A new biprimary color system is demonstrated for single‐layer reflective displays, capturing much of the improved color performance of multilayer displays while potentially maintaining single‐layer display advantages in high resolution and faster switching. Electrophoretic pixels were operated with dual‐particle complementary‐colored dispersions such as green/magenta (G/M). Using simple interdigitated three‐electrode architecture, four colored states (KWGM) were achieved with a preliminary contrast ratio of 10 : 1. Furthermore, biprimary ink dispersions were shown to be functional in a more advanced electrokinetic pixel structure. A full‐color biprimary pixel contains three complementary subpixels (G/M, B/Y, R/C), and the requisite electrophoretic ink dispersions were also formulated and spectrally characterized in this work. Lastly, theoretical color space mapping confirms that the biprimary concept provides twice the brightness and twice the color fraction compared with the conventional RGBW subpixel approach, and that the biprimary concept can approach performance close to that of magazine print (Specifications for Web‐Offset Print).
Purpose The purpose of this study was to reduce the experience dependence during the orthognathic surgical planning that involves virtually simulating the corrective procedure for jaw deformities. Methods We introduce a geometric deep learning framework for generating reference facial bone shape models for objective guidance in surgical planning. First, we propose a surface deformation network to warp a patient's deformed bone to a set of normal bones for generating a dictionary of patient‐specific normal bony shapes. Subsequently, sparse representation learning is employed to estimate a reference shape model based on the dictionary. Results We evaluated our method on a clinical dataset containing 24 patients, and compared it with a state‐of‐the‐art method that relies on landmark‐based sparse representation. Our method yields significantly higher accuracy than the competing method for estimating normal jaws and maintains the midfaces of patients’ facial bones as well as the conventional way. Conclusions Experimental results indicate that our method generates accurate shape models that meet clinical standards.
The magnetic flux leakage (MFL) evaluation is often used for the overhauling of oil extracting operation in the oil field to realize the real-time damage assessment of the pipeline. Since the MFL signal is affected by various noise sources in the field, this paper introduces the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On the basis of this, a particle swarm optimization wavelet threshold (PSO-WT) method is proposed, and the signal reconstruction option is improved to extract the leakage magnetic flux signal of tubing defects. First, CEEMDAN is used to add pairs of positive and negative white noise to the MFL signal, and then the signal is decomposed into several intrinsic mode functions (IMFs). Second, the correlation coefficient selection limit is defined. Taking into account the characteristics of the decomposed signal, the useless IMFs and useful IMFs are selected from the IMF components, where some of the useful IMF components contain less noise. Third, the PSO-WT algorithm is combined to further filter the noisy and useful IMF components. Finally, the filtered IMF components and the pure useful IMF components are selected to reconstruct the signal. In the experiment, the ensemble empirical mode decomposition (EEMD) method and CEEMDAN are used to decompose the noisy MFL signals ensemble in the field. The MFL signal is reconstructed under the correlation coefficient selection. It can be seen from the comparison of EEMD that the MFL signal is reconstructed under the same conditions after CEEMDAN decomposition, and its signal-to-noise ratio is increased by 8%. At the same time, after CEEMDAN decomposition, the selected noisy useful IMFs are further filtered by the wavelet threshold (WT) method and the PSO-WT method. Also, it indicates that the reconstructed signal processed by PSO-WT is 17% higher than the reconstructed signal after WT processing.
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