A note on the total chromatic number of Halin graphs with maximum degree 4

AbstractIn this paper, we prove that χT(G) = 5 for any Halin graph G with Δ(G) = 4, where Δ(G) and χT(G) denote the maximal degree and the total chromatic number of G, respectively

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

Toward Full-Stack Acceleration of Deep Convolutional Neural Networks on FPGAs

Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a growing demand for hardware accelerators that accommodate a variety of CNNs to improve their inference latency and energy efficiency, in order to enable their deployment in real-time applications. Among popular platforms, field-programmable gate arrays (FPGAs) have been widely adopted for CNN acceleration because of their capability to provide superior energy efficiency and low-latency processing, while supporting high reconfigurability, making them favorable for accelerating rapidly evolving CNN algorithms. This article introduces a highly customized streaming hardware architecture that focuses on improving the compute efficiency for streaming applications by providing full-stack acceleration of CNNs on FPGAs. The proposed accelerator maps most computational functions, that is, convolutional and deconvolutional layers into a singular unified module, and implements the residual and concatenative connections between the functions with high efficiency, to support the inference of mainstream CNNs with different topologies. This architecture is further optimized through exploiting different levels of parallelism, layer fusion, and fully leveraging digital signal processing blocks (DSPs). The proposed accelerator has been implemented on Intel's Arria 10 GX1150 hardware and evaluated with a wide range of benchmark models. The results demonstrate a high performance of over 1.3 TOP/s of throughput, up to 97% of compute [multiply-accumulate (MAC)] efficiency, which outperforms the state-of-the-art FPGA accelerators