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.

Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical professionals.To address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic mapping.Subsequently,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling point.The Daisy descriptor matrix for this point is then computed.Finally,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical image.This scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image watermarking.The embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational overhead.Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

Rate-limiting factors in hydrate decomposition through depressurization across various scales:A mini-review

Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the challenges of long-term preparation,high costs and associated risks.Experimental studies,on the other hand,offer a safe and cost-effective means of exploring the mechanisms of hydrate dissociation and optimizing exploitation conditions.Gas hydrate decomposition is a complicated process along with intrinsic kinetics,mass transfer and heat transfer,which are the influencing factors for hydrate decomposition rate.The identification of the rate-limiting factor for hydrate dissociation during depressurization varies with the scale of the reservoir,making it challenging to extrapolate findings from laboratory experiments to the actual exploitation.This review aims to summarize current knowledge of investigations on hydrate decomposition on the subject of the research scale(core scale,middle scale,large scale and field tests)and to analyze determining factors for decomposition rate,considering the various research scales and their associated influencing factors.

Enhancing the Interaction of Carbon Nanotubes by Metal-Organic Decomposition with Improved Mechanical Strength and Ultra-Broadband EMI Shielding Performance

The remarkable properties of carbon nanotubes(CNTs)have led to promising applications in the field of electromagnetic inter-ference(EMI)shielding.However,for macroscopic CNT assemblies,such as CNT film,achieving high electrical and mechanical properties remains challenging,which heavily depends on the tube-tube interac-tions of CNTs.Herein,we develop a novel strategy based on metal-organic decomposition(MOD)to fabricate a flexible silver-carbon nanotube(Ag-CNT)film.The Ag particles are introduced in situ into the CNT film through annealing of MOD,leading to enhanced tube-tube interactions.As a result,the electrical conductivity of Ag-CNT film is up to 6.82×10^(5) S m^(-1),and the EMI shielding effectiveness of Ag-CNT film with a thickness of~7.8μm exceeds 66 dB in the ultra-broad frequency range(3-40 GHz).The tensile strength and Young’s modulus of Ag-CNT film increase from 30.09±3.14 to 76.06±6.20 MPa(~253%)and from 1.12±0.33 to 8.90±0.97 GPa(~795%),respectively.Moreover,the Ag-CNT film exhibits excellent near-field shield-ing performance,which can effectively block wireless transmission.This innovative approach provides an effective route to further apply macroscopic CNT assemblies to future portable and wearable electronic devices.

Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.

Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Progress in the research on organic piezoelectric catalysts for dye decomposition

Organic contaminants have posed a direct and substantial risk to human wellness and the environment.In recent years,piezo-electric catalysis has evolved as a novel and effective method for decomposing these contaminants.Although piezoelectric materials offer a wide range of options,most related studies thus far have focused on inorganic materials and have paid little attention to organic materi-als.Organic materials have advantages,such as being lightweight,inexpensive,and easy to process,over inorganic materials.Therefore,this paper provides a comprehensive review of the progress made in the research on piezoelectric catalysis using organic materials,high-lighting their catalytic efficiency in addressing various pollutants.In addition,the applications of organic materials in piezoelectric cata-lysis for water decomposition to produce hydrogen,disinfect bacteria,treat tumors,and reduce carbon dioxide are presented.Finally,fu-ture developmental trends regarding the piezoelectric catalytic potential of organic materials are explored.

Efficient Direct Decomposition of NO over La_(0.8)A_(0.2)NiO_(3)(A=K, Ba, Y) Catalysts under Microwave Irradiation

La_(0.8)A_(0.2)NiO_(3) (A=K,Ba,Y) catalysts supported on the microwave-absorbing ceramic heating carrier were prepared by the sol-gel method.The crystalline phase and the catalytic activity of the La_(0.8)A_(0.2)NiO_(3)catalysts were characterized by XRD and H_(2) temperature-programmed reduction (TPR).The effects of reaction temperature,oxygen concentration,and gas flow rate on the direct decomposition of nitric oxide over the synthesized catalysts were studied under microwave irradiation (2.45 GHz).The XRD results indicated that the La_(0.8)A_(0.2)NiO_(3) catalysts formed an ABO_(3) perovskite structure,and the H_(2)-TPR results revealed that the relative reducibility of the catalysts increased in the order of La_(0.8)K_(0.2)NiO_(3)>La_(0.8)Ba_(0.2)NiO_(3)>La_(0.8)Y_(0.2)Ni O_(3).Under microwave irradiation,the highest NO conversion amounted to 98.9%,which was obtained with the La_(0.8)K_(0.2)NiO_(3) catalyst at 400℃.The oxygen concentration did not inhibit the NO decomposition on the La_(0.8)A_(0.2)NiO_(3) catalysts,thus the N_(2) selectivity exceeded 99.8%under excess oxygen at 550℃.The NOconversion of the La_(0.8)A_(0.2)NiO_(3) catalysts decreased linearly with the increase in the gas flow rate.

Facile preparation of Fe/N-based biomass porous carbon composite towards enhancing the thermal decomposition of DAP-4

Fe/N-based biomass porous carbon composite(Fe/N-p Carbon) was prepared by a facile high-temperature carbonization method from biomass,and the effect of Fe/N-p Carbon on the thermal decomposition of energetic molecular perovskite-based material DAP-4 was studied.Biomass porous carbonaceous materials was considered as the micro/nano support layers for in situ deposition of Fe/N precursors.Fe/Np Carbon was prepared simply by the high-temperature carbonization method.It was found that it showed the inherent catalysis properties for thermal decomposition of DAP-4.The heat release of DAP-4/Fe/N-p Carbon by DSC curves tested had increased slightly,compared from DAP-4/Fe/N-p Carbon-0.The decomposition temperature peak of DAP-4 at the presence of Fe/N-p Carbon had reduced by 79°C from384.4°C(pure DAP-4) to 305.4°C(DAP-4/Fe/N-p Carbon-3).The apparent activation energy of DAP-4thermal decomposition also had decreased by 29.1 J/mol.The possible catalytic decomposition mechanism of DAP-4 with Fe/N-p Carbon was proposed.

Reactive molecular dynamics insight into the thermal decomposition mechanism of 2,6-Bis(picrylamino)-3,5-dinitropyridine

2,6-bis(picrylamino)-3,5-dinitropyridine(PYX)has excellent thermostability,which makes its thermal decomposition mechanism receive much attention.In this paper,the mechanism of PYX thermal decomposition was investigated thoroughly by the ReaxFF-lg force field combined with DFT-B3LYP(6-311++G)method.The detailed decomposition mechanism,small-molecule product evolution,and cluster evolution of PYX were mainly analyzed.In the initial stage of decomposition,the intramolecular hydrogen transfer reaction and the formation of dimerized clusters are earlier than the denitration reaction.With the progress of the reaction,one side of the bitter amino group is removed from the pyridine ring,and then the pyridine ring is cleaved.The final products produced in the thermal decomposition process are CO_(2),H_(2)O,N_(2),and H_(2).Among them,H_(2)O has the earliest generation time,and the reaction rate constant(k_(3))is the largest.Many clusters are formed during the decomposition of PYX,and the formation,aggregation,and decomposition of these clusters are strongly affected by temperature.At low temperatures(2500 K-2750 K),many clusters are formed.At high temperatures(2750 K-3250 K),the clusters aggregate to form larger clusters.At 3500 K,the large clusters decompose and become small.In the late stage of the reaction,H and N in the clusters escaped almost entirely,but more O was trapped in the clusters,which affected the auto-oxidation process of PYX.PYX's initial decomposition activation energy(E_(a))was calculated to be 126.58 kJ/mol.This work contributes to a theoretical understanding of PYX's entire thermal decomposition process.

Helmholtz decomposition with a scalar Poisson equation in elastic anisotropic media

P-and S-wave separation plays an important role in elastic reverse-time migration.It can reduce the artifacts caused by crosstalk between different modes and improve image quality.In addition,P-and Swave separation can also be used to better understand and distinguish wave types in complex media.At present,the methods for separating wave modes in anisotropic media mainly include spatial nonstationary filtering,low-rank approximation,and vector Poisson equation.Most of these methods require multiple Fourier transforms or the calculation of large matrices,which require high computational costs for problems with large scale.In this paper,an efficient method is proposed to separate the wave mode for anisotropic media by using a scalar anisotropic Poisson operator in the spatial domain.For 2D problems,the computational complexity required by this method is 1/2 of the methods based on solving a vector Poisson equation.Therefore,compared with existing methods based on pseudoHelmholtz decomposition operators,this method can significantly reduce the computational cost.Numerical examples also show that the P and S waves decomposed by this method not only have the correct amplitude and phase relative to the input wavefield but also can reduce the computational complexity significantly.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

SRMD:Sparse Random Mode Decomposition

Signal decomposition and multiscale signal analysis provide many useful tools for timefrequency analysis.We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram.The randomization is both in the time window locations and the frequency sampling,which lowers the overall sampling and computational cost.The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes,and thus leads to a new data-driven mode decomposition.The applications include signal representation,outlier removal,and mode decomposition.On benchmark tests,we show that our approach outperforms other state-of-the-art decomposition methods.

The rate of deadwood decomposition processes in tree stand gaps resulting from bark beetle infestation spots in mountain forests

Decaying wood is an essential element of forest ecosystems and it affects its other components.The aim of our research was to determine the decomposition rate of deadwood in various humidity and thermal conditions in the gaps formed in the montane forest stands.The research was carried out in the Babiog orski National Park.The research plots were marked out in the gaps of the stands,which were formed as a result of bark beetle gradation.Control plots were located in undisturbed stands.The research covered wood of two species–spruce and beech in the form of cubes with dimensions of 50 mm×50 mm×22 mm.Wood samples were placed directly on the soil surface and subjected to laboratory analysis after 36 months.A significant influence of the wood species and the study plot type on the physicochemical properties of the tested wood samples was found.Wood characteristics strongly correlated with soil moisture.A significantly higher mass decline of wood samples was recorded on the reference study plots,which were characterized by more stable moisture conditions.Poorer decomposition of wood in the gaps regardless of the species is related to lower moisture.The wood species covered by the study differed in the decomposition rate.Spruce wood samples were characterized by a significantly higher decomposition rate compared to beech wood samples.Our research has confirmed that disturbances that lead to the formation of gaps have a direct impact on the decomposition process of deadwood.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

How can technology and efficiency alleviate the dilemma of economic growth and carbon emissions in China's industrial economy? A metafrontier decoupling decomposition analysis

This paper attempts to explore the decoupling relationship and its drivers between industrial economic increase and energy-related CO_(2) emissions(ICE). Firstly, the decoupling relationship was evaluated by Tapio index. Then, based on the DEA meta-frontier theory framework which taking into account the regional and industrial heterogeneity and index decomposition method, the driving factors of decoupling process were explored mainly from the view of technology and efficiency. The results show that during2000-2019, weak decoupling was the primary state. Investment scale expansion was the largest reason hindering decoupling process of industrial increase from ICE. Both energy saving and production technology achieved significant progress, which facilitated the decoupling process. Simultaneously, the energy technology gap and production technology gap among regions have been narrowed, and played a role in promoting decoupling process. On the contrary, both scale economy efficiency and pure technical efficiency have inhibiting effects on decoupling process. The former indicates that the scale economy of China's industry was not conducive to improve energy efficiency and production efficiency, while the latter indicates that resource misallocation problem may exist in both energy market and product market.

Decompositions of the Kadomtsev-Petviashvili equation and their symmetry reductions

Starting with a decomposition conjecture,we carefully explain the basic decompositions for the Kadomtsev-Petviashvili(KP)equation as well as the necessary calculation procedures,and it is shown that the KP equation allows the Burgers-STO(BSTO)decomposition,two types of reducible coupled BSTO decompositions and the BSTO-KdV decomposition.Furthermore,we concentrate ourselves on pointing out the main idea and result of Bäcklund transformation of the KP equation based on a special superposition principle in the particular context of the BSTO decompositions.Using the framework of standard Lie point symmetry theory,these decompositions are studied and the problem of computing the corresponding symmetry constraints is treated.

Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.