme value region

. Regional range difference

is calculated for the two frames of images collected in the experiment.…”

Section: Methodsmentioning

confidence: 99%

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Ding

Zhang³

et al. 2023

Sensors

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show abstract

“…33–35 For CCSEM-EDX, an EDX spectrometer (EDAX, Inc.) was used to collect spectra for 10 176 total particles across all samples. Using K -mean clustering (elbow method), 36 particles were classified based on their elemental percentages of C, O, N, S, Na, Mg, Al, Si, P, Cl, K, Ca, Zn, Mn, and Fe. A k -mean clustering algorithm was used to guide the number of particle classes, and a rule-based algorithm (Fig.…”

Section: Methodsmentioning

confidence: 99%

Case study evaluation of size-resolved molecular composition and phase state of carbonaceous particles in wildfire influenced smoke from the Pacific Northwest

Vandergrift,

Lata,

Mathai

et al. 2023

Environ. Sci.: Atmos.

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“…Cell types were determined by minibatch K-means clustering of mean fluorescence intensity for each fluorophore, where the number of distinct cell clusters were selected by the Elbow method on the within-cluster sum of square curve. 15,16 Through this method 14 distinct cell clusters were identified in the broad immune panel and 13 cell clusters in the T-cell panel (Fig. 3A-B and Supplementary Fig.…”

Section: Unbiased Clustering Pipeline Identifies Distinct Immune Arch...mentioning

confidence: 99%

“…The clustering was performed for the number of cell types k = 5, 6, …, 20, with 10 random initializations for each choice of k. The final number of cell types was determined by the Elbow method on the within-cluster sum of square curve. 15 All gated cells were included in cell clustering without downsampling. Uniform manifold approximation and projection (UMAP) 48 was performed using…”

Section: Mini-batch K-means Cell Typingmentioning

confidence: 99%

High-Dimensional Spectral Cytometry Reveals Therapeutically Relevant Immune Subtypes in Gastric Cancer

Abate

Teng

Lin

et al. 2023

Preprint

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Identification of locally advanced gastric cancer (GC) patients who might potentially benefit from immune-based strategies is limited by both the poor predictive quality of existing biomarkers, including molecular subtypes, tumor mutational burden, and PD-L1 expression, as well as inadequate understanding of the gastric cancer immune microenvironment. Here, we leveraged high-dimensional spectral cytometry to re-classify locally advanced gastric tumors based on immune composition. The gastric cancer microenvironment was comprised of a diverse immune infiltrate including high proportions of plasmablasts, macrophages, and myeloid-derived suppressor cells. Computational cell typing and sample clustering based on tiered broad immune and T-cell focused phenotyping identified three distinct immune subtypes. The most immunogenic subtype exhibited high proportions of activated CD4+ T-cells and plasmablasts and included tumors that would have been classified as non-immunogenic based on prior classifications. Analysis of gastric cancer patients treated with immune checkpoint blockade indicates that patients who responded to immunotherapy had a pre-treatment tumor composition that corresponded to higher immune scores from our analysis. This work establishes a novel immunological classification of gastric cancer including identification of patients and immune networks likely to benefit from immune-based therapies.

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Application of K-Means Clustering Algorithm in Online English Learning Effect Evaluation

Cited by 22 publications

References 0 publications

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Obstacle Detection Method Based on RSU and Vehicle Camera Fusion

Case study evaluation of size-resolved molecular composition and phase state of carbonaceous particles in wildfire influenced smoke from the Pacific Northwest

High-Dimensional Spectral Cytometry Reveals Therapeutically Relevant Immune Subtypes in Gastric Cancer

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