Decomposition Results Research Articles

Measurement of local government green governance efficiency based on total waste emissions and PM2.5 concentration: Evidence from China.

The problem of environmental pollution is becoming more and more prominent. Making ecological governance take an effective and sustainable development path has become a complex problem for countries to think about. The proposal of green governance provides new ideas for governments to manage enterprises and local environmental governance. The DEA method is commonly used to measure the effectiveness of environmental governance, but the traditional DEA method ignores environmental factors and management factors, and the measurement error is significant. Therefore, this paper introduces the total waste discharge and PM2.5 average concentration and other unexpected outputs, using the three-stage DEA model and three-stage DEA Malmquist index model, creatively constructing the green governance measurement index system, which measures and evaluates the green governance efficiency of 30 provinces in China from 2004 to 2019 from static and dynamic perspectives. The research results show that the efficiency value obtained by the three-stage DEA model is higher than the first stage, which confirms that the external environmental factors have a specific impact on the GGE. At the same time, the comprehensive technical efficiency value presents a "U"-shaped trend with time. From the perspective of sub-regions, there is heterogeneity in the efficiency values between regions, showing a decreasing trend of "east, west, and middle." From the efficiency decomposition results, the main reason for the negative growth rate of GGE is the low efficiency of technological progress, which provides targeted suggestions for governance in various regions of China. This study will help provinces prepare to strengthen investment in technological innovation, maximize the benefits of input and output, and promote green and sustainable development.

A Hybrid Method for Structural Modal Parameter Identification Based on IEMD/ARMA: A Numerical Study and Experimental Model Validation

This article presents a hybrid method of structural modal parameter identification, based on improved empirical mode decomposition (EMD) and autoregressive and moving average (ARMA). Special attention is given to some implementation issues, such as the modal mixing, false modes, the judgment of the real intrinsic mode function (IMF) of classical EMD, and the difficulty of fixing the order of ARMA. To resolve the existing defects of EMD, an improved EMD (IEMD) that combines frequency band filtering and cluster analysis is proposed in this paper, where frequency band filtering divides the signal into several narrowband signals before the EMD process, and cluster analysis is used to determine the real IMFs. Euclidean distance is used to cluster the decomposition results, with no need to adjust any indexes or thresholds, and only by means of using the nearest distance to efficiently determine the real IMF. Moreover, IEMD is used as a pre-processing tool for ARMA, to resolve the difficulty of fixing its order. The capabilities of the proposed method were compared and assessed using a numerical simulation and an experimental model. The numerical simulation and experimental results showed that the improved method could resolve the modal mixing and false modal problems in the classical EMD process and could automatically identified the real IMFs, while the proposed IEMD was combined with ARMA to successfully identify the frequency and mode shape of the structure. Additionally, since each IMF is a single component signal, it is easy to determine the order of the ARMA model.

Synthesis and Characterization of NiO Nanoparticles by Using Thermal Decompositions of Ni(II) – ONO and ONNO Type Schiff Base Complexes

Five different Nickel (II) complexes were prepared using a ONNO type Schiff base bis-N;N’(salicylidene)-1,3-propanediamine (LH2) and an ONO type Schiff base N(2-hydroxy)salicylaldimine (SAPH2) in the presence of NH3 or amine and NiO nanoparticles were obtained with thermal decomposition of this complexes in air. The prepared complexes and Schiff bases have been characterized by element analysis, IR spectroscopy, Thermogravimetry (TG), 1HNMR and 13CNMR spectroscopy, Mass Spectrometry (MS), Atomic absorption spectrometry (AAS), and Scanning Electron Microscopy (SEM). The surface area of the obtained nanoparticle NiO material was measured by the Brunauer-Emmett-Teller (BET) method. The thermal decomposition products NiO nanoparticles were investigated with XRD, and SEM technics. SEM investigations showed that due to the very little solubility of the prepared two complexes and consequently they were precipitated in the form of nanoparticles. In addition, it observed that this very insoluble complexes are converted to NiO nanoparticles a result of thermal decomposition.

The Feature Recognition of Motor Noise Based on the Improved EEMD Model.

The noise generated by the machine is closely related to the running state of the machine, so the product can be effectively detected by analyzing the noise signal. The noise identification and control methods based on the EEMD model are widely used in motor noise control. However, the EEMD only considers the influence of noise amplitude on the decomposition results, and the added white noise cannot be completely neutralized. In this paper, an improved EEMD method is proposed by analyzing the influence of the maximum frequency on the decomposition results, in which the noise with different maximum frequency and amplitude is added to decompose the signal, and the decomposition effect is judged by the orthogonality coefficient of the decomposition result. Finally, the simulation signal and the measured signal are compared and analyzed, and the results show that the improved EEMD method has some advantages over the original method in suppressing mode confusion and fault diagnosis.

Educational mobility in Canada, 1969-2016: Evidence from the longitudinal and international study of adults.

That young Canadians are obtaining more education than previous generations might suggest that the relationship between parents' education and that of their children has weakened. However, accounts of intergenerational educational mobility in Canada published in the past two decades are scant. Drawing on the Longitudinal and International Study of Adults, this paper examines trends in intergenerational educational mobility from 1969 to 2016. Adopting an educational transition approach, I find no change in the relative relationship between parents' and children's education over time despite the structural expansion of education. The results of a Blinder-Oaxaca decomposition suggest the upgrading of parents' education across cohorts partially explains this stability. The analyses further reveal a bifurcation in Canada's post-secondary system whereby higher parental education provides and advantage to youth for university degree completion while those whose parents have less education make their way to other post-secondary institutions.

Macroeconomic effects of crude oil shocks: Evidence from South Asian countries.

This research tends to convey the relationship between crude oil price volatility and key macroeconomics indicators, i.e., gross domestic product (GDP), inflation rate (IR), interest rate, and exchange rate. The study collected the time-series data (2000–2020) from the South Asian countries (Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka), and employed macroeconomic policy modeling tools (impulse response function and the prediction error variance decomposition technique) in the vector autorepression (VAR) setup. The outcome of the impulse response function explained considerable variance among macroeconomic indicators in response to crude oil price shocks. The macroeconomic indicators are extremely susceptible to minor fluctuations in oil prices causing a significant impact on the region's socioeconomic situation. The result of variance decomposition indicates that each country in the region reacts differently to crude oil price volatility which reflects their macroeconomics fundamentals, independent policy, sector structure, and country differences. The findings support change in public policies in a way to reduce their dependency on oil energy and encourage them toward renewal and green energy sources for better environmental results and sustainable development.

Impact Load Sparse Recognition Method Based on Mc Penalty Function

The rotor system is an important part of large-scale rotating machinery. Bearings, as a key component of the rotor system, play a vital role in the healthy operation of the rotor system. The bearings operate under harsh conditions such as high temperature, high pressure, and high speed. They are complex and extremely prone to failure, especially when the bearing is affected by impact load, which seriously affects the remaining service life of the bearing. Uneven bearing friction, caused by the impact, is one of the main factors that cause premature failure of the bearing. The early identification of shock loads and reasonable measures are extremely important for the safe operation of equipment. This paper proposes an impact load identification method based on the sparse decomposition of the Mini-max concave penalty function (Mini-max concave penalty function, MC). The method uses the MC penalty function to reconstruct the regularized sparse recognition model, and then uses the improved original dual interior point method to solve the problem. This model realizes the identification of vibration and shock loads. Relevant experimental verification was carried out, and the results show that the sparse decomposition result based on the MC penalty function is better than the L1-regularized sparse decomposition result, and the noise is well suppressed in the non-loaded area of the impact load. This method can be applied to the early fault diagnosis of the vibration signal of the gas turbine rotor.

An ensemble-policy non-intrusive load monitoring technique based entirely on deep feature-guided attention mechanism

Non-intrusive load monitoring (NILM), as an important part of intelligent electricity consumption, improves the cognitive level of the load by analyzing the bus power in a specific range to obtain refined electricity consumption data at appliance level in that range, such as the number of loads, load categories, load operating status and corresponding energy consumption. For a long time, the research on NILM algorithm has been quite in depth, especially with the deep learning method being used in NILM, breakthrough progress has been made. In this paper, an integrated policy non-intrusive load monitoring technique based entirely on deep feature-guided attention mechanism is constructed based on sequence-to-point deep neural networks. Firstly, according to the operating cycle characteristics of different appliances, the data are segmented with different sliding windows and the integerized power values are mapped to high-dimensional feature vectors by word embedding, which transforms the value regression problem of decomposed power into a multiclassification problem to find the probability of decomposed power values at each discrete integer power value. Then the encoder composed of deep residual network is used for multi-layer feature extraction, which is used to obtain richer feature information, and the extracted feature quantities will be sent to the decoder through two paths respectively, one is mapped to the power feature space through word embedding as the input to the decoder; the other is sent to the decoder based entirely on the attention mechanism through convolution as the checked vector K and the content vector V. The decoder obtains the information with the highest relevance to the current moment by assigning weights to the before and after information, and thus decodes the final load decomposition result. Finally, this paper introduces a snapshot technique to obtain an inexpensive multi-model integration strategy, which improves the generalization ability of the model and the accuracy of the load decomposition. The effectiveness of the proposed method is verified by experiments on the REDD dataset and UK-DALE dataset.

One-step in-situ low damage etching of SiC/SiC composites by high-temperature chemical-assisted laser processing

SiC/SiC composites have a promising application prospect in the fields of aero-engine turbine blades and other hot-end components due to their excellent properties. It is difficult to machine SiC/SiC composites by traditional mechanical processing because of the ultra-high hardness and anisotropic character. The routinely laser direct processing in the air produce thermal effect damage, resulting in heat-affected zones and microcracks. Here high-temperature chemical-assisted nanosecond laser processing was investigated to realize one-step in-situ fast etching without causing damage to the structure of SiC/SiC composites. Firstly, the chemical wet etching only occurred in the laser-induced local high-temperature region. There was no additional etching damage to the substrate material in the region where the temperature was relatively low. Secondly, chemical liquid served as a transparent constraint layer, the ablation pressure on the material increased in a liquid confined environment. In comparison with laser direct etching in the air, the exposed fiber was not a needle-like structure and tubular structure but turned into a thin and flat tongue-like structure. The etching behavior of 0° fiber layer, 90° fiber layer, and crossing regions of fibers of different directions at low and high laser intensities were studied. Further, the hardness and friction coefficient of SiC/SiC composites after laser direct etching in the air and high-temperature chemical-assisted laser etching were compared. The chemical elementary composition before and after etching was measured to reveal the oxidized and carbonized characteristics. It was concluded that the action mechanism was the combined result of high-temperature decomposition, laser-induced ablation pressure in the liquid confined region, and local chemical wet etching. The results demonstrate that high-temperature chemical-assisted laser etching has some advantages over traditional laser beam machining and is a potential processing method for SiC/SiC composites.

Measuring inclusive green total factor productivity from urban level in China

Background: Inclusive green development aims to combine economic inclusiveness with greenness, which is an important goal of current economic development for China to achieve common prosperity. Measuring inclusive green total factor productivity (IGTFP) is of great significance for evaluating the quality of inclusive economic growth and accelerating inclusive economic growth.Method: This study establishes an index evaluation system of IGTFP from four aspects of high quality, efficient, fair, and sustainable development, using super-SBM to measure IGTFP based on panel data of 276 cities in China from 2006 to 2019, and conducts empirical comparative analysis from national central cities, provincial capitals, and ordinary prefecture.Result: 1) The IGTFP and technical progress rate in the central city and the provincial capital are significantly higher than that of the ordinary prefecture, but there is no significant difference in technical efficiency; the growth rate of IGTFP in the most central city remains around 10%, which is significantly higher in the south than that in the north. 2) According to the index decomposition result, all cities have basically realized the double-line improvement of technological efficiency and technological progress rate, but the technological efficiency is mostly lower than the technological progress rate. 3) From the perspective of economic convergence, only the IGTFP of provincial capitals shows the σ convergence feature, that is, the phenomenon of intra-group convergence; the IGTFP of all cities in three levels shows the β convergence feature, indicating that there is an obvious catch-up phenomenon within the group.Conclusion: The integration of “technology” and “efficiency” is the main driving force and path to realize the sustainable improvement of IGTFP in cities. Inclusive green growth needs to break “regional boundaries,” including north–south boundaries and urban boundaries.

Impact of interaction between financial development, urbanization, and energy consumption in China

Given the urgent need for China to reduce the growth rate of energy consumption (EC), the relationship between financial development (FD), urbanization (UR), and EC is directly related to the effectiveness of China's current ecological civilization construction. This study analyzes the internal relationship between the three from the perspectives of financial scale (FD1), financial structure (FD2), and financial efficiency (FD3) through the generalized method of moments (GMM) estimation, impulse response, and variance decomposition of the panel vector autoregression (PVAR) model. The following conclusions are derived. First, the results of the PVAR model show that when FD is measured with FD1, FD2, and FD3, each variable has path dependence, and the lag-phase-one term of each variable has a strengthening effect on itself. Second, based on the pulse response diagram, a positive response occurs after FD1 and FD2 give a standardized shock to EC, but a negative response occurs when FD is characterized by FD3. When FD is measured with FD1, a positive response occurs after UR gives a normalized shock to EC. When FD is measured with FD2 and FD3, there is a weak negative response. Third, the results of the variance decomposition show that the changes of FD, UR, and EC rely more on their own inertia. Although the impact of FD and UR on EC has an increasing trend, the impact is small. Therefore, local governments should rationally control the financial scale, optimize the financial structure, improve the financial efficiency, and strengthen the propaganda of energy conservation.

MENINGKATKAN KUALITAS GENERASI MUDA MELALUI PEMERIKSAAN HAEMOGLOBIN, HEMATOKRIT DAN GULA DARAH SISWA SMA NEGERI I TANJUNG SAKTI

Hemoglobin is the main protein of the human body that serves as a carrier of oxygen to the tissues and transport media of carbon dioxide from the body tissues of the lungs, the oxygen the body needs as a source of energy that will generate power so that humans are able to execute duties or daily occultation. The main source of haemoglobin is iron which is the source of the nutrient intake we consume. Hemoglobin examination is one of the most frequent blood tests performed by each laboratory because hemoglobin examination in the blood has an important role in the diagnosis of a disease. The usefulness of examining hemoglobin levels is to assess the level of anemia, response to anemia therapy, or disease progression associated with anemia and polycythemia. Hematocyte examination is also important for assessing the normal state or not of erythrocytes and blood volume as well as related to hemoglobin in the body. Decreased hematocrit is one indicator of anemia caused by various factors. Blood glucose is the result of carbihydrate decomposition and glycogen changes in the liver. Glucose examination in darh is a screening procedure that shows the inability of the pancreas to produce insulin, the inability of the small intestine to absorb glucose, the inability of cells to use glucose efficiently or the inability of the liver to collect and solve glycogen.
 Keywords: Haemoglobin, Hematocrit and Glucose levels in blood

Health capacity to work of the older adults in China: evidence from CHIPs

ABSTRACT This study attempts to estimate how much older adults would work if they were to work as much as middle-aged adults with the same health status in China. Using data from the China Household Income Project survey in 2013 and 2018, and based on the Milligan–Wise and CMR models, three main findings emerge: first, an untapped additional work capacity exists for those in the group aged 60–69, which comprises 29.8–65.8% of urban residents and 4.4–22.4% of rural residents. Second, additional work capacity is higher for urban residents with higher levels of education than for their counterparts, and the educational disparity in work capacity is greater for urban residents than for rural residents. Third, the decomposition results indicate that changes in health status contributed to increased potential work capacity for urban residents while decreasing that of rural residents.

Spatiotemporal Characteristics, Decoupling Effect and Driving Factors of Carbon Emission from Cultivated Land Utilization in Hubei Province.

The carbon emission level and spatiotemporal characteristics in Hubei Province were estimated and studied using the Intergovernmental Panel on Climate Change (IPCC) carbon emission coefficient technique based on county data from Hubei Province from 2000 to 2020. The relationship between carbon emissions from cultivated land utilization and agricultural economic growth was examined using the Tapio decoupling index, and the factors influencing carbon emissions in Hubei Province were further examined using the Logarithmic Mean Divisia Index (LMDI model). The results demonstrate that: (1) Spatiotemporal variations in carbon emissions are evident. In terms of time, the volume of carbon emissions in Hubei Province is still substantial, and the transition to low-carbon land use is quite gradual. Geographically, the high-value region of the middle east coexists with the low-value zone of the west, with apparent regional contrasts. (2) The decoupling between carbon emissions and agricultural economic growth is becoming more and more obvious in Hubei Province. The number of counties and cities in a negative decoupling state has significantly decreased, and the majority of counties are now in a strong decoupling condition. (3) Agricultural production efficiency is the most significant driving factor for restricting carbon emission, according to the decomposition results of carbon emission driving factors based on the LMDI model. In addition, the results of sample decomposition based on topographic characteristics indicate that agricultural production efficiency is primarily responsible for the suppression of carbon emissions in flat regions. The increase in carbon emissions in hilly regions is primarily influenced by agricultural productivity. The increase in carbon emissions in mountainous regions is mostly influenced by agricultural labor intensity. This study′s finding has enlightening implications for the high-quality growth of agriculture.

Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection

Anomaly detection is an essential task for quality management in smart manufacturing. An accurate data-driven detection method usually needs enough data and labels. However, in practice, there commonly exist newly set-up processes in manufacturing, and they only have quite limited data available for analysis. Borrowing the name from the recommender system, we call this process a cold-start process. The sparsity of anomaly, the deviation of the profile, and noise aggravate the detection difficulty. Transfer learning could help to detect anomalies for cold-start processes by transferring the knowledge from more experienced processes to the new processes. However, the existing transfer learning and multi-task learning frameworks are established on task- or domain-level relatedness. We observe instead, within a domain, some components (background and anomaly) share more commonality, others (profile deviation and noise) not. To this end, we propose a more delicate component-level transfer learning scheme, i.e., decomposition-based hybrid transfer learning ( DHTL ): It first decomposes a domain (e.g., a data source containing profiles) into different components (smooth background, profile deviation, anomaly, and noise); then, each component’s transferability is analyzed by expert knowledge; Lastly, different transfer learning techniques could be tailored accordingly. We adopted the Bayesian probabilistic hierarchical model to formulate parameter transfer for the background, and “ L 2,1 + L 1 ”-norm to formulate low dimension feature-representation transfer for the anomaly. An efficient algorithm based on Block Coordinate Descend is proposed to learn the parameters. A case study based on glass coating pressure profiles demonstrates the improved accuracy and completeness of detected anomaly, and a simulation demonstrates the fidelity of the decomposition results.

Fault identification of rolling bearing with variable speed based on generalized broadband mode decomposition and distance evaluation technique

In the fault diagnosis of rolling bearing, fault signals are often interfered by other noise signals, so that the fault characteristics are not obvious. In particular, vibration signals of rolling bearing with variable speed show non-stationary features, and the existing analysis methods are difficult to accurately extract their fault characteristics. The main idea of the previously proposed broadband mode decomposition (BMD) method is to search in the association dictionary containing both broadband and narrowband signals, so it can accurately extract the characteristics of stationary broadband signals. However, when applied to variable speed signals disturbed by strong noises, BMD algorithm is likely to produce modal confusion and has some errors in decomposition. Therefore, in this paper, a generalized broadband mode decomposition (GBMD) method is proposed to remove the rotating speed, and more accurate decomposition results are obtained. In order to realize the fault diagnosis of rolling bearing with variable speed, firstly, the original signals are decomposed by GBMD. Secondly, the eigenvalues of the first three components of the signals are calculated and the eigenvalue matrix is constructed. And distance evaluation technique (DET) method is utilized to screen out marked features. Finally, the marked features are input into backward propagation neural network (BP) for training and testing to identify the fault types of rolling bearing. Compared with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), and BMD, GBMD has good effect in anti-noise performance and fault feature extraction. Therefore, the combination of BP can achieve higher identification accuracy.

Modal Analysis of the Wake Instabilities of a Propeller Operating in Coastal Environments

Zhou, J.; Sun, P., and Pan, L., 2022. Modal analysis of the wake instabilities of a propeller operating in coastal environments. Journal of Coastal Research, 38(6), 1163–1171. Coconut Creek (Florida), ISSN 0749-0208. The dynamic characteristics of propeller wakes are a function of hydrodynamics, noise propagation, and structural vibration, among other factors. The evolution of propeller wake flow mechanisms from the near field to the far field has always been a significant topic of turbulence research. Numerical simulations of propeller wakes under different loading conditions were conducted using the improved delayed-detached eddy simulation (IDDES) method. Based on the mode decomposition technique, the results of numerical simulations were used as the input data to perform a reduced-order analysis of propeller wake fields. The results showed that there is a considerable difference between the morphology characteristics of propeller wakes under high and low loading conditions, where the former tends to be unstable earlier in the evolution. According to the mode decomposition results, with decreasing mode frequency, there is strong circumferential diffusion in the tip vortices, which is more pronounced under the high loading condition and at locations further downstream. Circumferential diffusion is a key factor triggering the propeller wake to change from the stable to the unstable state. The present work can provide a significant reference for the design and optimization of next-generation marine propellers operating behind vessels sailing in both coastal and offshore environments.

Profile decomposition in Sobolev spaces and decomposition of integral functionals I: Inhomogeneous case

The present paper is devoted to analyzing the lack of compactness of bounded sequences in inhomogeneous Sobolev spaces, where bounded sequences might fail to be compact due to an isometric group action, that is, translation. It will be proved that every bounded sequence (un) has (possibly infinitely many) profiles, and then the sequence is asymptotically decomposed into a sum of translated profiles and a double-suffix residual term, where the residual term becomes arbitrarily small in appropriate Lebesgue or Sobolev spaces of lower order. To this end, functional analytic frameworks are established in an abstract way by making use of a group action G, in order to characterize profiles by (un) with G. One also finds that a decomposition of the Sobolev norm into profiles is bounded by the supremum of the norm of un. Moreover, the profile decomposition leads to results of decomposition of integral functionals of subcritical order. It is noteworthy that the space where the decomposition of integral functionals holds is the same as that where the residual term is vanishing.

Changes and Trend Disparities in Life Expectancy and Health-Adjusted Life Expectancy Attributed to Disability and Mortality From 1990 to 2019 in China.

ObjectiveThis study aims to investigate sex, age, and cause-specific contributions to changes and trend disparities in life expectancy (LE) and health-adjusted life expectancy (HALE) attributed to disability and mortality from 1990 to 2019 in China, which provides insight into policy-making, health systems planning, and resource allocation.MethodsContributions of disability and mortality to changes and trend disparities in LE and HALE were estimated with standard abridged life table, Sullivan's method, and decomposition method, using retrospective demographic analysis based on mortality and years lived with disability (YLD) rates extracted from Global Burden of Disease Study 2019 (GBD 2019).ResultsFrom 1990 to 2019, LE and HALE increased by 10.49 and 8.71 years for both sexes, mainly due to noncommunicable diseases (NCDs) (5.83 years, 55.58% for LE and 6.28 years, 72.10% for HALE). However, HIV/AIDS and sexually transmitted infections had negative effects on changes in LE (−0.03 years, −0.29%) and HALE (−0.05 years, −0.57%). Lung cancer and ischemic heart disease caused the biggest reduction in LE (−0.14 years, −1.33%) and HALE (−0.42 years, −4.82%). Also, cardiovascular diseases (−0.08 years, −0.92%), neurological disorders (−0.08 years, −0.92%), diabetes and kidney diseases (−0.06 years, −0.69%), and transport injuries (−0.06 years, −0.69%) had main negative disability effects in HALE. Moreover, life expectancy lived with disability (LED) increased by 1.78 years, mainly attributed to respiratory infections and tuberculosis (1.04 years, 58.43%) and maternal and neonatal disorders (0.78 years, 43.82%).ConclusionThe LE and HALE in China have grown rapidly over the past few decades, mainly attributed to NCDs. It is necessary to further reduce the negative mortality effect of HIV/AIDS, lung cancer, colon and rectum cancer, pancreatic cancer, and ischemic heart disease and the negative disability effect of stroke, diabetes mellitus, and road injuries. In addition, the signs of disparities in mortality and disability of different sexes and ages call for targeted and precise interventions for key groups such as males and the elderly. According to the decomposition results, we may better determine the key objects of health policies that take into account substantial cause-specific variations to facilitate the realization of “healthy China 2030” plan.

EMD-based time–frequency denoising algorithm for the self-sensing of vibration signals in ultrasonic-assisted grinding

An empirical mode decomposition (EMD)-based time–frequency denoising algorithm is derived in this paper to accomplish the extraction of active ingredients for multi-frequency mixed signals, and then the self-sensing of vibration signals is realized without additional sensors for an ultrasonic-assisted grinding device. EMD is employed to accomplish the signal decomposition, and multiple intrinsic mode function components and the residual are obtained. Then, the weighted factors used for signal reconstruction are obtained based on innovation statistical distance, which is selected as a criterion to evaluate the time–frequency domain correlation between decomposition results and the original signal. Next, the maximum correntropy criterion-based similarity detection method is designed to adaptively modify the weighted factors, and the signal is reconstructed on the foundation of the modified weighted factors. Finally, numerical simulations and a self-sensing experiment are conducted to verify the denoising performance of the proposed algorithm. The self-sensing of the 4.59 dB vibration signal is realized for an ultrasonic-assisted grinding device.