Curvature Quantified Douglas-Peucker-based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications.In this work,an effective curvature quantified Douglas-Peucker(CQDP)-based PMU data compression method is proposed for situational awareness of power systems.First,a curvature integrated distance(CID)for measuring the local flection and fluc-tuation of PMU signals is developed.The Doug-las-Peucker(DP)algorithm integrated with a quan-tile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals.This allows adaptive adjustment of the al-gorithm parameters,so as to maintain the desired com-pression ratio and reconstruction accuracy as much as possible,irrespective of the power system dynamics.Fi-nally,case studies on the Western Electricity Coordinat-ing Council(WECC)179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method.The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system,and alleviates the compression performance degradation problem faced by existing compression methods.Index Terms—Curvature quantified Douglas-Peucker,data compression,phasor measurement unit,power sys-tem situational awareness.

Comparison of effect of locking compression plate internal fixation and reconstruction plate fixation for treatment of mid-shaft clavicular fracture

Objective:To compare the effect of locking compression plate internal fixation and reconstruction plate fixation for the treatment of mid-shaft clavicular fracture.Methods: In this study, the medical records of 68 patients with mid-shaft clavicular fractures treated in our department from May 2014 to May 2017 were retrospectively analyzed. 32 patients in the control group received the reconstruction plate fixation, while 36 patients in the observation group received the locking compression plate internal fixation. Patients were followed up for 12 months, thereafter, various indicators including operative indexes, Constant-Murley (CM) score, disability of arm shoulder and hand (DASH) score, activities of daily living (ADL), and complications were compared between groups.Results: The incision length, intraoperative blood loss, operation time, and fracture healing time of the observation group were significantly lower than the control group (P<0.05). The postoperative CM scores of the two groups were significantly increased, moreover, the scores of muscle strength, daily life and activity were significantly in the observation group were higher than those of the control group (P<0.05), and no difference was found in the pain degree (P>0.05). There was a decrease trend in the DASH score and an increase trend in the ADL score in both groups, while both indexes were better in the observation group than in the control group (P<0.05). The total incidence of complications in the observation group was significantly lower than that in the control group (8.33% vs 18.75%,P<0.05).Conclusion: Compared with the reconstruction plate fixation, locking compression plate internal fixation has the advantages of more mini invasive, better amelioration of shoulder joint function and less complication for patients with mid-shaft clavicular fractures.

Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.

Sparse Seismic Data Reconstruction Based on a Convolutional Neural Network Algorithm

At present,the acquisition of seismic data is developing toward high-precision and high-density methods.However,complex natural environments and cultural factors in many exploration areas cause difficulties in achieving uniform and intensive acquisition,which makes complete seismic data collection impossible.Therefore,data reconstruction is required in the processing link to ensure imaging accuracy.Deep learning,as a new field in rapid development,presents clear advantages in feature extraction and modeling.In this study,the convolutional neural network deep learning algorithm is applied to seismic data reconstruction.Based on the convolutional neural network algorithm and combined with the characteristics of seismic data acquisition,two training strategies of supervised and unsupervised learning are designed to reconstruct sparse acquisition seismic records.First,a supervised learning strategy is proposed for labeled data,wherein the complete seismic data are segmented as the input of the training set and are randomly sampled before each training,thereby increasing the number of samples and the richness of features.Second,an unsupervised learning strategy based on large samples is proposed for unlabeled data,and the rolling segmentation method is used to update(pseudo)labels and training parameters in the training process.Through the reconstruction test of simulated and actual data,the deep learning algorithm based on a convolutional neural network shows better reconstruction quality and higher accuracy than compressed sensing based on Curvelet transform.

Compression and reconstruction of speech signals based on compressed sensing

Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.

Fast magnetic reconstruction of the portal vein with allogeneic blood vessels in canines

BACKGROUND： The resection and reconstruction of large vessels, including the portal vein, are frequently needed in tumor resection. Warm ischemia before reconstruction might have deleterious effects on the function of some vital organs and therefore, how to reconstruct the vessels quickly after resection is extremely important. The present study was to introduce a new type of magnetic compression anastomosis （MCA） device to establish a quick non-suture anastomosis of the portal vein after resection in canines.

Seismic data reconstruction based on CS and Fourier theory

Traditional seismic data sampling follows the Nyquist sampling theorem. In this paper, we introduce the theory of compressive sensing （CS）, breaking through the limitations of the traditional Nyquist sampling theorem, rendering the coherent aliases of regular undersampling into harmless incoherent random noise using random undersampling, and effectively turning the reconstruction problem into a much simpler denoising problem. We introduce the projections onto convex sets （POCS） algorithm in the data reconstruction process, apply the exponential decay threshold parameter in the iterations, and modify the traditional reconstruction process that performs forward and reverse transforms in the time and space domain. We propose a new method that uses forward and reverse transforms in the space domain. The proposed method uses less computer memory and improves computational speed. We also analyze the antinoise and anti-aliasing ability of the proposed method, and compare the 2D and 3D data reconstruction. Theoretical models and real data show that the proposed method is effective and of practical importance, as it can reconstruct missing traces and reduce the exploration cost of complex data acquisition.

Isotropic compression behavior of granular assembly with non-spherical particles by X-ray micro-computed tomography and discrete element modeling

The particle morphological properties,such as sphericity,concavity and convexity,of a granular assembly significantly affect its macroscopic and microscopic compressive behaviors under isotropic loading condition.However,limited studies on investigating the microscopic behavior of the granular assembly with real particle shapes under isotropic compression were reported.In this study,X-ray computed tomography(mCT)and discrete element modeling(DEM)were utilized to investigate isotropic compression behavior of the granular assembly with regard to the particle morphological properties,such as particle sphericity,concavity and interparticle frictions.The mCT was first used to extract the particle morphological parameters and then the DEM was utilized to numerically investigate the influences of the particle morphological properties on the isotropic compression behavior.The image reconstruction from mCT images indicated that the presented particle quantification algorithm was robust,and the presented microscopic analysis via the DEM simulation demonstrated that the particle surface concavity significantly affected the isotropic compression behavior.The observations of the particle connectivity and local void ratio distribution also provided insights into the granular assembly under isotropic compression.Results found that the particle concavity and interparticle friction influenced the most of the isotropic compression behavior of the granular assemblies.

Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction

To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.

Application of 3D Digital Reconstruction and Printing to the Diagnosis and Treatment of Iliac Vein Compression

The objective of this research was to explore the feasibility and clinical application of a new diagnostic imaging method for the diagnosis and treatment of iliac vein compression(IVC)based on three-dimensional(3D)digital reconstruction and printing.This study included patients with chronic venous disease(CVD)who were admitted to the Department of Vascular Surgery,Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine,from January to March,2019,and underwent computed tomography venography(CTV)to detect IVC.CTV findings were used to reconstruct 3D-printed models of blood vessels.A total of 17 patients(5 men and 12 women)with IVC,who were primarily diagnosed with CTV,were included in this study.In addition,24 significant venous compression sites were found in 17 patients,of which 7 patients had only one compression site(41.2%),nine patients had two compression sites(52.9%),and one patient had three compression sites(5.9%).3D digital reconstruction and printing is a convenient,noninvasive,and accurate diagnostic imaging method that provides a clear and accurate evaluation of veins and arteries,as well as the anatomical positional relationship for the diagnosis and treatment of IVC.

Research on Deformation Reconstruction Based on Structural Curvature of CFRP Propeller With Fiber Bragg Grating Sensor Network

The deformation and reconstruction of the composite propeller under the static load in the laboratory is studied so as to provide the basic research for the deformation and reconstruction of the underwater deformed propeller.The fiber Bragg grating(FBG)sensor is proposed to be used for strain monitoring and deformation reconstruction of the carbon fiber reinforced polymer(CFRP)propeller,and a reconstruction algorithm of structural curvature deformation of the CFRP propeller based on strain information is presented.The reconstruction algorithm is verified by using variable-thickness CFRP laminates in the finite element software.The results show that the relative error of the reconstruction algorithm is within 8%.Then,an experimental system of strain monitoring and deformation reconstruction for the CFRP propeller based on the FBG sensor network is built.The propeller blade is loaded in the form of the cantilever beam,and the blade deformation is reconstructed by the strain measured by the FBG sensor network.Compared with the blade deformation measured by three coordinate scanners,the reconstruction relative error is within 15%.

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.

A FAST CONVERGING SPARSE RECONSTRUCTION ALGORITHM IN GHOST IMAGING

A fast converging sparse reconstruction algorithm in ghost imaging is presented. It utilizes total variation regularization and its formulation is based on the Karush-Kuhn-Tucker (KKT) theorem in the theory of convex optimization. Tests using experimental data show that, compared with the algorithm of Gradient Projection for Sparse Reconstruction (GPSR), the proposed algorithm yields better results with less computation work.

A sparsity adaptive compressed signal reconstruction based on sensing dictionary

Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.

Seismic data reconstruction based on iterative linear expansion of thresholds

Based on the compressive sensing,a novel algorithm is proposed to solve reconstruction problem under sparsity assumptions.Instead of estimating the reconstructed data through minimizing the objective function,the authors parameterize the problem as a linear combination of few elementary thresholding functions,which can be solved by calculating the linear weighting coefficients.It is to update the thresholding functions during the process of iteration.The advantage of this method is that the optimization problem only needs to be solved by calculating linear coefficients for each time.With the elementary thresholding functions satisfying certain constraints,a global convergence of the iterative algorithm is guaranteed.The synthetic and the field data results prove the effectiveness of the proposed algorithm.

Compressed sensing sparse reconstruction for coherent field imaging

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant in- fluence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.

A NOVEL METHOD TO REALIZE COMPRESSED VIDEO SUPER-RESOLUTION RECONSTRUCTION

This letter proposes a novel method of compressed video super-resolution reconstruction based on MAP-POCS (Maximum Posterior Probability-Projection Onto Convex Set). At first assuming the high-resolution model subject to Poisson-Markov distribution, then constructing the projecting convex based on MAP. According to the characteristics of compressed video, two different convexes are constructed based on integrating the inter-frame and intra-frame information in the wavelet-domain. The results of the experiment demonstrate that the new method not only outperforms the traditional algorithms on the aspects of PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Square Error) and reconstruction vision effect, but also has the advantages of rapid convergence and easy extension.

Clinical Study on CT-guided Modified Akupotomye in the Treatment of Lumbar Nerve Posterior Branch Compression

Objective: To observe the clinical effect of modified akupotomye closed lysis under CT guidance on compression of posterior lumbar nerve branch.Methods: Patients were diagnosed by HRCT 3-D reconstruction combined with clinical symptoms and signs.After HRCT three-dimensional reconstruction combined with clinical symptoms and signs, the patients were confirmed as posterior lumbar nerve compression.After CT accurate surface positioning, CT-guided modified akupotomye was used for closed lysis of the posterior lumbar nerve branch.Oswestry Dysfunction Index Questionnaire(ODI) was used for quantitative scoring, 7 days before and after treatment and 6 months after treatment.Results: In 62 cases, 20 cases were cured, with 25 cases markedly effective, 11 cases effective, and 36 cases ineffective.The total effective rate was 90.3%.ODI score: Self-paired t test 7 days before after treatment, P < 0.01;Before treatment and 6 months after treatment, self-paired t test(P < 0.01);Self-paired t-test was performed 7 days after treatment and 6 months after treatment(P > 0.05).Conclusion: With CT precise positioning, the modified akupotomye can be used to do closed lysis, to relieve the adhesion and compression, so that the low back pain can be relieved, with good clinical.The akupotomye closed lysis, combined with modern imaging technology has not only achieved good clinical effect, but also can improve the accuracy, safety and scientificity of akupotomye treatment.

Hybrid reconstruction algorithm for computed tomography based on diagonal total variation

Inspired by total variation(TV), this paper represents a new iterative algorithm based on diagonal total variation(DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reconstructed image, we used DTV to sparsely represent images when iterative convergence of the reconstructed algorithm with TV-constraint had no effect during the reconstruction process. To investigate our proposed algorithm, the numerical and experimental studies were performed, and rootmean-square error(RMSE) and structure similarity(SSIM)were used to evaluate the reconstructed image quality. The results demonstrated that the proposed method could effectively reduce noise, suppress artifacts, and reconstruct highquality image from incomplete projection data.