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In the machining industry, tool wear has a great influence on machining efficiency, product quality, and production costs. To achieve accurate tool wear estimation, a novel CNN-transformer neural network (CTNN) model is proposed in this paper. In the CTNN model, the transformer model and convolutional neural networks (CNN) are used to process condition monitoring (CM) data in parallel, such as cutting force. The motivations are as follows. For one thing, both the transformer model and CNN can extract useful temporal features from CM data, and the learned temporal features by these two parts are fused to achieve accurate tool wear estimation. For another, CNN contributes to enhancing the transformer’s ability to capture the sequence order. In addition, data noise introduces the aleatoric uncertainty to the estimation results. To quantify the aleatoric uncertainty, a negative log-likelihood loss function is employed to enable the model to output the probabilistic distribution associated with tool wear. In such cases, the model outputs both the tool wear and variance, and the variance is learned within the model in an unsupervised manner. Finally, the effectiveness and superiority of the proposed method are validated on a public milling dataset. It is found by experiments that both the transformer model and CNN play important roles in tool wear estimation, and better performance can be obtained when they are used in parallel. In summary, the experimental results suggest that the proposed model can obtain promising results in tool wear estimation.

We report the most sensitive upper limits to date on the 21 cm epoch of reionization power spectrum using 94 nights of observing with Phase I of the Hydrogen Epoch of Reionization Array (HERA). Using similar analysis techniques as in previously reported limits, we find at 95% confidence that Δ2(k = 0.34 h Mpc−1) ≤ 457 mK2 at z = 7.9 and that Δ2(k = 0.36 h Mpc−1) ≤ 3496 mK2 at z = 10.4, an improvement by a factor of 2.1 and 2.6, respectively. These limits are mostly consistent with thermal noise over a wide range of k after our data quality cuts, despite performing a relatively conservative analysis designed to minimize signal loss. Our results are validated with both statistical tests on the data and end-to-end pipeline simulations. We also report updated constraints on the astrophysics of reionization and the cosmic dawn. Using multiple independent modeling and inference techniques previously employed by HERA Collaboration, we find that the intergalactic medium must have been heated above the adiabatic cooling limit at least as early as z = 10.4, ruling out a broad set of so-called “cold reionization” scenarios. If this heating is due to high-mass X-ray binaries during the cosmic dawn, as is generally believed, our result’s 99% credible interval excludes the local relationship between soft X-ray luminosity and star formation and thus requires heating driven by evolved low-metallicity stars.

Abstract:Visible light communication (VLC) is recommended for indoor transmissions in 5G network, whereby DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) is adopted to eliminate the inter-symbol interference (ISI) but suffers from considerable performance loss induced by clipping distortion. In this paper, bit-interleaved coded modulation with iterative demapping and decoding (BICM-ID) scheme for clipped DCO-OFDM is investigated to enhance the performance of VLC systems. In order to further mitigate the clipping distortions, a novel soft demapping criterion is proposed, and a simplified demapping algorithm is developed to reduce the complexity of the proposed criterion. Simulation results illustrate that the enhanced demapping algorithm achieves a significant performance gain. 1971-1978 (2015). 9. T. Mao, Z. Wang, Q. Wang, and L. Dai, "Ellipse-based DCO-OFDM for visible light communications," Opt.Commun. 360, 1-6 (2016). 10. Z. Wang, Q. Wang, S. Chen and L. Hanzo, "An adaptive scaling and biasing scheme for OFDM-based visible light communication systems," Opt. Express 22(10), 12707-12715 (2014). 11. J. Tan, Z. Wang, Q. Wang, and L. Dai, "Near-optimal low-complexity sequence detection for clipped DCO-OFDM," IEEE Photon. Technol. Lett 28(3), 233-236 (2016). 12. S. ten Brink, J. Speidel, and R. H. Yan, "Iterative demapping and decoding for multilevel modulation," in Proc.Globecom. 579-584 (1998 36-45 (2015). 14. P. Robertson, E. Villebrun, and P. Hoeher, "A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain," in Proc. ICC, 1009ICC, -1013ICC, (1995. 15. J. Tan, Z. Wang, C. Qian, Z. Wang, S. Chen, and L. Hanzo, "A reduced-complexity demapping algorithm for BICM-ID systems," IEEE Trans. Veh. Technol. 64(9), 4350-4356 (2015). 16. S. ten Brink, "Convergence behavior of iteratively decoded parallel concatenated codes," IEEE Trans. Commun.

Abstract-The inherent high peak-to-average power ratio issue of dc-biased optical orthogonal frequency division multiplexing (DCO-OFDM) is sensitive to the limited dynamic region of light emitting diode component and prone to clipping distortion, which deteriorates the performance of visible light communication systems. This letter proposes a maximum likelihood sequence detection (MLSD) method for the clipped DCO-OFDM, whereas the double-sided clipping characteristic is incorporated to improve the performance. Besides that, a near-optimal low-complexity MLSD method is presented to reduce the calculation complexity. Simulations demonstrate that the proposed low-complexity MLSD receiver could approach the performance of ideal case of non-clipped DCO-OFDM.Index Terms-Visible light communication (VLC), dc-biased orthogonal frequency-division multiplexing (DCO-OFDM), maximum likelihood sequence detection (MLSD), clipping distortion.