Decomposition Characteristics Research Articles

Study the Utilization of Biochar Derived from Sugarcane Waste to Enhance the Biogas Efficiency During the Anaerobic Digestion Process for Treating Pig Farm Wastewater

This experiment aims to study the use of biochar (Biochar, BC) from sugarcane trash through the pyrolysis process at different temperatures of 300, 400, 500, 600 and 700°C. Then add BC to the Anaerobic Digestion (AD) system of wastewater from pig farms, then analyze the characteristics of wastewater and the amount of biogas obtained before and after treatment and to study the characteristics of decomposition, the generation of biogas (Biogas) to the reduction of organic matter in wastewater (COD). From this experiment, it can be concluded that all 6 systems are producing biogas, such as: the normal system and the system with BC filling at different burning temperatures: 300, 400, 500, 600 and 700℃ in the amount of 0.4g into the system. .64, 46.13, 51.90 and 65.98% respectively. From the experiments of all systems, it is observed that the system filled with BC at a temperature of 700℃ is able to produce biogas the best because the high heating temperature will cause the electrons of the charcoal to break easily and cause more holes to be a place for microbes to grow well. A comparison of the conventional system and adding biochar with the same amount but different temperature into the sewage treatment system without using air can be concluded that the conventional system is the generation of biogas is still low efficiency and the system of adding BC at the temperature of 300, 400, 500, 600℃ in the same amount. Notice that the generation of biogas is higher in order and the filling of BC at a temperature of 700℃ is able to produce biogas higher than other systems.

Decomposition characteristics and influencing mechanisms of C4F7N/CO2 gas with different metal materials

C4F7N/CO2 gas mixture, as the main new environmentally friendly gas insulation medium, is now being widely used in the ring network switchgear and other gas electrical equipment. In this paper, a test platform was constructed to simulate partial discharge in a gas medium, the decomposition characteristics of the C4F7N/CO2 gas mixture during suspended potential discharge were studied under four different metal electrode materials: stainless steel, purple copper, tungsten copper, and aluminum alloy. The results show that the main decomposition products of the C4F7N/CO2 gas mixture during suspended discharge are CO, CF4, C4F8, C3F8, C3F6, CF3CN, and C2F4 gases, which are independent of the metal material. On the whole, when the metal electrode material is stainless steel, the highest amount of decomposition products are generated from the C4F7N/CO2 gas mixture; when the metal electrode material is aluminum alloy, the amount of decomposition products produced by C4F7N/CO2 gas mixture is the least, and the content difference of some decomposition products between these two metal materials is as high as 70%. The decomposition products of the C4F7N/CO2 gas mixture decreased gradually with the increase in the gas pressure. Finally, the influence mechanism of different metal materials on the decomposition process of the C4F7N/CO2 gas mixture was analyzed from the microscopic perspectives of charge transfer between gas molecules and molecular structural characteristics. In general, the research results can provide technical reference for the design, selection, and optimization of environmental protection gas insulation equipment.

DETrack: Multi-Object Tracking Algorithm Based on Feature Decomposition and Feature Enhancement

DETrack: Multi-Object Tracking Algorithm Based on Feature Decomposition and Feature Enhancement

WaveFusionNet: Infrared and visible image fusion based on multi-scale feature encoder–decoder and discrete wavelet decomposition

To merge complementary information from multimodal images, such as thermal saliency from infrared images and texture details from visible images, traditional multi-scale transform-based methods have been extensively studied, with deep learning-based methods gaining significant popularity in recent years. However, there has been limited research on optimally combining the advantages of these two categories in fusion. In this paper, we propose a novel infrared and visible image fusion (IVIF) framework, WaveFusionNet, which integrates precise frequency feature decomposition from the discrete wavelet transform (DWT) with the comprehensive feature extraction from the multi-scale encoder. Firstly, we train an encoder–decoder network for multi-scale feature extraction and image reconstruction. DWT is used for down-sampling with minimal information loss by decomposing extracted features into low and high-frequency sub-bands. Next, a dual-band feature fusion (DBFF) module is trained to merge these sub-bands by integrating a spatial feature transform-based sub-network for low-frequency fusion and a maximum absolute value selection strategy for fusing high-frequencies. Finally, all fused sub-bands are fed into the pre-trained decoder to reconstruct the final image. Experimental results on three benchmark datasets (TNO, Roadscene, and MSRS) demonstrate that the proposed fusion method outperforms recent IVIF methods in both quantitative assessment and visual perception while maintaining competitive time complexity.

MBHFuse: A multi- branch heterogeneous global and local infrared and visible image fusion with differential convolutional amplification features

Fusing infrared and visible imagery aims to harness their complementary spectral data, enhancing output image quality, sharpness, and content. However, convolutional neural networks (CNNs) are not capable of capturing long-range dependencies, while the Transformer architecture faces the challenge of consuming huge computational resources. To address these critical challenges, this paper proposes a novel framework named MBHFuse, which employs a multi-branch heterogeneous global and local image fusion approach. To effectively extract global and local features, we design a multi-branch heterogeneous encoder module and introduce a differential convolution amplification module (DCAM) to further extract complementary information. Additionally, we devise a new loss function, incorporating multi-branch feature decomposition loss, intensity loss, gradient loss, mean squared error loss, and structural similarity loss, for training the proposed MBHFuse model. Through extensive experiments on public datasets, we demonstrate that the proposed framework outperforms other state-of-the-art (SOTA) methods in both qualitative and quantitative evaluations. Our code will be available at https://github.com/sunyichen1994/MBHFuse.

A two-step bearing fault diagnosis strategy under variable speed based on symplectic geometry modal decomposition and practical fault feature extraction framework

Abstract Strong noise interference can lead to failure of bearing fault diagnosis techniques. This paper proposes a two-step fault diagnosis strategy to address the challenge of weak fault feature extraction in bearing fault diagnosis using acoustic or vibration data at varying speed. Firstly, the paper introduces a short-time symplectic modal decomposition (stSGMD) method that utilizes fractional Fourier transform. This method involves signal processing with short-time windowing to extract fault-sensitive components. The window is then expanded to obtain the complete component through fractional Fourier narrow-band filtering based on energy concentration in the fractional Fourier domain. A novel entropy index, standard deviation discrete entropy, is introduced to quantify the intensity of fault shocks in non-stationary signal and is used to select components in the stSGMD. Subsequently, a fault feature extraction framework called global objective deconvolution (GOD) is presented for extracting instantaneous fault features at varying speed. This method establishes a global objective matrix for the extraction process. The GOD is utilized to deconvolute the complete fault-sensitive component, followed by envelope order analysis for demodulating the fault feature order. Numerical simulations and experimental studies on acoustics and vibration are performed. The results demonstrate that stSGMD improves the demodulation capability of SGMD, while GOD effectively extracts fault features. It is expected that the presented method will be effectively utilized for fault feature extractions in bearings operating under linear variable speed conditions.

Micro/meso-porous Double-shell Hollow Carbon Spheres through Spatially Confined Pyrolysis for Supercapacitors and Zinc-ion Capacitor.

Energy storage in supercapacitors and hybrid zinc ion capacitors (ZIC) using porous carbon materials offers a promsing alternative method for clean energy solutions. The unique combination of hierarchical porous structure and nitrogen doping in these materials has demonstrated significant capacity for energy storage. Nevertheless, the full potential of these materials, particularly the relationship between pore structure configuration and performance, remains underexplored. Herein, a confined pyrolysis strategy based on the polymerization characteristics of polydopamine (PDA) was developed to construction of hollow carbon spheres with microporous/mesoporous dual shell structure. The depth of micropores and cavity can be controlled by adjusting the duration of heat treatment and hydrothermal treatment, in accordance with the decomposition and polymerization characteristics of PDA. Due to the elasticity of this structure, the relationship between the micro/mesoporous depth of the prepared carbon spheres and the energy storage performance in supercapacitors and ZIC is established. Through optimizing the ion transport capacity of carbon spheres and considering the influence of its internal cavity structure on energy storage, the resulting carbon spheres exhibit high specific capacitance of 389 F g-1 in supercapacitor and specific capacitance of 260 F g-1 and excellent stability with 99.3% retention after 30000 chare/discharge cycles in ZIC.

Rolling Bearing Fault Diagnosis Based on SABO-VMD and WMH-KNN.

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH-KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH-KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO-VMD and WMH-KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.

An unsupervised video anomaly detection method via Optical Flow decomposition and Spatio-Temporal feature learning

The purpose of this paper is to present an unsupervised video anomaly detection method using Optical Flow decomposition and Spatio-Temporal feature learning (OFST). This method employs a combination of optical flow reconstruction and video frame prediction to achieve satisfactory results. The proposed OFST framework is composed of two modules: the Multi-Granularity Memory-augmented Autoencoder with Optical Flow Decomposition (MG-MemAE-OFD) and a Two-Stream Network based on Spatio-Temporal feature learning (TSN-ST). The MG-MemAE-OFD module is composed of three functional blocks: optical flow decomposition, autoencoder, and multi-granularity memory networks. The optical flow decomposition block is used to extract the main motion information of objects in optical flow, and the granularity memory network is utilized to memorize normal patterns and improve the quality of the reconstructions. To predict video frames, we introduce a two-stream network based on spatiotemporal feature learning (TSN-ST), which adopts parallel standard Transformer blocks and a temporal block to learn spatiotemporal features from video frames and optical flows. The OFST combines these two modules so that the prediction error of abnormal samples is further increased due to the larger reconstruction error. In contrast, the normal samples obtain a lower reconstruction error and prediction error. Therefore, the anomaly detection capability of the method is greatly enhanced. Our proposed model was evaluated on public datasets. Specifically, in terms of the area under the curve (AUC), our model achieved an accuracy of 85.74% on the Ped1 dataset, 99.62% on the Ped2 dataset, 93.89% on the Avenue dataset, and 76.0% on the ShanghaiTech Dataset. Our experimental results show an average improvement of 1.2% compared to the current state-of-the-art.

Collaborative disposal of exhaust gas and waste wind turbine blades: Mechanical properties of recycled glass fibres and economic analysis

The accumulation of waste wind turbine blades poses significant environmental challenges. Pyrolysis offers an industrially viable solution for both disposal and the recycling of valuable glass fibers from these blades. Exhaust gas from industrials with waste heat can be utilized to reduce the process cost. However, for mixed atmosphere of exhaust gas containing both CO2 and O2, its influence on resin matrix decomposition characteristics and fiber mechanical properties are currently unclear. This work provided a solution collaborated with exhaust gas to disposal of waste wind turbine blades, in which operating parameters (atmosphere composition and process procedure) were optimized. Results showed that the early invention at pyrolysis with low concentration of O2 in exhaust gas played a major role in facilitating the formation of surface protection. Atmosphere during oxidation of pyrolytic residual carbon was crucial in maintaining fiber strength. At this stage, the interaction between CO2 (especially at 15% concentration) and O2 could mitigating fiber degradation through promoting the re-oxidization of Si dangling bonds and reducing water corrosion. However, stepwise processing of pyrolysis first and then oxidation under exhaust gas with O2 containing was not recommended due to its little cost-effectiveness. The single step disposal of wind turbine blades collaborated with coal-fired flue gas could increase the strength of recycled glass fiber by over 50% and reduced process costs by 34%.

Feature decomposition-based gaze estimation with auxiliary head pose regression

Recognition and understanding of facial images or eye images are critical for eye tracking. Recent studies have shown that the simultaneous use of facial and eye images can effectively lower gaze errors. However, these methods typically consider facial and eye images as two unrelated inputs, without taking into account their distinct representational abilities at the feature level. Additionally, implicitly learned head pose from highly coupled facial features would make the trained model less interpretable and prone to the gaze-head overfitting problem. To address these issues, we propose a method that aims to enhance task-relevant features while suppressing other noises by leveraging feature decomposition. We disentangle eye-related features from the facial image via a projection module and further make them distinctive with an attention-based head pose regression task, which could enhance the representation of gaze-related features and make the model less susceptible to task-irrelevant features. After that, the mutually separated eye features and head pose are recombined to achieve more accurate gaze estimation. Experimental results demonstrate that our method achieves state-of-the-art performance, with an estimation error of 3.90° on the MPIIGaze dataset and 5.15° error on the EyeDiap dataset, respectively.

Thermal stability of the DPT reaction system synthesized by acetic anhydride method

The adiabatic decomposition of a reaction mixture prepared in DPT using the acetic anhydride method was analyzed using an Accelerating Rate Calorimeter (ARC) to determine the thermal decomposition characteristics and kinetic parameters of the reaction solution at different reaction times. The adiabatic decomposition kinetics were modeled using TSS software, and the corresponding kinetic parameters were derived. Subsequently, a numerical simulation was employed to predict the critical ambient temperature of the reaction liquid in the reactor in case of cooling failure. The results indicated that the CT value was lowest at a feeding time of 10 minutes, highest at a holding time of 15 minutes, and closest to the cooling temperature of the reactor at a feeding time of 30 minutes.

Preparation of Zn-Co Bimetallic Catalysts and Their Catalytic Properties for Thermal Decomposition of Ammonium Perchlorate.

This research delves into the preparation of heteronuclear bimetallic catalysts and explores their catalytic properties in the thermal decomposition of ammonium perchlorate (AP). The study's central focus is on enhancing the thermal decomposition characteristics of AP and, consequently, the combustion performance of composite solid propellants. The synthesized materials underwent structural characterization by XRD, XPS, SEM, and FTIR. Catalytic properties were examined using DTA tests. Notably, catalysts derived from calcination at 500 °C exhibited heightened catalytic activity. They advanced the pyrolysis temperature by 135.4 °C and reduced the activation energy by 82.38 kJ/mol compared with pure AP. To further elucidate the decomposition mechanism of AP, the investigation also employed a combined approach involving DSC-TG-FTIR-MS analysis.

Deep underwater image compression for enhanced machine vision applications

Underwater image compression is fundamental in underwater visual applications. The storage resources of autonomous underwater vehicles (AUVs) and underwater cameras are limited. By employing effective image compression methods, it is possible to optimize the resource utilization of these devices, thereby extending the operational time underwater. Current image compression methods neglect the unique characteristics of the underwater environment, thus failing to support downstream underwater visual tasks efficiently. We propose a novel underwater image compression framework that integrates frequency priors and feature decomposition fusion in response to these challenges. Our framework incorporates a task-driven feature decomposition fusion module (FDFM). This module enables the network to understand and preserve machine-friendly information during the compression process, prioritizing task relevance over human visual perception. Additionally, we propose a frequency-guided underwater image correction module (UICM) to address noise issues and accurately identify redundant information, enhancing the overall compression process. Our framework effectively preserves machine-friendly features at a low bit rate. Extensive experiments across various downstream visual tasks, including object detection, semantic segmentation, and saliency detection, consistently demonstrated significant improvements achieved by our approach.

Artifact feature purification for cross-domain detection of AI-generated images

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, brings potential security risks to our society. Existing generated image detection methods suffer from performance drops when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For the explicit one, a suspicious frequency-band proposal method and a spatial feature decomposition method are proposed to extract artifact-related features. For the implicit one, a training strategy based on mutual information estimation is proposed to further purify the artifact-related features. The experiments are conducted in two settings. Firstly, we perform a cross-generator evaluation, wherein detectors trained using data from one generator are evaluated on data generated by other generators. Secondly, we conduct a cross-scene evaluation, wherein detectors trained for a specific domain of content (e.g., ImageNet) are assessed on data collected from another domain (e.g., LSUN-Bedroom). Results show that for cross-generator detection, the average accuracy of APN is 5.6%∼16.4% higher than the previous 11 methods on the GenImage dataset and 1.7%∼50.1% on the DiffusionForensics dataset. For cross-scene detection, APN maintains its high performance. Via visualization analysis, we find that the proposed method can extract diverse forgery patterns and condense the forgery information diluted in irrelated features. We also find that the artifact features APN focuses on across generators and scenes are global and diverse. The code will be available at https://github.com/RichardSunnyMeng/APN-official-codes.

Phase Noise Compensation Algorithm for Space-Borne Azimuth Multi-Channel SAR.

Azimuth multi-channel synthetic aperture radar (SAR) has always been an important technical means to achieve high-resolution wide-swath (HRWS) SAR imaging. However, in the space-borne azimuth multi-channel SAR system, random phase noise will be produced during the operation of each channel receiver. The phase noise of each channel is superimposed on the SAR echo signal of the corresponding channel, which will cause the phase imbalance between the channels and lead to the generation of false targets. In view of the above problems, this paper proposes a random phase noise compensation method for space-borne azimuth multi-channel SAR. This method performs feature decomposition by calculating the covariance matrix of the echo signal and converts the random phase noise estimation into the optimal solution of the cost function. Considering that the phase noise in the receiver has frequency-dependent and time-varying characteristics, this method calculates the phase noise estimation value corresponding to each range-frequency point in the range direction and obtains the phase noise estimation value by expectation in the azimuth direction. The proposed random phase noise compensation method can suppress false targets well and make the radar present a well-focused SAR image. Finally, the usefulness of the suggested method is verified by simulation experiments.

Synthetic-Aperture Radar Radio-Frequency Interference Suppression Based on Regularized Optimization Feature Decomposition Network

Synthetic-aperture radar (SAR) can work in all weather conditions and at all times, and satellite-borne radar has the characteristics of short revisiting period and large imaging width. Therefore, satellite-borne synthetic-aperture radar has been widely deployed, and the SAR images have been widely used in geographic mapping, radar interpretation, ship detection, and other fields. Satellite-borne synthetic-aperture radar is also susceptible to various types of intentional or unintentional interference during the imaging process, and because the interference is a direct wave, its power is much stronger than the wave reflected by targets. As a common interference pattern, radio-frequency interference widely exists in various satellite-borne synthetic-aperture radars, which seriously deteriorates SAR image quality. In order to solve the above problems, this paper proposes a feature decomposition network to suppress interference based on regularization optimization. The contributions of this work are as follows: 1. By analyzing the performance limitations of the existing methods, this work proposes a novel regularization method for radio-frequency interference suppression tasks. From the perspective of data distribution histograms and residual components, the proposed method eliminates the variable components introduced by common regularization, greatly reduces the difficulty of data mapping, and significantly improves its robustness and performance. 2. This work proposes a feature decomposition network, where the feature decomposition module contains two parts; one part only represents the interference signal, and the other part only represents the radar signal. The neurons representing the interference signal are discarded, and the neurons representing the radar signal are used as input for the subsequent network. A cosine similarity constraint is used to separate the interference from the network as much as possible. Finally, this method is validated on the MiniSAR dataset and Sentinel-1A dataset.

Locally sparse and robust partial least squares in scalar-on-function regression

We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our proposed method involves computing the functional partial least squares components through sparse partial robust M regression, facilitating robust and locally sparse estimations of the regression coefficient function. This strategy delivers a robust decomposition for the functional predictor and regression coefficient functions. After the decomposition, model parameters are estimated using a weighted loss function, incorporating robustness through iterative reweighting of the partial least squares components. The robust decomposition feature of our proposed method enables the robust estimation of model parameters in the scalar-on-function regression model, ensuring reliable predictions in the presence of outliers and leverage points. Moreover, it accurately identifies zero and nonzero sub-regions where the slope function is estimated, even in the presence of outliers and leverage points. We assess our proposed method’s estimation and predictive performance through a series of Monte Carlo experiments and an empirical dataset—that is, data collected in relation to oriented strand board. Compared to existing methods our proposed method performs favorably. Notably, our robust procedure exhibits superior performance in the presence of outliers while maintaining competitiveness in their absence. Our method has been implemented in the robsfplsr package in .

Investigation on ignition characteristics of charring conductive polymers stimulated by electric energy

The arc ignition based on charring conductive polymers has advantages of simple structure, low ignition power consumption and restart capacity, which bringing it broadly application prospect in hybrid propulsion system of micro/nano satellite. In order to optimize the performance of arc ignition system, it is essential to have a deeper understanding of the ignition processes and ignition characteristics of charring conductive polymers. In this paper, the thermal decomposition, electrical conductivity and thermal conductivity characteristics of charring conductive polymers with different conductive additives and matrix materials were comprehensively evaluated. An experimental investigation was conducted to determine the ignition behaviors and characteristics of different charring conductive polymers in a visual ignition combustor. The experiment result showed that the ignition delay and external energy required for ignition are negatively correlated with voltage and initial temperature of the ignition grain, but positively correlated with oxidizer flow velocity. Compared with charring conductive polymers containing multi-walled carbon nanotube, the ignition delay of charring conductive polymers with carbon black is significantly higher and the pyrolysis time is relatively longer. However, the ignition and initial flame propagation of charring conductive polymers with carbon black is more violent and more inclined to carbon particle ignition. Finally, the restart characteristic of different charring conductive polymers was studied. The ignition delay and external energy required for ignition of different charring conductive polymers all reduced with the increasing of the number of ignitions. However, the ignition characteristics would not change a lot after repeated ignition.

Manufacturing and characterization of functionally gradient material from cemented carbide to diamond via filament-based material extrusion 3D printing

Functionally gradient material (FGM) fabricating via additive manufacturing draws great attention on alleviating the abrupt material discontinuity at the interface of different materials. Introducing gradient structure into polycrystalline diamond composites to produce diamond/cemented carbide FGM is an effective method to reduce the residual stress and enhance the bonding strength of diamond layer and cemented carbide substrate. However, achieving a smooth transition of material properties from cemented carbide to diamond is challenging. In this research, diamond/cemented carbide FGM with a well-controlled gradient was manufactured via filament-based material extrusion. Initially, green bodies of diamond/cemented carbide FGM were obtained by printing with customized composite filaments. Subsequently, thermal decomposition characteristic of the printing filament was analyzed by TGA, and the influence of different heating rates during the binder decomposition stage on the morphology and structure of the green bodies was investigated. After debinding, diamond/cemented carbide FGM without flaw was synthesized at 6.5GPa and 1550℃. Subsequently, microstructure characterization was performed to investigate the distribution characteristics of the materials inside the diamond/cemented carbide FGM. A continuous transition from tungsten carbide to diamond was achieved inside the gradient layer. Furthermore, the stress state of diamond in different positions of the diamond/cemented carbide FGM was analyzed by Raman spectroscopy. It was found that all the residual stresses in the axial interface of the gradient layer were compressive stress, which can prevent occurrence of microcracks resulted from the tensile stresses formed in the interface. Finally, the impact test reveal that gradient structure is conductive to improving the bond strength between PCD layer and cemented carbide substrate as the impact times before fracture of diamond/cemented carbide FGM has been increased by 15%. The gradient structure design strategy, generated via the filament-based material extrusion technology, pioneers new ideas to the development of high-performance FGMs in mass production.