基于Intrinsic Time-scale Decomposition算法的扰动信号特征量提取问题研究

由于电能质量扰动信号具有非线性的特点,当下算法无法有效提取电能质量扰动信号,采取Intrinsic Timescale Decomposition算法,可以有效提取电能质量扰动信号的频率、幅值、相位等特征量。利用Intrinsic Time-scale Decomposition算法提取电能质量扰动信号的Proper Rotation Component分量,后对Proper Rotation Component分量进行Hilbert变换以求得电能质量扰动信号的相位和瞬时频率,再根据电能质量扰动信号的高频突变点得到电能质量扰动信号的起止时间;根据envelope函数求得衰减因子。利用软件开发程序MATHEMATICA对电能质量扰动信号进行分析,结果表明,Intrinsic Time-scale Decomposition算法可以很好地提取电能质量扰动信号特征量,并且具有极佳的抗噪性能。

k-center问题的算法研究综述

k-center问题是设施选址的基础问题,同样是NP难问题,在分配、紧急服务等领域也有着实际的应用。随着问题规模的扩大,原有的算法已不再适用,需要进一步优化或者改进。为了找到求解该问题的高效算法,对现有算法进行研究。对各类求解k-center问题的算法进行梳理,将求解算法划分为精确算法、启发式算法、元启发式算法、近似算法等,从算法原理、改进思路、性能和精度等方面进行对比综述。精确算法在求解小规模k-center问题时可在多项式时间内得到最优解,但是算法效率低,不适用于大规模问题;启发式算法可以在多项式时间内给出相对最优解,但是没有理论保证,无法衡量与最优解的关系;元启发式算法可根据对目前存在的智能优化算法进行改进,给出相对最优解,但是解的质量无法保证;利用近似算法得到的解具有近似比保证,有较大的理论研究价值,但是实用价值较弱。目前求解k-center问题的元启发式算法已取得一定的研究成果,但是在求解时间、求解规模、算法效率等方面仍待突破,这将是未来k-center问题的研究重点。

基于边界信息的自适应过采样算法

针对人工少数类过采样(synthetic minority over-sampling technique,SMOTE)算法存在样本合成区域狭小,容易将少数类泛化到多数类及引入噪声的问题,提出一种基于噪声过滤、边界点自适应采样的过采样算法。首先,该算法使用K近邻算法进行噪声过滤,接着确定边界点并在边界点中寻找合适的点作为根样本点,并以其K近邻点中与其同类且欧氏距离最远的点作为候选样本点。然后,根据根样本点所携带的边界信息确定该点所合成的样本数量,并根据根样本点和候选样本点生成一个N维球体作为样本的合成区间。最后,对合成样本进行判断以确定其是否满足条件。通过实验证明,该算法生成的样本质量要优于SMOTE及其常见变种算法。

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.

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.

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.

基于粒子群算法的纯电动商用车转矩分配策略

针对纯电动商用车采用双电动机驱动时存在的转矩分配问题,提出基于粒子群算法的模糊控制策略.首先在Simulink/Stateflow软件中搭建整车动力系统物理模型,以粒子群算法为基础,进行整车转矩分配.由于计算量大,无法运用于实车,故根据粒子群算法的结果,结合传统项目经验,设计一个参数实时调节模糊控制器来进行转矩分配.该方法运行速度快,且基本达到粒子群全局优化的效果.验证与分析结果表明:与原车单电动机动力系统相比,采用该方法的双电动机动力系统能量消耗减少了12.08%;在双电动机动力系统下,该方法与平均分配控制策略相比,电动机总能量损失降低了13.09%.

基于IWOA-LSTM算法的预应力钢筋混凝土梁损伤识别

为准确识别桥梁结构的损伤程度,制作了桥梁的关键构件——预应力钢筋混凝土梁,进行三点弯曲加载试验.收集了损伤破坏全过程的声发射(AE)信号,通过AE信号参数分析,将梁的损伤破坏过程划分为4个典型阶段.构建了长短时记忆神经网络(LSTM)模型,根据经验设置LSTM模型的超参数容易导致网络陷入局部最优而影响了分类结果,提出采用Sine混沌映射和自适应权重来改进鲸鱼优化算法(WOA),对LSTM进行超参数寻优.设计了IWOA-LSTM算法模型,训练识别试验梁各损伤阶段的AE信号特征参数.定型网络结构,并识别同种工况下其他梁的AE信号.结果表明:IWOA-LSTM算法模型识别准确率均超过或接近92%,相较于普通LSTM模型,IWOA-LSTM模型识别准确率提高了约7%.

Modified multiple-component scattering power decomposition for PolSAR data based on eigenspace of coherency matrix

A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.

多传感器融合算法的四旋翼无人机室内定位技术研究

针对缺乏明显位置标识的室内环境,为实现无人机的精确定位,提出了一种多传感器融合算法。该算法综合运用了中值滤波、阈值分割、投影变换、最小二乘法等手段,以实现无人机位置和位姿的精确估计。通过一系列实验,对所提出的算法与模拟算法进行了对比分析,验证了所提算法的准确性,并完成了四旋翼无人机在室内环境下的初步定位任务。

Ba promoter effect on cobalt-catalyzed ammonia decomposition kinetics: A theoretical analysis

Ammonia decomposition is a key reaction in the context of hydrogen storage, transport, and release. This study combines density functional theory(DFT) calculations with microkinetic modeling to address the promotion mechanism of Ba species for ammonia decomposition on Co catalysts. The modified adsorption properties of Co upon the addition of metallic Ba or BaO suggest that the promoters play a role in alleviating the competitive adsorption of H. Calculating the full reaction pathway of ammonia decomposition shows that limiting the investigation to the N–N association step, as done previously, overlooks the effect of the promoter on the energy barriers of the NHxdehydrogenation steps. Challenges of modeling the ammonia decomposition reaction are addressed by understanding that the NH_(2) intermediate is stabilized on the step sites rather than the terrace sites. When the effect of H-coverage on the adsorption of NH_(3) is not considered in the microkinetic simulations, the results conflict with the experiments.However, accounting for the effect of H-coverage, as performed here, shows that BaO-doped Co has higher rates than pristine Co and Ba-doped Co at the reaction temperature of 723.15 K. When H is adsorbed on the Ba-doped Co, the adsorption of ammonia becomes significantly endergonic, which makes the rates relatively slow. The superiority of the BaO-promoted catalyst is attributed to a lower energy for the transition state of the rate-determining step, coupled with a reduced impact of the hydrogen coverage on weakening the ammonia adsorption. The kinetic analysis of the influence of Ba and BaO on the Co surface shows that BaO-doped Co aligns more closely with experimental observations than Badoped Co. This implies that Ba on the Co surface is likely to be in an oxide form under reaction conditions.Understanding the kinetics of the ammonia decomposition reaction provides a foundation for developing highly effective catalysts to accelerate the industrial utilization of ammonia as a sustainable hydrogen carrier.

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.

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.

Balance Sparse Decomposition Method with Nonconvex Regularization for Gearbox Fault Diagnosis

As an important part of rotating machinery,gearboxes often fail due to their complex working conditions and harsh working environment.Therefore,it is very necessary to effectively extract the fault features of the gearboxes.Gearbox fault signals usually contain multiple characteristic components and are accompanied by strong noise interference.Traditional sparse modeling methods are based on synthesis models,and there are few studies on analysis and balance models.In this paper,a balance nonconvex regularized sparse decomposition method is proposed,which based on a balance model and an arctangent nonconvex penalty function.The sparse dictionary is constructed by using Tunable Q-Factor Wavelet Transform(TQWT)that satisfies the tight frame condition,which can achieve efficient and fast solution.It is optimized and solved by alternating direction method of multipliers(ADMM)algorithm,and the non-convex regularized sparse decomposition algorithm of synthetic and analytical models are given.Through simulation experiments,the determination methods of regularization parameters and balance parameters are given,and compared with the L1 norm regularization sparse decomposition method under the three models.Simulation analysis and engineering experimental signal analysis verify the effectiveness and superiority of the proposed method.

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.

Regularized dynamic mode decomposition algorithm for time sequence predictions

Dynamic mode decomposition(DMD) aims at extracting intrinsic mechanisms in a time sequence via linear recurrence relation of its observables, thereby predicting later terms in the sequence. Stability is a major concern in DMD predictions. We adopt a regularized form and propose a Regularized DMD(Re DMD) algorithm to determine the regularization parameter. This leverages stability and accuracy. Numerical tests for Burgers' equation demonstrate that Re DMD effectively stabilizes the DMD prediction while maintaining accuracy. Comparisons are made with the truncated DMD algorithm.

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.