128 research outputs found
A theoretical model for predicting the Peak Cutting Force of conical picks
In order to predict the PCF (Peak Cutting Force) of conical pick in rock cutting process, a theoretical model is established based on elastic fracture mechanics theory. The vertical fracture model of rock cutting fragment is also established based on the maximum tensile criterion. The relation between vertical fracture angle and associated parameters (cutting parameter and ratio B of rock compressive strength to tensile strength) is obtained by numerical analysis method and polynomial regression method, and the correctness of rock vertical fracture model is verified through experiments. Linear regression coefficient between the PCF of prediction and experiments is 0.81, and significance level less than 0.05 shows that the model for predicting the PCF is correct and reliable. A comparative analysis between the PCF obtained from this model and Evans model reveals that the result of this prediction model is more reliable and accurate. The results of this work could provide some guidance for studying the rock cutting theory of conical pick and designing the cutting mechanism
Nonlinear dynamic characteristics of load time series in rock cutting
The characteristics of the cutting load time series were investigated using chaos and fractal theories to study the information and dynamic characteristics of rock cutting. The following observations were made after analyzing the power spectrum, denoising phase reconstruction, correlation dimension and maximum Lyapunov exponent of the time series. A continuous broadband without a significant dominant frequency was found in the power spectrum. The restructured phase space presented a distinct strange attractor after wavelet denoising. The correlation dimension was saturated at an embedding dimension of 7. Lastly, and the maximum Lyapunov exponent exceeded 0 via the small data method. These findings reflected the chaotic dynamic characteristics of the cutting load time series. The box dimensions of the cutting load were further investigated under different conditions, and the difference in cutting depth, cutting velocity and assisted waterjet types were found to be ineffective in changing the fractal characteristic. As cutting depth become small, rock fragment size also decreased, whereas fractal dimension increased. Moreover, a certain range of cutting velocity increased fragment size but decreased fractal dimension. Therefore, fractal dimension could be regarded as an evaluation index to assess the extent of rock fragmentation. The rock-cutting mechanism remained unchanged under different assisted waterjet types. The waterjet front cutter impacts and damages rock, however, the waterjet behind of cutter is mainly used to clean fragments and to lubricate the cutter
Numerical simulation of rock fragmentation process by roadheader pick
A numerical model of rock fragmentation caused by a roadheader pick was established based on the particle flow code in two dimensions to study the rock fragmentation mechanism of the roadheader pick. The model simulated crack initiation, propagation and chip formation. The feasibility and reliability of the method as well as numerical model were verified by experiment. Results show that the rock fragmentation process includes three stages: crack initiation, crushing zone and radial crack formation, major tensile crack propagation and rock fragment formation. The crushing zone, number of radial cracks, specific energy consumption of rock cutting and dust level increase as the pick-tip corner radius increases. Consequently the pick-tip corner radius should range from 0 to 2 mm to obtain large rock fragments and low specific energy consumption. The damage of medium-hard and hard rock by the roadheader pick is more remarkable than that of soft rock. Furthermore, the sharp pick is suitable for the soft rock, whereas the pick tip with a proper rounding corner is perfect for the medium-hard and hard rock
A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals
Precise segmentation is a vital first step to analyze semantic information of
cardiac cycle and capture anomaly with cardiovascular signals. However, in the
field of deep semantic segmentation, inference is often unilaterally confounded
by the individual attribute of data. Towards cardiovascular signals,
quasi-periodicity is the essential characteristic to be learned, regarded as
the synthesize of the attributes of morphology (Am) and rhythm (Ar). Our key
insight is to suppress the over-dependence on Am or Ar while the generation
process of deep representations. To address this issue, we establish a
structural causal model as the foundation to customize the intervention
approaches on Am and Ar, respectively. In this paper, we propose contrastive
causal intervention (CCI) to form a novel training paradigm under a frame-level
contrastive framework. The intervention can eliminate the implicit statistical
bias brought by the single attribute and lead to more objective
representations. We conduct comprehensive experiments with the controlled
condition for QRS location and heart sound segmentation. The final results
indicate that our approach can evidently improve the performance by up to 0.41%
for QRS location and 2.73% for heart sound segmentation. The efficiency of the
proposed method is generalized to multiple databases and noisy signals.Comment: submitted to IEEE Journal of Biomedical and Health Informatics
(J-BHI
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning
Electrocardiogram (ECG) monitoring is one of the most powerful technique of
cardiovascular disease (CVD) early identification, and the introduction of
intelligent wearable ECG devices has enabled daily monitoring. However, due to
the need for professional expertise in the ECGs interpretation, general public
access has once again been restricted, prompting the need for the development
of advanced diagnostic algorithms. Classic rule-based algorithms are now
completely outperformed by deep learning based methods. But the advancement of
smart diagnostic algorithms is hampered by issues like small dataset,
inconsistent data labeling, inefficient use of local and global ECG
information, memory and inference time consuming deployment of multiple models,
and lack of information transfer between tasks. We propose a multi-resolution
model that can sustain high-resolution low-level semantic information
throughout, with the help of the development of low-resolution high-level
semantic information, by capitalizing on both local morphological information
and global rhythm information. From the perspective of effective data leverage
and inter-task knowledge transfer, we develop a parameter isolation based ECG
continual learning (ECG-CL) approach. We evaluated our model's performance on
four open-access datasets by designing segmentation-to-classification for
cross-domain incremental learning, minority-to-majority class for category
incremental learning, and small-to-large sample for task incremental learning.
Our approach is shown to successfully extract informative morphological and
rhythmic features from ECG segmentation, leading to higher quality
classification results. From the perspective of intelligent wearable
applications, the possibility of a comprehensive ECG interpretation algorithm
based on single-lead ECGs is also confirmed.Comment: 10 page
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition
Automatic emotion recognition based on multichannel Electroencephalography
(EEG) holds great potential in advancing human-computer interaction. However,
several significant challenges persist in existing research on algorithmic
emotion recognition. These challenges include the need for a robust model to
effectively learn discriminative node attributes over long paths, the
exploration of ambiguous topological information in EEG channels and effective
frequency bands, and the mapping between intrinsic data qualities and provided
labels. To address these challenges, this study introduces the
distribution-based uncertainty method to represent spatial dependencies and
temporal-spectral relativeness in EEG signals based on Graph Convolutional
Network (GCN) architecture that adaptively assigns weights to functional
aggregate node features, enabling effective long-path capturing while
mitigating over-smoothing phenomena. Moreover, the graph mixup technique is
employed to enhance latent connected edges and mitigate noisy label issues.
Furthermore, we integrate the uncertainty learning method with deep GCN weights
in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We
evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for
emotion recognition tasks. The experimental results demonstrate the superiority
of our methodology over previous methods, yielding positive and significant
improvements. Ablation studies confirm the substantial contributions of each
component to the overall performance.Comment: 10 page
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