Decomposition Characteristics Research Articles

Insight into the reaction characteristics of H2-reduction decomposition of gypsum: Experimental and isothermal kinetic analysis

In addition to its primary component, calcium sulfate, industrial by-product gypsum also contains trace amounts of heavy metals and soluble salts. When accumulated or landfilled over an extended period, this can lead to soil and groundwater pollution. Utilizing the H2 reduction decomposition of industrial by-product gypsum not only addresses the challenge of managing this waste but also offers a low CO2 emission method for producing quicklime. This study investigates the influence of reaction temperature, H2 concentration, and the PCO2/PH2 partial pressure ratio on the reductive decomposition of CaSO4 through experimental analysis. Subsequently, it delves into the kinetic characteristics of CaO production from the reductive decomposition of CaSO4 by H2, employing isothermal kinetic approaches. Finally, the feasibility of employing industrial by-product gypsum reduction and decomposition technology for quicklime preparation is explored. The results reveal that CaSO4 can be entirely decomposed into CaO when the temperature exceeds 1000 °C, H2 concentration is ≥ 2%, and the PCO2/PH2 ≥ 8. The interaction of CaS with CaSO4 is identified as the primary secondary reaction within the intermediate product CaS. When CaSO4 is decomposed in an atmosphere comprising 2% H2, 20% CO2, and N2, the apparent activation energies for the reaction, as determined by both the model-free and model-fitting methods, are 41.56 kJ/mol and 49.34 kJ/mol, respectively, with a modified reaction order of n = 1.748 and a modified model function of G(α) = [−ln (1-α)]0.572. Furthermore, the overall reaction rate is governed by the nucleation and growth rates of CaO. Lastly, the product quicklime obtained from the reductive decomposition of desulfurization gypsum and phosphogypsum using H2 contains over 90% CaO, complete absence of CaS, and conforms to the first-grade construction quicklime standards.

The deoxygenation mechanism of biomass thermal conversion with molten salts: Experimental and theoretical analysis

Utilizing molten salt for biomass pyrolysis has emerged as a pivotal technology for enhancing product value and has become a crucial alternative to traditional pyrolysis methods. However, there remains a dearth of research on the deoxygenation mechanism of biomass during molten salt thermal treatment. This study focuses on investigating the deoxygenation characteristics of biomass during molten salts thermal treatment through a comprehensive analysis involving experimental and density functional theory (DFT) study. Our experimental results revealed that, compared to traditional pyrolysis, molten salt (NaNO3–NaNO2) significantly increased oxygenated gas products, favoring deoxygenation during biomass conversion. Formic acid was employed as a simplified oxygen-containing model compound to investigate the decomposition characteristics with and without the presence of molten salts using DFT calculations. DFT studies of formic acid decomposition revealed lower activation energies on NaNO3 (234.39 kJ/mol) and NaNO2 (84.11 kJ/mol) surfaces compared to homogeneous reactions (234.63 kJ/mol). This indicates that NaNO2 has a more pronounced promotional effect, facilitating formic acid decomposition. Furthermore, kinetic calculations show that both homogeneous and heterogeneous reaction rate constants for formic acid decomposition increase with temperature. The reaction rate constants on the surface of NaNO3 are similar to homogeneous reactions, while it is much higher two on NaNO2 surfaces. These findings highlight the critical role of NaNO2 in formic acid decomposition within the NaNO3–NaNO2 molten salt system. The experimental and computational results elucidate the deoxygenation mechanism of biomass thermal conversion using molten salts and establish a theoretical foundation for the high-value utilization of pyrolysis products.

Assessment of self‐heating and spontaneous combustion of Napier grass using thermogravimetric analysis

AbstractNapier grass is a potential solid biofuel that can be used as a renewable energy resource. Nevertheless, its tendency for self‐heating and the subsequent self‐ignition remain unknown, which are common problems in solid fuel. In this work, thermal decomposition characteristics of Napier grass in air and inert conditions were investigated using thermogravimetric analysis (TGA) and the pertinent kinetic parameters were established using model‐free methods. The average activation energy of Napier grass thermal decomposition is 285 and 382 kJ/mol under oxidative and inert conditions, respectively. The linear plot obtained through model‐free methods shows a high linear correlation coefficient at conversion ranges of 0.2–0.7 during combustion and 0.4–0.6 during pyrolysis. The ignition and burnout temperatures were evaluated, and ignition index of Napier grass was established. Average ignition temperature obtained for air was 217°C while 200°C for inert condition. The burnout temperatures between 691 and 984°C were observed. This research facilitates in understanding fire threat posed by tendency of Napier grass to self‐heat and provides valuable information for safe conditions of Napier grass storage and transportation.

Thermal decomposition characteristics and spontaneous combustion phenomenon of emulsion explosive containing FeS2 impurity

AbstractIn the process of pyrite blasting mining, emulsion explosives in blast holes can sometimes spontaneously detonate prematurely. In order to reveal its accidental explosion mechanism, the effects of different content of FeS2 impurity on the thermal decomposition characteristics and thermal spontaneous combustion parameters of emulsion explosives were studied in detail. The experimental results of thermal analysis showed that pure emulsion explosive had a strong stability, but when it was doped with FeS2 impurity, its initial temperature of thermal decomposition reaction reduced and the decomposition rate accelerated. With the increase of FeS2 impurity content, the average activation energy of emulsion explosives decomposition decreased from 110.84 to 63.78 kJ/mol, resulting in an increasing risk of thermal runaway. The results of thermal spontaneous combustion experiments showed that FeS2 impurity would significantly shorten the ignition delay period and combustion flame duration of emulsion explosive, and increase the combustion flame temperature. With the increase of FeS2 impurity content in emulsion explosive, the ignition delay period and the duration of the flame continued to decrease and increase, respectively, and the maximum average temperature of the flame showed a trend of rising at first and then decreasing. The research results were of great significance to the accident identification of emulsion explosive accidental explosion in pyrite blasting operations.

Cognitive load detection using Ci-SSA for EEG signal decomposition and nature-inspired feature selection

Cognitive load detection using Ci-SSA for EEG signal decomposition and nature-inspired feature selection

Semi-supervised Learning Based on Graph Stochastic Co-Training

This article is devoted to the development of a new approach in semi-supervised machine learning. The goal of this article is to analyze the accuracy of the single-view co-training system, based on the use of a modified graph-based stochastic label propagation algorithm for a multiclass classification problem. Graph transformation of data is preceded by feature decomposition, with three algorithms being compared: Singular Value Decomposition, Truncated Singular Value Decomposition, Iterative Primary Component Analysis, Kernel Primary Component Analysis. To improve the accuracy of the proposed method, additional parameter was included in the label propagation algorithm, allowing for the usage of the algorithm in co-training systems. Further performance increases are achieved via optimization of data modification, which is achieved by applying feature decomposition methods and parallelizing the calculation-heavy processes. As examples of practical use were considered solutions to the problem of multiclass classification for standard datasets of the library sklearn and for the real dataset Traffic Signs Preprocessed. Analyses of the results of the implementation of the proposed approach showed improvements in accuracy and of performance solving the multiclass classification problem.

Thermal stability of levopimaric acid and its oxidation products

Biofuels are renewable alternatives to fossil fuels. Levopimaric acid‒base biofuels have attracted increasing attention. However, their stability remains a critical issue in practice. Thus, there is a strong impetus to evaluate the thermal stability of levopimaric acid. Through thermogravimetry (TG) and a custom-designed mini closed pressure vessel test (MCPVT) operating under isothermal and stepped temperature conditions, we investigated thermal oxidation characteristics of levopimaric acid under oxygen atmosphere. Thin-layer chromatography (TLC) and iodimetry were used to measure the hydrogen peroxides generated by levopimaric acid oxidation. A high pressure differential scanning calorimeter (HPDSC) was used to assess hydroperoxide thermal decomposition characteristics. Gas chromatography-mass spectrometry (GC-MS) was used to characterize the oxidation products. The thermal decomposition kinetics of levopimaric acid were thus elucidated, and a high peroxide value was detected in the levopimaric acid. The decomposition heat (QDSC) and exothermic onset temperature (Tonset) of hydroperoxides were 338.75 J g−1 and 375.37 K, respectively. Finally, levopimaric acid underwent a second-stage oxidation process at its melt point (423.15 K), resulting in complex oxidation products. Thermal oxidation of levopimaric acid could yield potential thermal hazards, indicating that antioxidants must be added during levopimaric acid application to protect against such hazardous effects.

Density functional studies on the thermal decomposition of H2O2 and heterogeneous oxidation of benzene on the Cu surface

The related research of volatile organic compounds (VOCs) is of great significance to atmospheric governance. Benzene is extremely harmful to humans and the environment as a representative substance in VOCs. In recent years, H2O2, a highly effective oxidant, has seen usage in flue gas purification. However, the purification efficiency remains limited due to the low utilization rate of hydroxyl radicals (·OH), which are derived from H2O2 decomposition. This research primarily focuses on catalyst selection. Density functional theory (DFT) was employed to investigate the characteristics of H2O2 adsorption and decomposition on Cu-based catalysts, as well as the ineffective consumption reaction of·OH on the Cu surface. Concurrently, the interaction mechanism between benzene and·OH on Cu surface was investigated. The findings demonstrate that Cu-based catalysts can enhance the decomposition of H2O2 molecules, and that H2O2 can dissociate to·OH on the surface. Moreover, these catalysts exert a pronounced inhibitory effect on the ineffective consumption reaction between·OH, H2O2 molecules, and the HO2·. The degradation process of benzene under the influence of·OH was analyzed, and the critical step of the reaction, along with the specifics of the ring-opening reaction, were determined. These reactions mechanisms were elucidated, thereby providing theoretical support for the development of novel catalysts.

Thermal decomposition characteristics and thermal safety performance evaluation of HAN-based propellant under different external environmental stimulations

This research aims to investigate thermal characteristics and assess thermal hazards of hydroxylamine nitrate (HAN)-based liquid propellant using a combination of accelerating rate calorimeter and high-pressure differential scanning calorimetry. Adiabatic experiments revealed that the exothermic reaction initiated at 115.7 °C and a rise sharply was at 128.4 °C with a maximum self-heating rate of 164.3 °C/min and the exothermic event was accompanied by a pressure rise of 3.7 bar in 0.03s. The corrected values of adiabatic temperature rise and time to maximum rate were 605.6 °C and 1.46 min, which confirms the vulnerability of the propellant to undergo a catastrophic explosion. To prevent thermal loss prevention accidents, time to maximum rate was obtained as 24 h under 111.3 °C. The apparent activation energy calculated decreased greatly with the increase of storage temperature from 25 °C to 45 °C, and so did the thermal explosion temperature (Tb) and self-accelerating decomposition temperature (TSADT). Additionally, the linear relationship between Ea and T was E = 991.7–2.7904T. The reliability of TSADT prediction was validated through slow cook-off test. Furthermore, the propellant exhibited more violent thermal decomposition under high pressure, resulting in a higher peak power. The thermokinetic parameters related to this phenomenon were identified, specifically at pressure of 4.0 MPa.

Self-similar, spatially localized structures in turbulent pipe flow from a data-driven wavelet decomposition

This study aims to extract and characterize structures in fully developed pipe flow at a friction Reynolds number of $\textit {Re}_\tau = 12\,400$ . To do so, we employ data-driven wavelet decomposition (DDWD) (Floryan & Graham, Proc. Natl Acad. Sci. USA, vol. 118, 2021, e2021299118), a method that combines features of proper orthogonal decomposition and wavelet analysis in order to extract energetic and spatially localized structures from data. We apply DDWD to streamwise velocity signals measured separately via a thermal anemometer at 40 wall-normal positions. The resulting localized velocity structures, which we interpret as being reflective of underlying eddies, are self-similar across streamwise extents of 40 wall units to one pipe radius, and across wall-normal positions from $y^+=350$ to $y/R=1$ . Notably, the structures are similar in shape to Meyer wavelets. Projections of the data onto the DDWD wavelet subspaces are found to be self-similar as well, but in Fourier space; the bounds of self-similarity are the same as before, except streamwise self-similarity starts at a larger length scale of $450$ wall units. The evidence of self-similarity provided in this study lends further support to Townsend's attached eddy hypothesis, although we note that the self-similar structures are detected beyond the log layer and extend to large length scales.

Deep neural network with empirical mode decomposition and Bayesian optimisation for residential load forecasting

In the context of a resilient energy system, accurate residential load forecasting has become a non-trivial requirement for ensuring effective management and planning strategy/policy development. Due to the highly stochastic nature of energy load profiles, it is difficult to predict accurately, and usually, predictions are error-prone. This paper explores the potential of Empirical Mode Decomposition (EMD) in simplifying the dynamics of complex demand profiles. The simplified components are then embedded within a deep learning model, specifically Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM), to forecast short-term residential loads. The novel modelling framework integrates Bayesian optimisation strategy, feature decomposition technique, feature engineering phase, and percentile-based bias correction algorithm to enhance model accuracy. The model is developed using a case-study residential dwelling located in Fintry (Scotland), and the model performance is assessed over four forecast horizons. The overall efficiency of framework is also investigated for three algorithms: random forest, gradient boosting decision trees (GBDT), and an LSTM network. While EMD and feature engineering were found to greatly improve prediction accuracy, the number of IMFs used was shown to significantly impact the model’s performance and computational complexity. The model was tested on two further case studies from Fintry.

Leak detection method of liquid-filled pipeline based on VMD and SVM

ABSTRACT In order to solve the problem of inconspicuous leakage signal characteristics under external noise interference, a leakage detection method based on the combination of variational modal decomposition (VMD) and support vector machine (SVM) is proposed. The method first calculates the spearman correlation coefficients (SCC) of multiple intrinsic modal components (IMFs) obtained by VMD with the source signal, then extracts the energy and central frequency features of IMFs with larger SCC, and finally performs leak detection using the SVM classifier. The experimental results show that the VMD-SVM method can effectively perform leak detection with an accuracy of 98.27%. The accuracy of the VMD-SVM method proposed in this paper is improved by 6.5%, 5.63% and 10.39% compared to the time-frequency (TF) feature SVM, empirical modal decomposition (EMD) feature SVM and wavelet (DWT) feature SVM, methods, respectively. In addition, feature sensitivities are analyzed to reduce model complexity while ensuring accuracy.

Development of a novel PLDLA/β-TCP composite-based biodegradable anterior cervical plate and screw system.

We investigated the effectiveness and safety of a new composite-based biodegradable anterior cervical plate/screw (ACPS) system for the anterior cervical discectomy and fusion (ACDF) fixation. A biocomposite in combination with 30 wt% β-tricalcium phosphate (β-TCP; a biodegradable ceramic having osteoconductive ability) and 70 wt% poly-l/d-lactide copolymer (PLDLA; a biodegradable polymer) was developed and used in the ACPS device, comprising one plate and four screws for fixation. Based on a literature review, a clinically required period of performance maintenance was set as 16 weeks, and to verify the performance for a period of 16 weeks or more, the test was conducted for 26 weeks. Following ISO 13781:2017 testing protocols, an in vitro degradation test was performed to verify the performance and evaluate the decomposition characteristics of the biodegradable ACPS system. Using an animal model as a preclinical investigation, the prepared ACPS device was implanted into five mongrel dogs weighing over 30 kg to evaluate the detachment prevention effect of the ACPS system on polyether ether ketone (PEEK) cage after ACDF. By week 26, the molecular weight was decreased by 45.35% for the plate and 52.56% for the screw; the bending strength of the plate was decreased by approximately 26.2% when compared with the initial stage. The torsional yield strength and pullout strength of the screw was increased by 52.31% and 5.3%, respectively by week 2 and then subsequently decreased until week 26. No detachment or dislocation of the inserted PEEK cage was observed for 26 weeks in vivo study. These findings recommend that the ACPS system might be a promising biodegradable tool for the fixation of interbody implants and supporting the fusion in an ACDF model. Furthermore, additional clinical trials are planned for the future.

Oxidation characteristic and thermal runaway of isoprene

In this study, the oxidation characteristics of isoprene were investigated using a custom-designed mini closed pressure vessel test (MCPVT). The results show that isoprene is unstable and polymerization occurs under a nitrogen atmosphere. Under an oxygen atmosphere, the oxidation process of isoprene was divided into three stages: (1) isoprene reacts with oxygen to produce peroxide; (2) Peroxides produce free radicals through thermal decomposition; (3) Free radicals cause complex oxidation and thermal runaway reactions. The oxidation of isoprene conforms to the second-order reaction kinetics, and the activation energy was 86.88 kJ·mol−1. The thermal decomposition characteristics of the total oxidation product and purified peroxide mixture were determined by differential scanning calorimetry (DSC). The initial exothermic temperatures Ton were 371.17 K and 365.84 K, respectively. And the decomposition heat QDSC were 816.66 J·g−1 and 991.08 J·g−1, respectively. It indicates that high concentration of isoprene peroxide has a high risk of thermal runaway. The results of thermal runaway experiment showed that the temperature and pressure of isoprene oxidation were prone to rise rapidly, which indicates that the oxidation reaction was dangerous. The reaction products of isoprene were analyzed by gas chromatography-mass spectrometry (GC–MS). The main oxidation products were methyl vinyl ketone, methacrolein, 3-methylfuran, etc. The main thermal runaway products were dimethoxymethane, 2,3-pentanedione, naphthalene, etc. Based on the reaction products, the possible reaction pathway of isoprene was proposed.

Intensification of the Process of Coal Ignition by Adding Biomass Under Flame Combustion Conditions

The characteristics of thermal decomposition and combustion processes on the heating of fine particles (100–200 m) of Chernogorsky coal, larch wood, and mixtures based on them, including the concentrations of the main components of flue gases (CO, CO2, NOx, and H2S + SO2), were determined using moderntechniques, analytical instrumentation, and experimental equipment. The biomass contents of the fuel mixtures based on coal were 10, 20, and 30 wt %. The temperatures at which the ignition of the coke residue occurred and the combustion process was completed were established using the synchronous thermal analysis of individual solid fuels and their mixtures. Larch sawdust was more reactive than Chernogorsky coal due to the lowest temperature at which the carbon residue was ignited; therefore, the addition of even 10% biomass to coal had a positive effect on the reactivity of the mixture. Under conditions of fuel heating in a flow of air at temperatures of 500–800C, the ignition delay times were determined using a hardware–software complex for high-speed video recording of fast processes. Based on the results of the experimental studies, it was found that the ignition delay times of the test fuels in a flow of heated air varied in a range from 0.02 to 0.22 s, and the addition of 10–30 wt % biomass to coal shortened the ignition delay times of fuel mixtures by up to 50%. The analysis of flue gases upon the combustion of solid fuels made it possible to establish the concentrations of the main anthropogenic emissions; the use of biomass as an additive to coal reduced the emissions of carbondioxide, nitrogen oxides, and sulfur compounds (H2S + SO2) by 2.2–13.5, 6.2–28.9, and 18.2–33.3%, respectively.

Miniature mass spectrometer signal processing based on ensemble empirical mode decomposition feature enhancement.

Miniature mass spectrometer signal processing based on ensemble empirical mode decomposition feature enhancement.

Податки солідарності: новітній досвід ЄС та перспективи для України

Introduction. The development of the taxation theory and the best practices of the World Anti-crisis Tax Policy prove the gradual separation of an independent and very specific group of taxes, called “solidarity taxes”, which are beginning to play a fairly significant role in modern tax policy. The intensive development of solidarity taxes and their implementation in the European fiscal space in 2022 are closely related to the crisis phenomena that have arisen in the field of energy prices and the associated increase in the inflation rate. This is one of the consequences of the military aggression of the Russian Federation against Ukraine. Another consequence of this war was the high need for investment necessary for the revival of Ukraine. Problem Statement. The problems of the design of solidarity taxes and their scope of application, as well as the possibilities and features of their use as a promising source of investment financing, remain insufficiently studied. And the latest experience of the second half of 2022-early 2023 regarding the coordinated application of taxes on unpredictable profits in EU countries requires critical analysis and generalization. Purpose. To study the economic content, evolution and regulatory framework of the application of solidarity taxes in the EU and the prospects for their implementation in Ukraine at the stage of post-war economic recovery. Methods. The author used analytical and qualitative research methods, including logical and comparative analysis, to identify general and special features of solidarity taxes, decomposition and grouping methods to study the element base and identify models of the studied taxes. Results. The theoretical basis and European practice of using solidarity taxes are analyzed, and the genesis of their development in the EU countries is studied. The principles of development and implementation of a common tax policy within the EU to counteract the consequences of the energy crisis through the introduction of a temporary contribution of solidarity, coping with unexpected profits in the energy sector and oil refining are considered. Attention is focused on the expediency, limitations and risks of the prospective application of solidarity taxes as its own source of investment financing at the stage of the post-war economic recovery of Ukraine. Conclusions. Solidarity taxes implementation is a promising direction for creating our own national sources of investment financing in the context of the post-war economic recovery. The introduction of solidarity taxes in Ukraine requires the adaptation of the European methodology to the specifics of the set goals and conditions of taxation in Ukraine, as well as scientific justification of restrictions on the application of this tax and preventive mechanisms to counteract the negative consequences of its implementation.

Precise Regulation of Combustion Characteristics of High-Energy Composite Propellants by Incorporation of CL-20 Crystals Intercalated with Energetic Burn Rate Modifiers.

In this study, a 2D structured triaminoguanidine-glyoxal polymer with a high nitrogen content has been coordinated with metal ions to produce energetic metal complexes (TAGP-Ms) employed as energetic burn rate inhibitors. The metal ions (Ba2+, K+, and Ca2+) are elaborately selected based on their ability of suppressing the burn rate of composite propellants. The CL-20 crystals were intercalated with prepared TAGP-Ms materials via a solvent-antisolvent method for realization of the precise control on burning behaviors of studied propellants. The influence of TAGP-Ms inhibitors on thermal decomposition and combustion characteristics of high-energy composite propellants was evaluated using thermal analysis and a combustion diagnostic method. Results of TGA/DSC-FTIR measurements suggest that the thermal decomposition of CL-20-containing composite propellants was found to be constrained by varied degrees as a result of TAGP-Ms additions, in which the TAGP-K displays a stronger effect on suppressing the thermal decomposition of CL-20 compared with that of other TAGP-Ms. The FTIR spectra indicate that the primary gaseous phase products are composed of N2O, H2O, and CO2 in CL-20 decomposition, as well as by HCl, H2O, NO2, and N2O in the decomposition of AP for all studied composite propellants. The combustion characterizations show that the TAGP-K-containing composite propellant exhibits a significantly reduced rate of heat release but is associated with a higher flame radiation intensity increased by 4.2% compared with that of the reference propellant, which clearly implies that the TAGP-K is capable of suppressing the energy release rate while ensuring the high energetic features of propellants to be well maintained. Moreover, the burn rate pressure exponents are considerably decreased by ∼10% for the TAGP-K-containing propellants in comparison with those of propellants with the typical formulation, which strongly suggests that TGAP-Ms are promising candidates for tuning the combustion behaviors of composite propellants by influencing the decomposition processes of CL-20 and AP collectively.

Intermolecular Hydrogen Bonding in Associated Fluids: The Case of Isopentyl Alcohol Dissolved in Carbon Tetrachloride.

Fourier-transform infrared (FTIR) spectra of isopentyl-alcohol dissolved in carbon tetrachloride (CCl4) were recorded as a function of concentration and temperature. Dilute isopentyl alcohol/CCl4 solutions were prepared in alcohol at concentrations of 1, 0.5, 0.3, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.001 and 0.0005 M. Infrared absorption measurements were taken within a temperature range of 17-67 °C below the boiling point of the solutions. Decomposition of the spectral features corresponding to associated and unassociated species was performed to quantitatively follow the effect of temperature and concentration on intermolecular hydrogen bonding (HB) in isopentyl alcohol. The spectral feature in the 3600-3650 cm-1 frequency range attributed to the free OH stretching band was studied in detail to determine changes based on concentration and temperature variations. Computational methodologies were applied to evaluate the energetics and vibrational properties of the species involved in the structure in the gaseous state where no interactions are present. The results are discussed in view of relevant structural models to gain quantitative information concerning the effect of concentration and temperature on intermolecular hydrogen bonding.

A novel multi-featured decision system for multi-classification tasks

Abstract Feature engineering is a difficult task, and for real signal data, it is difficult to find a certain feature that can easily distinguish all classes. Multiple features can provide more information, which means the fusion of multi-feature learning strategies has potential significant advantages. Based on this premise, this paper proposes a multi-class framework based on the multi-featured decision to distinguish all the different classes, and takes automatic dependent surveillance-broadcast (ADS-B) signal data as an example, first extracts the phase features and wavelet decomposition features of the signal data, then selects the features with high discrimination between classes, then proposes a one-dimensional residual neural network based on 16 convolutional layers to learn the unique features of different features and classes separately, and finally proposes a novel multi-featured decision method based on voting method and a priori probability. Results show that the proposed one-dimensional residual neural network has better performance metrics on the test set compared to some machine learning-based and neural network-based algorithms, with classification accuracies of 86.1%, 84.6% and 83.6% on wavelet decomposition features, raw features and phase features, respectively, on ADS-B preamble signals. The proposed feature decision framework based on the voting method and a priori probability has a recall, precision and F1 value of 80.24%, 89.89% and 84.79% on ADS-B preamble signals, respectively.