Fast t-SNE algorithm with forest of balanced LSH trees and hybrid computation of repulsive forces

Orliński, Marek; Jankowski, Norbert

doi:10.1016/j.knosys.2020.106318

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…After being adapted by JDA, the size of 315 × 60 feature set initially extracted from three tools can be converted to the size of 315 × 4 feature set. In order to intuitively compare the change characteristics of the two features before and after feature adaptation, the t-distributed stochastic neighbor embedding (t-SNE (Orliński and Jankowski, 2022)) is used to visualize the feature sets of three tool wear samples before and after adaptation. The results are shown in Figures 6 and 7.…”

Section: Discussionmentioning

confidence: 99%

A hybrid tool wear prediction model based on JDA

Huang,

Yu,

Yao

et al. 2024

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PurposeAiming at solving the problems of low prediction accuracy and poor generalization caused by the difference in tool wear data distribution and the fixation of single global model parameters, a hybrid prediction modeling method for tool wear based on joint distribution adaptation (JDA) is proposed.Design/methodology/approachFirstly, JDA is exploited to adapt the data features with different data distributions. Then, the adapted data features are identified by the KNN classifier. Finally, according to the tool state classification results, different regression prediction models are assigned to different wear stages to complete the whole tool wear prediction task.FindingsThe results of milling experiments show that the maximum prediction accuracy of this method is 95.13%, and it has good recognition accuracy and generalization performance. Through the application of the tool wear hybrid prediction modeling method, the prediction accuracy and generalization performance of the model are improved and the tool monitoring is realized.Originality/valueThe research results can provide solutions and a theoretical basis for the application of tool wear monitoring technology in practical industrial applications.

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Section: Discussionmentioning

confidence: 99%

A hybrid tool wear prediction model based on JDA

Huang,

Yu,

Yao

et al. 2024

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“…This paper proposes the use of gradient for trajectory optimization, which can be seen as solving extreme value problems of multivariate objective functions 28 . The optimal approach for solving such unconstrained optimization problems is to use the Newton method.…”

Section: Optimization Of Trajectorymentioning

confidence: 99%

Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs

Sun,

Ding

et al. 2024

Sci Rep

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Unmanned aerial vehicles (UAVs) have become the focus of current research because of their practicability in various scenarios. However, current local path planning methods often result in trajectories with numerous sharp or inflection points, which are not ideal for smooth UAV flight. This paper introduces a UAV path planning approach based on distance gradients. The key improvements include generating collision-free paths using collision information from initial trajectories and obstacles. Then, collision-free paths are subsequently optimized using distance gradient information. Additionally, a trajectory time adjustment method is proposed to ensure the feasibility and safety of the trajectory while prioritizing smoothness. The Limited-memory BFGS algorithm is employed to efficiently solve optimal local paths, with the ability to quickly restart the trajectory optimization program. The effectiveness of the proposed method is validated in the Robot Operating System simulation environment, demonstrating its ability to meet trajectory planning requirements for UAVs in complex unknown environments with high dynamics. Moreover, it surpasses traditional UAV trajectory planning methods in terms of solution speed, trajectory length, and data volume.

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“…To better visualize the final embeddings obtained by the last layers of the different models, a 2D projected representation of them were computed using the so-called T-SNE algorithm, updated in [30] with a level of perplexity equal to 80.…”

Section: B Self-supervised User Embeddingmentioning

confidence: 99%

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

et al. 2021

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During the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well as sensitive content throughout the network has fostered applied research to automatically measure the reliability of social networks accounts via Artificial Intelligence (AI). In this paper, we present a multilingual approach for addressing the bot identification task in Twitter via Deep learning (DL) approaches to support end-users when checking the credibility of a certain Twitter account. To do so, several experiments were conducted using state-of-the-art Multilingual Language Models to generate an encoding of the text-based features of the user account that are later on concatenated with the rest of the metadata to build a potential input vector on top of a Dense Network denoted as Bot-DenseNet. Consequently, this paper assesses the language constraint from previous studies where the encoding of the user account only considered either the metadata information or the metadata information together with some basic semantic text features. Moreover, the Bot-DenseNet produces a low-dimensional representation of the user account which can be used for any application within the Information Retrieval (IR) framework.

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Fast t-SNE algorithm with forest of balanced LSH trees and hybrid computation of repulsive forces

Cited by 8 publications

References 11 publications

A hybrid tool wear prediction model based on JDA

A hybrid tool wear prediction model based on JDA

Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs

A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

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