Coatings based on polysulfobetaine polymers are being developed as environmentally benign, fouling-resistant marine coatings. Poly(sulfobetaine methacrylate) (polySBMA) brushes were grafted onto glass surfaces using surface-initiated atom transfer radical polymerization (ATRP). The settlement, growth, and attachment strength of marine algae were investigated on polySBMA-coated surfaces. Results showed that few spores of the green marine alga, Ulva, settled (attached) on the polySMBA surfaces, and the adhesion strength of both spores and sporelings (young plants) was low. Diatoms were also mostly unable to adhere to the polySMBA surfaces. Assays demonstrated that SBMA polymers in solution were not toxic. The data are discussed in terms of the interfacial properties presented by the polySMBA surfaces. Zwitterionic polymers and coatings exhibit great advantages for their effectiveness to resist marine fouling while being environmentally benign and are promising as ultralow fouling marine coatings.
In the practice of dentistry, oral dental CT images are frequently used to assist doctors in diagnosis. Filtered back projection (FBP) technique is widely employed in practice for the reconstruction of CT images obtained from X-ray calculations. However, when metal objects occur in a patient's oral cavity, the CT images would show density discontinuities due to the metals' "X-ray absorption coefficient is much larger than human tissues. When the FBP algorithm is applied to CT images with metals, severe metal artifacts would be obtained, which significantly reconstructed images. Therefore, metal artifact reduction (MAR) work is becoming an important problem in dentistry image processing. In this paper, we propose a novel iterative sinogram metal artifact reduction model (IS-MARM) to solve the problem. Inspired by the Diffusion model, we propose a new method to reduce metal artifacts and interpolate new data in sinogram of dentistry images iteratively. This approach reduces the difficulty of model learning and achieves good results. Secondly, we proposed a new simple method of iterative data generating to simulate real-world metals in CT sinogram images. Finally, we have demonstrated the effectiveness of our method through experiments on dental CT MAR work.
Acoustic emission (AE) based condition monitoring is a popular method to inspect a material health condition in many areas[ 1 ]. It monitors irreversible structure changes of material through radiation of acoustic waves. Typically, the structure change generates a typical spectrum of acoustic waves starting at 1 kHz, and falling off at several MHz, which can be detected and analyzed via an AE system. Meanwhile, AE signals convey massive information such as rise time, AE energy, AE peak frequency, which are expressive of the failure process. AE based condition monitoring technology exhibits excellent capability of detecting, positioning, and characterizing damage and has been wildly used in monitoring of inner structure changes in bridges, pressure containers, and pipeline systems. In the last decade, the AE technology has been applied to condition monitoring of power modules and shows a promising application scenario in the power electronics field, which help us acquire accurate failure information and making precise perdition of a failure in the power module[ 2 ]. In this work, we firstly combined the AE monitoring and a deep learning model - Gated Recurrent Unit (GRU) to monitor and predict the failure of power module as shown in Figure 1[ 3 ]. The proposed method integrally analyzes and summarizes the patterns from the time-series data obtained during power cycle tests of power modules and predicts the failures. Specifically, the time-series data consisting of junction temperature, electric power, thermal resistance, and several AE parameters were obtained from AE sensor data and power cycling test. The proposed method (a) automatically extracts common or different time-series patterns among devices and estimates device states, and (b) effectively predicts failures by switching prediction models according to changes in device states. By time-series analysis of AE sensor data, it was confirmed that the AE parameters obtained from the AE sensor data capture the progression and signs of failures. In addition, by verifying the effectiveness of the time-series pattern detection, it was confirmed that the proposed method can accurately extract the timeseries patterns during normal conditions, the time-series patterns that show signs of failure, and the time-series patterns after failure. Furthermore, by verifying the prediction accuracy and learning efficiency, it was showed that the proposed method is capable of faster learning while retaining the same or better performance compared to existing methods. Reference [1] A. Nair and C. Cai, "Acoustic emission monitoring of bridges: Review and case studies," Eng Struct, vol. 32, no. 6, pp. 1704-1714, 2010. [2] S. Müller, C. Drechsler, U. Heinkel, and C. Herold, "Acoustic emission for state-of-health determination in power modules," in 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD) , 2016: IEEE, pp. 468-471. [3] R. Dey and F. M. Salem, "Gate-variants of gated recurrent unit (GRU) neural networks," in 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS) , 2017: IEEE, pp. 1597-1600.
Electron paramagnetic resonance imaging (EPRI) is a rising technique for preclinical imaging of small animals. The technique uses paramagnetic spin contrast materials to determine the spectral-spatial (SS) distribution of materials within the subject. A widely used EPRI modality employs continuous wave (CW) scanning scheme with Zeeman modulation (ZM). The imaging model in this technique can be related to the Radon transform (RT) of the SS image, and image reconstruction is equivalent to reconstruction from RT data. However, data collection is limited by the finite strength of the magnetic field gradient applied to the subject, and there is a desire to speed up scanning by collecting data only over a limited-angular range (LAR). In this study, we tailor a recently developed DTV algorithm in CT to investigate accurate image reconstruction from RT over LARs in EPRI. The results show that the DTV algorithm can be adapted for image reconstruction of quality comparable to that of images reconstructed from full-angular range (FAR) data, suggesting that algorithms can be developed to enable LAR scanning in CW-ZM EPRI with reduced imaging time.
A new theoretical model of variable stiffness composite structures in this paper is presented, then a finite element model is established by ABAQUS and a test specimen is manufactured to verify the accuracy. Firstly, the out-of-plane displacement of the initial and second stable configuration of theoretical and simulation results for two kinds of structures (Orthogonal/Antisymmetric/Orthogonal and Antisymmetric/Orthogonal/Antisymmetric) are compared. Secondly, the force-displacement of variable stiffness composite is investigated by the numerical and experimental methods. Reasonable agreement can be obtained from the results of theoretical analysis, finite element method and experiment test. Thirdly, the effect of material parameters on out-of-plane displacement and force-displacement are discussed. It is shown that we can use the current model to predict the equilibrium configurations and load condition of a variable stiffness composite structures.
Because the magnetic signal information of pipeline defects obtained by magnetic flux leakage detection contains interference signals, it is difficult to accurately extract the features. Therefore, a novel pipeline defect feature extraction method based on VMD-OSVD (variational modal decomposition - optimal singular value decomposition) is proposed to promote the signal to noise ratio (SNR) and reduce aliasing in the frequency domain. By using the VMD method, the sampled magnetic signal is decomposed, and the optimal variational mode is selected according to the rate of relative change (VMK) of Shannon entropy (SE) to reconstruct the signal. After that, SVD algorithm is used to filter the reconstructed signal again, in which the H-matrix is optimized with the phase-space matrix to enhance SNR and decrease the frequency domain aliasing. The results show that the method has excellent denoising ability for defect magnetic signals, and SNR is increased by 21.01%, 24.04%, 0.96%, 32.14%, and 20.91%, respectively. The improved method has the best denoising effect on transverse mechanical scratches, but a poor denoising effect on spiral welding position. In the frequency domain, the characteristics of different defects are varied, and their corresponding frequency responses are spiral weld corrosion > transverse mechanical cracking > girth weld > deep hole > normal pipe. The high-frequency band is the spiral weld corrosion with f1 = 153.37 Hz. The low-frequency band is normal with f2 = 1 Hz. In general, the VMD-OSVD method is able to improve the SNR of the signal and characterize different pipe defects. And it has a certain guiding significance to the application of pipeline inspection in the field of safety in the future.
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