The diagnosis of compound faults in rolling bearings presents significant challenges, particularly in extracting discriminative features under low signal-to-noise ratio conditions and decoupling features embedded within overlapping resonance bands. To address these challenges, this paper proposes a new deconvolution inverse filter. First, a multimodal narrow-band filter is designed based on the shock pulse function for the initial deconvolution filter. Second, an objective function named envelope spectrum interference-to-harmonic ratio (ESIHR), to promote spectral decoupling, is designed to update the filter parameters via gradient descent. Finally, the filter parameters are optimized by minimizing the ESIHR. This process enhances the sparsity of the envelope spectrum by increasing fault-related information and reducing interference components, resulting in the effective separation of combined faults and noise suppression within overlapping resonance bands. The effectiveness of the proposed method is demonstrated through both simulation and experimental data. Comparison with common deconvolution methods confirms the superiority of the approach described in this article.

This study applies a green method combining sodium phytate and sodium hydroxide to prepare hierarchical porous biochar from shell-based biomass waste (e.g., pomelo peel, mandarin orange peel, orange peel, rice husk, and peanut shell). The resulting biochars have specific surface areas exceeding 2300 m2 g-1, with saturated adsorption capacities for chlorobenzene greater than 530 mg g-1. The pomelo peel-derived biochar exhibited an exceptionally high chlorobenzene adsorption capacity of 572.5 mg g-1. This outstanding performance is related to its high specific surface area (2331.19 m2 g-1), large micropore-specific surface area (1038.13 m2 g-1) and micropore volume (0.51 cm3 g-1), appropriate micro-mesoporous hierarchical structure (micropores accounting for 36.43%), abundant surface oxygen-containing functional groups, and disordered-graphitized microcrystalline structure. Volatile organic compound (VOC) adsorption experiments show that chlorobenzene adsorption has a distinct competitive advantage over benzene and toluene. This is attributed to its lower saturated vapor pressure, stronger polar interactions, smaller molecular kinetic diameter, and greater affinity and better matching with the available adsorption sites on the pomelo peel-derived biochar. By implementing green modification strategies, this work demonstrates high-value utilization of agricultural waste. The prepared hierarchical porous biochar exhibits excellent performance and significant application potential in the remediation of chlorinated VOCs, thus providing a novel pathway for the development of highly efficient VOC adsorption materials.

For a long time, the traditional motor manufacturing industry relies on the artificial hearing method to identify whether there is abnormal noise in the motor, thus leading to low efficiency and poor accuracy consistency. To solve these problems, a new prediction method based on the algorithm of harmonic percussion sound separation (HPSS) and continuous interphase sampling (CIS) of cochlear implants and the CNN-CBAM (Convolutional neural network based on Convolutional Block Attention Module) model, is proposed in this paper. Firstly, the original sound signals are separated into harmonic and percussive components by the HPSS algorithm, and then each component is processed by the CIS algorithm of cochlear implant to obtain electrode stimulation signal that can simulate human hearing. Subsequently, the classification task of motors are achieved by a deep learning model that combines CNN and CBAM. The proposed method is verified that the highest accuracy of 99.27% is achieved in the motor data set. Afterward for feature extraction, the results of ablation experiments with HPSS-CIS show that the average accuracy of this method is more than 4.5% higher than that of any single component. In addition, for the human auditory feature extraction method after HPSS processing, the CIS method is compared with the widely used Mel filter bank, and shows better performance.

In recent years, neural architecture search (NAS) has been proposed for automatically designing neural network architectures, which searches for network architectures that outperform novel human-designed convolutional neural network (CNN) architectures. Related research has always been a hot topic. This paper proposes a multi-objective evolutionary algorithm called the elitist non-dominated sorting crisscross algorithm (elitist NSCA) and applies it to neural architecture search, which considers two optimization objectives: the accuracy and network parameters. In the algorithm, an innovative search space borrowed from the latest residual block and dense connection is proposed to ensure the quality of the compact architectures. A variable-length crisscross optimization strategy, which creatively iterates the evolution through inter-individual horizontal crossovers and intra-individual vertical crossovers, is employed to simultaneously optimize the microstructure parameters and macroscopic architecture of the CNN. In addition, a corresponding mutation operator is added pertinently based on the performance of the proxy model, and the elitist strategy is improved through pruning to reduce the impact of abnormal fitnesses. The experimental results on multiple datasets show that the proposed algorithm has a higher accuracy and robustness than those of certain state-of-the-art algorithms.

Ensuring the privacy and security of sensitive information is paramount in the digital age. Quick response (QR) codes, known for their high data density and robust error correction capabilities, have become widely used, making them an ideal medium for secure data transmission. This paper introduces a novel data hiding method based on triplet differences, designed to embed secret information within QR code images. The proposed method manipulates the differences between pixel triplets in the QR code matrix, achieving high payload capacity while maintaining the structural integrity and readability of the QR code. By leveraging the inherent redundancy of the QR code matrix, this technique imperceptibly encodes hidden data and resists various forms of image processing attacks. Performance is evaluated based on data capacity, imperceptibility, and robustness to errors. Experimental results demonstrate that the triplet difference method significantly outperforms traditional data hiding techniques in terms of efficiency and security. This study highlights the potential applications of the triplet difference method in secure information sharing, digital watermarking, and authentication, paving the way for advanced data hiding techniques that enhance the security and practicality of QR code technology.

A palladium-catalyzed divergent cascade annulation reaction of unactivated cycloalkenes with alkynoic acids under aerobic conditions is accomplished. This catalytic protocol provides an efficient and economical strategy to accommodate a broad scope of (E)-γ-lactones fused five- or six-membered ring motifs with good regioselectivities and yields (57 examples, up to 94 % yield and E/Z up to 98 : 2). More importantly, the developed catalytic strategy is applicable to a wide array of unactivated cycloalkenes, including cyclopentene, cyclohexa-1,3-diene, cyclohexa-1,4-diene, cyclohepta-1,3-diene and conjugated 1,3-diene.

Reversible data hiding (RDH) is an advanced data protection technology that allows the embedding of additional information into an original digital medium while maintaining its integrity. Color images are typical carriers for information because of their rich data content, making them suitable for data embedding. Compared to grayscale images, color images with their three color channels (RGB) enhance data embedding capabilities while increasing algorithmic complexity. When implementing RDH in color images, researchers often exploit the inter-channel correlation to enhance embedding efficiency and minimize the impact on image visual quality. This paper proposes a novel RDH method for color images based on inter-channel correlation modeling and improved skewed histogram shifting. Initially, we construct an inter-channel correlation model based on the relationship among the RGB channels. Subsequently, an extended method for calculating the local complexity of pixels is proposed. Then, we adaptively select the pixel prediction context and design three types of extreme predictors. The improved skewed histogram shifting method is utilized for data embedding and extraction. Finally, experiments conducted on the USC-SIPI and Kodak datasets validate the superiority of our proposed method in terms of image fidelity.

Wavelet decomposition is pivotal for underwater image processing, known for its ability to analyse multi-scale image features in the frequency and spatial domains. In this paper, we propose a new biorthogonal cubic special spline wavelet (BCS-SW), based on the Cohen–Daubechies–Feauveau (CDF) wavelet construction method and the cubic special spline algorithm. BCS-SW has better properties in compact support, symmetry, and frequency domain characteristics. In addition, we propose a K-layer network (KLN) based on the BCS-SW for underwater image enhancement. The KLN performs a K-layer wavelet decomposition on underwater images to extract various frequency domain features at multiple frequencies, and each decomposition layer has a convolution layer corresponding to its spatial size. This design ensures that the KLN can understand the spatial and frequency domain features of the image at the same time, providing richer features for reconstructing the enhanced image. The experimental results show that the proposed BCS-SW and KLN algorithm has better image enhancement effect than some existing algorithms.

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of servers’ resource allocation has been widely studied. The limited computational resources of edge servers (ESs) cannot meet the different demands of terminal entities (TEs). This makes it a challenge to efficiently schedule computational tasks on ESs. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. This paper aims to investigate the dynamic tasks allocation problem between TEs and ESs and to meet the optimal benefits for both parties in MEC system. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. A bilateral game framework is applied to tackle the tasks allocation problem by modeling the problem as two noncooperative games: the supplier and customer side games. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A d istributed t ask o utsourcing a lgorithm (DTOA) is designed to determine the equilibrium. Our simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoffs, as well as flattening the peak and off-peak loads.

Accurately recognizing sound states in the production line of small electric motors is of great importance for manufacturers to carry out quick repairs and ensure high quality deliveries. Since the number of normal samples is much larger than the number of abnormal samples in practice, resulting in unbalanced data, which poses huge challenges to traditional detection methods. To overcome these difficulties, this study presents a morphological dictionary learning-based sparse classification (MDL-SC) combined with audio data augmentation method for small electric motor state recognition under unbalanced samples. Firstly, audio data augmentation methods such as adding background noise, pitch shifting, time stretching and combined augmentation are investigated for augmenting the number and diversity of samples. Secondly, morphological dictionary learning is proposed for characterizing transient sounds of small electric motors and enhancing the discriminative feature learning capability of the dictionary. Finally, the minimum reconstruction error strategy is relied upon to establish automatic recognition of small electric motor states. Three small motor datasets with unbalanced ratios are established in the experiments to verify the effectiveness of the proposed MDL-SC, which has higher recognition accuracy under unbalanced conditions compared with traditional dictionary learning based sparse classification (DL-SC), k-nearest neighbors, support vector machines and convolutional neural networks. This study can provide some theoretical implications for the later development of online detection of small electric motors or other types of electric motors.

Reversible data hiding is a technique that enables the secure embedding and complete extraction of data without reducing the quality of the carrier image. It has significant application value in fields such as medical images, military images, and digital forensics. However, existing reversible data hiding methods often need clarification on embedding capacity, image quality, and the trade-off between computational complexity and robustness. This paper proposes a reversible data hiding algorithm based on adaptive predictor and non-uniform payload allocation. The algorithm first uses an adaptive predictor to predict the image and then dynamically allocates different embedding bits according to the size and distribution of the prediction error, thus achieving non-uniform payload allocation. The algorithm only changes the low bits of the prediction error when embedding data, thus ensuring the high fidelity of the image quality. The algorithm can fully recover the original image when extracting data, thus achieving reversibility. The paper conducts experiments on various types of images, and the results show that the algorithm outperforms existing reversible data hiding methods in terms of embedding capacity and image quality while having lower computational complexity and stronger robustness.

Reversible data hiding (RDH) is a technique that embeds secret data into digital media while preserving the integrity of the original media and the secret data. RDH has a wide range of application scenarios in industrial image processing, such as intellectual property protection and data integrity verification. However, with the increasing prevalence of color images in industrial applications, traditional RDH methods for grayscale images are inadequate to meet the requirements of image fidelity. This paper proposes an RDH method for color images based on channel reference mapping (CRM) and adaptive pixel prediction. Initially, the CRM mode for a color image is established based on the pixel variation correlation between the RGB channels. Then, the pixel local complexity context is adaptively selected using the CRM mode. Next, each pixel value is adaptively predicted based on the features and characteristics of adjacent pixels and reference channels, and then data is embedded by expanding the prediction error. Finally, we compare seven existing RDH algorithms on the standard image dataset and the Kodak dataset to validate the advantages of our method. The experimental results demonstrate that our approach achieves average peak signal-to-noise ratio (PSNR) values of 63.61 and 60.53 dB when embedding 20,000 and 40,000 bits of data, respectively. These PSNR values surpass those of other RDH methods. These findings indicate that our method can effectively preserve the visual quality of images even under high embedding capacities.

针对传统的滚动轴承智能诊断模型计算效率低和准确率欠佳问题,课题组提出一种基于多点最优最小熵解卷积(multipoint optimal minimum entropy deconvolution adjusted,MOMEDA)和双向长短时记忆(bidirectional long short-term memory network,BiLSTM)网络相结合的滚动轴承故障诊断模型.该模型利用MOMEDA方法增强故障特征,并结合遗传算法(genetic algorithm,GA)对BiLSTM模型参数进行优化,实现滚动轴承智能、高效及鲁棒性诊断.利用该模型对经典轴承数据集以及牵引电机轴承故障数据集进行验证,平均准确率达到了 99.63％,分别比传统卷积神经网络(convolutional neural network,CNN)、单层长短时记忆网络(long short-term memory network,LSTM)、双向长短时记忆网络和最新的CNN-LSTM模型高16.02％,9.98％,7.01％和5.65％,验证了该模型的有效性和优越性.

Deep learning (DL)-based video satellite superresolution (SR) methods have recently yielded superior performance over traditional model-based methods by using an end-to-end manner. Existing DL-based methods usually assume that the blur kernels are known and, thus, do not model the blur kernels during restoration. However, this assumption is rarely held for real satellite videos and leads to oversmoothed results. In this article, we propose a Ghost module-based convolution network model for blind SR of satellite videos. The proposed Ghost module-based video SR (GVSR) method, which assumes that the blur kernel is unknown, consists of two main modules, i.e., the preliminary image generation module and the SR results' reconstruction module. First, the motion information from adjacent video frames and the wrapped images are explored by an optical flow estimation network, the blur kernel is flexibly obtained by a blur kernel estimation network, and the preliminary high-resolution image is generated by feeding both blur kernel and wrapped images. Second, a reconstruction network consisting of three paths with attention-based Ghost (AG) bottlenecks is designed to remove artifacts in the preliminary image and obtain the final high-quality SR results. Experiments conducted on Jilin-1 and OVS-1 satellite videos demonstrate that the qualitative and quantitative performance of our proposed method is superior to current state-of-the-art methods.

Hyperspectral anomaly detection (HAD) plays a vital role in military and civilian applications. However, compared with target detection or classification tasks, HAD is more challenging due to insufficient anomaly information and the difficulty of extracting local and global discriminative features. In this letter, a convolutional transformer-inspired autoencoder (CTA) is proposed for HAD. The CTA consists of a clustering-based module and an autoencoder-based module. First, note that the number of anomalies is small, and distinct from their surroundings, a clustering-based module is proposed to detect the pseudo-background and anomaly samples. Second, the autoencoder module is composed of an encoder and a decoder formed from several skip-connected convolutions and multihead attention-based transformers. The CTA is trained not only to distinguish the anomalies from the background but also to reconstruct the input hyperspectral images (HSIs). Benefiting from integrating the convolution and transformer, the CTA has local and global receptive fields. Moreover, both background and anomaly information explored by the clustering-based module can be adopted to improve the separability of anomalies. Experiments on two hyperspectral datasets demonstrate that the proposed CTA achieves superior detection performance to its counterparts. The code is available at https://github.com/hzhdhz/CTA.

To gain deeper insights into the microenvironment of breast cancer, we utilized GeoMx Digital Spatial Profiling (DSP) technology to analyze transcripts from 107 regions of interest in 65 untreated breast cancer tissue samples. Our study revealed spatial heterogeneity in the expression of marker genes in tumor cell enriched, immune cell enriched, and normal epithelial areas. We evaluated a total of 55 prognostic markers in tumor cell enriched regions and 15 in immune cell enriched regions, identifying that tumor cell enriched regions had higher levels of follicular helper T cells, resting dendritic cells, and plasma cells than immune cell enriched regions, while the levels of resting CD4 memory in T cells and regulatory (Treg) T cells were lower. Additionally, we analyzed the heterogeneity of HLA gene families, immunological checkpoints, and metabolic genes in these areas. Through univariate Cox analysis, we identified 5 prognosis-related metabolic genes. Furthermore, we conducted immunostaining experiments, including EMILIN2, SURF4, and LYPLA1, to verify our findings. Our investigation into the spatial heterogeneity of the breast cancer tumor environment has led to the discovery of specific diagnostic and prognostic markers in breast cancer.

The axle off-line detection is an important link to ensure the sound quality of the axle. The traditional methods mainly rely on condition indicators, psychoacoustic parameters and artificial intelligence, in which the generation mechanism of the abnormal signal is ignored, thus leading to low accuracy and poor interpretability. To solve these problems, a vehicle axle abnormal sound detection method based on abnormal sound mechanism is proposed in this paper. Firstly, the sound quality of 30 newly produced axles is determined by subjective evaluation, and it is clear that the tooth frequency impulse is the main reason for the abnormal sound of axles. Then, a new objective function, autocorrelation kurtosis, is used to deconvolute the axle vibration signal for the periodic impulse feature. Simulation and experimental results show that the proposed maximum autocorrelation kurtosis deconvolution (MACKD) is more effec-tive than maximum correlation kurtosis deconvolution (MCKD) and minimum entropy deconvolution (MED). On this basis, the impulse autocorrelation kurtosis index (IACK) is constructed and used to quan-tify the abnormal sound of the axle. The results show that the correlation between the proposed index and the subjective evaluation results is more than 0.9, which can better identify the sound quality of the axle.(c) 2023 Elsevier Ltd. All rights reserved.

This article proposes an improved wavelet threshold denoising for laser self-mixing interference signals. The improved wavelet threshold function exhibits smoothness and continuity near the threshold. By replacing hard or soft wavelet threshold with the improved wavelet threshold, it can eliminate the generation of fake self-mixing interference peaks due to local oscillation induced by hard wavelet threshold, as well as the loss of self-mixing interference peaks due to over-smoothness induced by the soft wavelet threshold. Compared with hard and soft wavelet threshold denoising, theoretical simulations and experimental results demonstrate that the displacement of vibrations are well reconstructed based on the improved wavelet threshold denoising.