Decomposition Of Components Research Articles

Improving the Potential of Coniferous Forest Aboveground Biomass Estimation by Integrating C- and L-Band SAR Data with Feature Selection and Non-Parametric Model

Forests play a significant role in terrestrial ecosystems by sequestering carbon, and forest biomass is a crucial indicator of carbon storage potential. However, the single-frequency SAR estimation of forest biomass often leads to saturation issues. This research aims to improve the potential for estimating forest aboveground biomass (AGB) by feature selection based on a scattering mechanism and sensitivity analysis and utilizing a non-parametric model that combines the advantage of dual-frequency SAR data. By employing GF-3 and ALOS-2 data, this study explores the scattering mechanism within a coniferous forest by using results of target decomposition and the pixel statistics method. By selecting an appropriate feature (backscatter coefficients and polarization parameters) and using stepwise regression models and a non-parametric model (the random forest adaptive genetic algorithm (RF-AGA)), the results revealed that the RF-AGA model with feature selection exhibited excellent AGB estimation performance without obvious saturation (RMSE = 10.42 t/ha, R2 = 0.93, leave-one-out cross validation). The σHV, σVH, Pauli three-component decomposition, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of thee C-band and σHV, σVH,σHH, Yamaguchi three-component decomposition, and VanZyl3 component decomposition of the L-band are suited for estimating the AGB of coniferous forests. Volume scattering was the dominant mechanism, followed by surface scattering, while double-bounce scattering had the smallest proportion. This study highlights the potential of investigating scattering mechanisms, sensitivity factors, and parameter selection in the C- and L-band SAR data for improved forest AGB estimation.

First-principles study of Adsorption and Gas Sensing Properties of Pt-modified InN for SF6 Decomposition Components

First-principles study of Adsorption and Gas Sensing Properties of Pt-modified InN for SF6 Decomposition Components

Optimized Tensor Decomposition and Principal Component Analysis Outperforming State-of-the-Art Methods When Analyzing Histone Modification Chromatin Immunoprecipitation Profiles

It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification but are general methods that aim to identify protein binding to the genome. In this study, tensor decomposition (TD) and principal component analysis (PCA)-based unsupervised feature extraction with optimized standard deviation were successfully applied to gene expression and DNA methylation. The proposed method was used to identify histone modification. Histone modification along the genome is binned within the region of length L. Considering principal components (PCs) or singular value vectors (SVVs) that PCA or TD attributes to samples, we can select PCs or SVVs attributed to regions. The selected PCs and SVVs further attribute p-values to regions, and adjusted p-values are used to select regions. The proposed method identified various histone modifications successfully and outperformed various state-of-the-art methods. This method is expected to serve as a de facto standard method to identify histone modification. For reproducibility and to ensure the systematic analysis of our study is applicable to datasets from different gene expression experiments, we have made our tools publicly available for download from gitHub.

Volatile compounds produced in smoked bacon inoculated with potential spoilage bacteria.

Volatile organic compounds (VOCs) produced during meat storage are mainly derived from the decomposition of meat components and the metabolism of spoilage bacteria. VOCs produced in sterile bacon model substrate inoculated or un-inoculated with spoilage bacteria, Staphylococcus xylosus (P2), Leuconostoc mesenteroides (P6), Carnobacterium maltaromaticum (P9), Leuconostoc gelidum (P16) and Serratia liquefaciens (P20), previously isolated, were identified by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Furthermore, combinations of the strains (Pm) were also obtained. In total, 54 volatile compounds, including aldehydes, alcohols, phenols, ketones, alkanes, alkanes, organic acids, esters and so forth, were determined after 45 days of storage in bacon inoculated with potential spoilage bacteria using the HS-SPME/GC-MS method. VOC concentrations of alcohols and organic acids in groups inoculated with bacteria were remarkably higher (P < 0.05) compared to that in control samples. Specifically, some VOCs are closely related to the metabolic activity of the inoculated bacterial strains; for example, 2,3-butanediol was associated with P2, P16 and P20, and acetic acid was mainly related to P6 and P9. The results of partial least squares regression indicated that there was a high correlation between the electronic nose sensors and VOCs of smoked inoculated potential spoilage bacteria. These compounds are potentially important for predicting deterioration of smoked bacon. © 2023 Society of Chemical Industry.

Strength formation mechanism and curing system optimization of low-carbon cementitious materials prepared by synergistic activation of multiple alkaline solid wastes

Lack of proper disposal of carbide slag (CS), soda residue (SR), and red mud (RM), which are alkaline solid wastes released from the production of acetylene, soda ash, and alumina, respectively, adversely affects the environment. This study synthesized a new low-carbon cementitious material using CS-SR-RM-fly ash (FA) as raw material and optimized its curing method.The influence of raw material composition and various curing systems on the mechanical properties and microstructure of specimens was analyzed using compressive strength, chloride ion consolidation, XRD, FTIR, and SEM-EDS. The optimum ratio of CS:SR:RM:FA was determined to be 2:1:4:3, and the compressive strength of 3/28 d at room temperature (RT) sealed curing reached 10.7/22.1 MPa. The alkaline environment created by CS-SR-RM facilitated the decomposition of active components in FA and RM, contributing to the formation of a stable three-dimensional network comprising N-A-S-H gel, C-(A)-S-H gel, and Friedel's salt crystal product. High-temperature (HT) curing at 80 °C for 8 h significantly enhanced early strength, with strength growth rates of up to 147.7/29.9% compared with RT sealed curing at 3/28 d. HT curing promoted the rapid and steady growth of hydration products, resulting in increased structural compactness and improved strength. The present study offers suggestions for reducing the environmental pollution caused by alkaline solid waste and developing new resource utilization means.

Two-step valorization of invasive species Rosa rubiginosa L. husk waste through eco-friendly optimized pectin extraction and subsequent pyrolysis

This research studied chemical and thermal treatments of rosehip husk (RH), from invasive species in Patagonia Rosa rubiginosa, which is considered a main waste in the seed-oil extraction industry, to obtain added-value products by optimizing an eco-friendly pectin extraction, followed by pyrolysis of the remaining solid (RHW). The aim was to valorize the rosehip husk of an invasive species by finding the optimal conditions for pectin extraction (maximum yield and minimum CO2 emissions) and assessing the pyrolysis process together with the products obtained, finding the temperature of maximum production of each species in the gaseous product. By applying multi-objective optimization, it was found that the optimal yield was 17.83 % with 456.7 g of CO2 emitted for kg of pectin at temperature T = 79.6 ºC, time t = 30 min, and pH = 2.3. By TGA, 6 processes were identified in the pyrolysis of RHW: moisture evaporation and 5 consecutive reactions which were related to lignocellulosic biomass components decomposition. The kinetic parameters of each reaction were obtained by isoconversional method (activation energy), compensation effect method (pre-exponential factor), and master-plots method (conversion function). The pyrolysis gaseous products of energetic interest identified were CH4, H2, and C2H4, with maximum production at 630, 700, 850 ºC respectively. The main compounds found by GC-MS were diacetone alcohol, mesityl oxide, butylated hydroxytoluene, among others. The ash was analyzed by XRD and showed a high Ca concentration (46.9 %) with presence of Ca(OH)2, MgO, KCaPO4, and K4Ca(PO4)2.

Hydrogeological controls on formation of Patearoa saline site in Central Otago, New Zealand and definition of geoecological salt lines

ABSTRACT The Patearoa saline site in Maniototo basin has developed on variably clay-altered Otago Schist. Decomposition and sedimentary redistribution of schist outcrop components has led to formation of bare soil-free substrates (upper metre scale) with contrasting permeability to rain and shallow groundwater percolation. Relatively impermeable clay-rich substrates, with a surface crust (cm scale) of detrital clay and muscovite, are saline with electrical conductivity (EC) of 1–35 mS/cm and locally hyperalkaline (pH > 10), with evaporative mineral accumulations. These saline substrates are downslope of more permeable substrates consisting of coarse schist debris and fractured outcrops, which have lower salinity (EC < 1 mS/cm) and lower pH (<7). A salt line can be mapped at metre scale between these substrate types on hillsides. The geochemical contrasts across the salt line have strong effects on plant communities that are colonising bare ground. Initial colonisation of bare saline substrates by some plants changes the geochemical signatures of the surficial few centimetres to form proto-soil with lower EC and pH. Proto-soil development facilitates further colonisation by pasture grasses, but the underlying substrates remain saline and highly alkaline. Salt lines similar to the one at Patearoa are mappable elsewhere in the Otago area and constitute fundamental geoecological boundaries.

Underwater polarization de-scattering imaging based on orthogonal polarization decomposition with low-pass filtering

Previous polarization imaging methods tend to assume that the degree of polarization of the target reflected light is a constant, while the targets with nonuniform polarization characteristics are less considered. The required background area or prior knowledge hinders the practical application of underwater polarization imaging technology. In this paper, we analyzed the basic physical model of underwater imaging, and proposed a new underwater polarization de-scattering method by considering orthogonal polarization decomposition of both the target reflected and backscattered light. The polarized-difference intensity of the backscattered light can be directly calculated from the polarization orientation angle of the two horizontal decomposition components. Meanwhile, a low-pass filter was used to suppress noise and globally evaluate the degree of polarization of the backscattered light. The proposed method can automatically distinguish the underwater targets polarization characteristics, and no background area or any prior information is required. The laboratory simulation experimental results demonstrate that this method can effectively improve the quality of underwater images under different conditions.

BERPIKIR KOMPUTASIONAL SISWA SMP DALAM MENYELESAIKAN MASALAH MATEMATIKA

The important skill to be applied in the 21st century is computational thinking (CT). Students need to develop computational thinking skills by being trained to solve open-ended problems. The descriptive qualitative method is the method used in this research. This research aims to describe the computational thinking skills of junior high school students in solving open-ended mathematical problems by explaining each component of computational thinking, namely abstraction, decomposition, algorithmic thinking, generalization, and debugging. The subjects of the research were several 8th-grade students from a private junior high school in Surabaya, consisting of 55 students who had been given TKM and TPM. Then, 3 students with different categories who provided unique answers were selected. Student in the low computational thinking category (BK1) do not fulfill all computational components. Student in the moderate category (BK2) have incomplete results in the final solution, and the problem-solving methods provided are also inadequate, resulting in imperfect final outcomes. Student in the high computational thinking category fulfill all computational components. Student (BK3) provides correct and varied problem-solving methods and solutions.&#x0D; &#x0D;

A Novel Time–Frequency Feature Fusion Approach for Robust Fault Detection in a Marine Main Engine

Ensuring operational reliability in machinery requires accurate fault detection. While time-domain vibration pulsation signals are intuitive for pattern recognition and feature extraction, downsampling can reduce analytical complexity, but may result in low-precision data, affecting fault detection performance. To address this, we propose time–frequency feature fusion, combining information from both the time and frequency domains for fault detection. Our approach transforms vibrational pulse data into instantaneous revolutions per minute (RPM) and employs statistical analysis for the time-domain features. For the frequency-domain features, we use the combined method of empirical mode decomposition and independent component analysis (EMD-ICA), along with the Wigner bispectrum method to capture the nonlinear characteristics and phase conjugation. Using a deep neural network (DNN), we classify the anomaly states, demonstrating the effectiveness and versatility of our approach in detecting anomalies and improving diagnostic precision. Compared to using time or frequency features alone, our time–frequency feature fusion model achieves higher accuracy, with 100% accuracy at lower downsampling rates and 96.3% accuracy at a downsampling rate of 100×.

Image deraining based on dual-channel component decomposition

Image deraining aims to remove rain streaks from images and reduce information loss in outdoor images caused by rain. As a fundamental task in image processing, image deraining not only enhances the visibility of images but also provides necessary image restoration for advanced vision tasks. Existing image deraining models mostly train end-to-end models by minimizing the similarity between the output image of the model and the rain-free ground truth. Although these methods have achieved significant results, they often perform poorly in the face of dense and changing rain streak scenes. In this paper, we propose a novel method, called Dual-Channel Component Decomposition Network (DCD-Net). The basic idea of DCD-Net is to leverage the separability prior of rainy images, treats the rain-free background layer and the rain streak mask layer as two parallel component extraction tasks. To this end, it builds a dual-branch parallel networks that extract the rain-free background image and decouple the reconstruction information of the rain streak mask, respectively. It finally applies a composite multi-level contrastive supervision to the output of the above dual-branch parallel network, thereby achieving rain streak removal. Extensive experiments on various datasets demonstrate that the proposed model outperforms existing methods in deraining dense rain streak images.

Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease.

Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson's disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson's disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson's disease. In addition, the features attributed to the mild cognitive impairment in Parkinson's disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson's disease.

Effects of oxidation temperature on microstructure and spontaneous combustion characteristics of coal: A case study of Shendong long-flame coal

To identify effects of the pre-oxidation process on the spontaneous combustion characteristics of Shendong low-rank long-flame coal, the microstructure and oxidation reactivity of coal samples pre-oxidized at different temperature including 70 °C, 100 °C, 200 °C, 300 °C, and 400 °C were studied and compared with those of raw coal. The surface morphology, pore features, functional groups, and microcrystalline structure of coal were studied using SEM, N2 adsorption, XPS, and XRD analyses. Moreover, TG-DSC experiments were conducted to investigate the changes in the spontaneous combustion characteristics of coal. The results indicated that low-temperature pre-oxidation at 70 °C and 100 °C does not alter the physical and chemical structure significantly, so the pore parameters, functional group contents, and microcrystalline structure of these two samples were very similar to those of raw coal. When the pre-oxidation temperature reached and exceeded 200 °C, the specific surface area and total pore volume of coal samples exhibited a significant decrease due to extensive thermal decomposition and consumption of coal components via oxidation. Moreover, high-temperature pre-oxidation treatment above 200 °C sharply reduced the aromatic and aliphatic carbon C–C/C–H content, and this is ascribed to the substantial consumption of primordial organic matter, this made coal macromolecules more tightly packed with an increase in carbon crystal size and carbon matrix order. In addition, with increasing temperature from 200 °C to 400 °C, the C–C/C–H contents decreased and aromaticity of coal increased, which revealed the weak oxidative activity. The heat release △H1 from 30 °C to 300 °C of coal samples after pre-oxidation at 200–400 °C was significantly lower than that of raw coal, while the △H1 values of coal samples treated at 70 °C and 100 °C were close to that of raw coal. Notably, that oxidation treatment at 70 °C resulted in an increase in the amount of heat released, because of the largest specific surface area that increases the risk of spontaneous combustion. Thus, the pre-oxidation treatment increases the risk of spontaneous combustion of coal when the pre-oxidation temperature is lower than a critical temperature.

Comparison and extension of advanced frequency domain non-active power decompositions

This research paper introduces new prospects in the field of power quality and energy transmission efficiency analysis and system design methodology through advanced frequency-domain power definitions. This proposal presents an improved extension of the power theory Currents’ Physical Components (CPC) by L.S. Czarnecki and compares its improvements with the CPC and an extension of the CPC by H. Lev-Ari. This proposal increases the resolution of physical phenomena reducing the power quality and hence contributes to its analysis and improvement. For this purpose, four additional non-active power components compared to the CPC are introduced. In contrast to the extension by H. Lev-Ari, these additional components are obtained from different definitions. In particular, the decomposition of the unbalance component into a spectral uniform and a spectral non-uniform component is novel to frequency-domain power definitions. As the CPC and its extension through H. Lev-Ari already decompose the collinear and shift components into spectral uniform and non-uniform components, the proposal for the unbalance components finally completes this structure. This yields a consistent and intuitive designation of physical phenomena related to spectral non-uniformity constituting as a concretization to the well-known distortion non-active power. Conclusive examples illustrate this improvement using the appertaining non-active power components.

Furan promotes cytotoxic effects through DNA damage and cell apoptosis in Leydig cells

Furan is an organic chemical that can cause adverse effects on human health and is formed as a result of the thermal decomposition of many food components during cooking, storage, and processing techniques. Studies have shown that exposure to furan causes nephrotoxicity, hepatotoxicity, immunotoxicity, and reproductive toxicity. According to our current knowledge of the literature, the genotoxic mode of action of furan is highly controversial. The genotoxic effects of furan on the male reproductive system, however, have not been studied. In this study, the TM3 Leydig cell line was treated with 750, 1500, and 3000 μM concentrations of furan for 24 h. Following the completion of the exposure period, the cytotoxicity of furan in TM3 Leydig cells was assessed using a cell viability assay and a spectrophotometric measurement of lactate dehydrogenase (LDH) enzyme levels. The double fluorescence staining method was used to demonstrate furan-induced apoptosis, and DNA damage was shown using the micronucleus, comet, and chromosomal aberration assays. The result indicated that furan administration of Leydig cells resulted in an increase in structural chromosomal aberration, comet, and micronucleus formation, reduced cell viability, increased LDH activity, and a higher incidence of apoptotic cells. These findings revealed that furan induces DNA damage in TM3 Leydig cells, causing genotoxicity and DNA damage-induced cytotoxicity.

Adsorption and Sensing of SF6 Decomposition Products by a Pd-Doped MoTe2 Monolayer: A First-Principles Study.

The detection of sulfur hexafluoride (SF6) decomposition components has become one of the best ways to diagnose early latent insulation faults in gas-insulated equipment, which can effectively prevent sudden accidents by identifying such faults. In this paper, we by first-principles theory investigated the adsorption and sensing behaviors of four typical SF6 decomposition components (H2S, SO2, SOF2, and SO2F2) on the pristine Pd-doped MoTe2 monolayer. The adsorption energy, work function, recovery time, charge density difference, density of state, and band structure of the adsorption structures are obtained as well as analyzed. The results indicate that the Pd dopant prefers to be trapped at the TMo site, with a binding energy of -2.25 eV. The Pd-MoTe2 chemisorbs the remaining gases except SO2xF2, with the adsorption capacity ranking as SOF2 > SO2 > H2S. The adsorption of gas molecules reduces the bandgap of Pd-MoTe2, thereby increasing conductivity. On the other hand, the recovery time of the Pd-MoTe2 monolayer material at a temperature of 398 K demonstrates its excellent gas desorption performance toward four decomposition gases. The research results provide a theoretical basis for Pd-MoTe2 to detect SF6 decomposition components, thus, promoting the stable operation of the power system.

An All-Fiber FLRD System for SO2 Detection Based on Graphene-Coated Microfiber

The accurate and effective detection of SF6 decomposition components inside a gas-insulated switchgear (GIS) is crucial for equipment fault diagnosis and condition assessment. The current method for detecting SF6 decomposition components involves gas extraction at the GIS inlet, which only provides limited information on the decomposition component content. Therefore, there is a need to explore more effective ways to obtain internal gas component information within GIS. In this study, we propose a graphene-coated microfiber gas detection method for SO2. We establish a physical simulation model of the microfiber and analyze the sensing mechanism of the microfiber diameter and cladding refractive index changes in its evanescent field. A graphene-coated microfiber gas sensor was prepared using a drop-coating method, and a fiber loop ring-down (FLRD) gas detection system was constructed for the experimental studies on SO2 gas detection. The results demonstrated that the graphene-coated microfiber exhibits an excellent gas-sensitive response to SO2 and achieves trace-level detection at room temperature. The concentration range of 0 to 200 ppm showed good linearity, with a maximum detection error of 4.76% and a sensitivity of 1.24 ns/ppm for SO2. This study introduces an all-fiber method for detecting SF6 decomposition components, offering a new approach for online monitoring of SF6 decomposition components in GIS equipment using built-in fiber-optic sensors.

In situ generated of hybrid interface in poly(1,3-dioxolane) quasi solid electrolyte and extended sulfone cosolvent for lithium-metal batteries

The practical application of lithium metal batteries with high energy density are difficult to apply due to the problem of uncontrolled lithium dendrite growth with commercial liquid electrolytes. Herein, we designed a poly 1,3-dioxolane (PDOL) based quasi solid polymer electrolyte with in-situ polymerization. A small amount of lithium difluoro(oxalato)borate (LiDFOB) provides cationic initiation center for the polymerization of dioxolane (DOL). Sulfolane (SL) were further introduced into the system as a cosolvent and the ionic conductivity was increased to 3.22 × 10-4 S cm-1followed with a high Li+ transfer number of 0.73 at room temperature. Trace amount of SbF3 was introduced to form a robustness inorganic-rich hybrid interface that reduces the decomposition of electrolyte components and retards the formation of lithium dendrites during cycling. The superior rate performance and stable 5C long cycle performance are obtained in LiFePO4||Li full cell owing to the interaction between Li+, SL and PDOL. This work expands the choice of components in quasi solid electrolytes to manufacture qualified LMBs with enhanced performance.

Deep Straw Burial Accelerates Straw Decomposition and Improves Soil Water Repellency

The continuous input, slow decomposition, and retention of straw can impede tillage and crop growth, and the decomposition process of the straw in soil is affected by its composition and the method of application. Experimental Station of Jilin Agricultural University, Changchun City, Jilin Province. The soil type was Argiudolls, the experimental field area was 30 m2, and the maize was planted continuously for 12 years without returning the straw to the field. There were four treatments: (1) control (CK), 10 g of straw was placed on the surface of a nylon mesh bag, and the nylon mesh bag was separated from the soil by polyethylene film without contacting with the soil; (2) straw mulching (CM), a nylon mesh bag with 10 g of straw was placed on the soil surface; (3) straw burying (CD), a nylon mesh bag with 10 g of straw was buried at a depth of 40 cm from the soil surface; (4) straw burying (CE), a nylon mesh bag with 40 cm of soil depth. Fifteen micro-zones were set up with a micro-zone area of 1 m2 and each micro-zone consisted of four treatments randomly grouped in three replicates per treatment. The on-site nylon bag burying trial started on 20 April 2021 and ended on 15 April 2022. Nylon bag and soil samples were collected on days 0, 30, 90, 180, 270, and 360. SOC, TN, straw component decomposition, and water droplet-soil contact angle were determined. Our results showed that the CE treatment increased soil organic carbon (SOC) and total nitrogen (TN) content compared to the CM and CD treatments. Compared with CK, straw decomposition rates increased by 13.3%, 30.8%, and 22.3% in the CM, CD, and CE treatments, respectively. Lignin decomposition rates increased by 7.8%, 27.3%, and 16.2%; cellulose decomposition rates increased by 14.6%, 35.4%, and 27.3%; and hemicellulose decomposition rates increased by 17.2%, 31.7% and 23.7% in the CD treatment, respectively. Compared with CK, the contact angle of droplets in the CD treatment was statistically significantly increased by 91.5% when the droplets remained on the soil surface for 5 s. The rate of decrease of droplet contact angle with time was statistically significantly decreased by 11.8%, and the penetration rate of droplets on the soil surface was slower. Overall, the CD treatment promoted straw decomposition and increased SWR compared to the CM and CE treatments. which are important attributes to enhance soil quality and improve soil structural stability.

Mono-trend mode decomposition for robust feature extraction from vibration signals of rotating Machinery

The analysis of vibration signals of rotating machinery plays an important role in equipment fault diagnosis and health monitoring. However, the decomposition of fault characteristic components often lacks robustness and accuracy under nonstationary operating conditions due to potential strong background noise and the complexity of existing components. In this paper, we address this issue by utilizing the relationships among components of vibration signals generated by rotating structures, described as the mono-trend modes (MTMs). Our proposed method, called the MTM decomposition (MTMD), enables robust feature extraction from vibration signals under nonstationary operating conditions. Initially, the instantaneous angular speed (IAS) of the input shaft is measured by tachometers or estimated by the framework of parameterized resampling time–frequency transform. The instantaneous frequencies (IFs) of the targeted components are then determined based on their orders relative to the IAS. Subsequently, the instantaneous amplitudes (IAs) of the targeted components are estimated by solving a joint optimization problem that includes a penalty term designed in consideration of the low variation characteristic of IAs. This penalty term enhances the efficient distribution of energy among close-spaced components. As a result, all targeted MTMs can be extracted simultaneously without any accumulated error. Various simulated experiments and practical experiments on a high-power planetary gearbox are conducted to analyze and validate the effectiveness of the proposed MTMD method in extracting robust features, especially in the presence of strong noise and close-spaced components.