Search Results (16)

Mycelium-based composites (MBCs) have emerged as eco-friendly alternatives, utilizing fungal mycelium as a natural binder for agro-industrial residues. This study focuses on developing an MBC based on abundant waste in Colombia, pith Arboloco (A) (Montanoa quadrangularis), a plant endemic to the Colombian–Venezuelan Andes with outstanding insulating properties, and natural fiber of Kikuyu grass (G) (Cenchrus clandestinus), utilizing Ganoderma lucidum as an agent to form a mycelium network in the MBC. Three formulations, T (100% A), F1 (70% A/30% G), and F2 (30% A/70% G), were evaluated under two different Arboloco particle size ranges (1.0 to 5.6 mm) for their physical, mechanical, and thermal properties. The Arboloco particle sizes did not show significant differences in the MBC properties. An increase in Kikuyu grass proportion (F2) demonstrated superior density (60.4 ± 4.5 kg/m³), lower water absorption (56.6 ± 18.4%), and better compressive strength (0.1686 MPa at 50% deformation). Both mixing formulations (F1–F2) achieved promising average thermal conductivity and specific heat capacity values of 0.047 ± 0.002 W m⁻¹ K⁻¹ and 1714 ± 105 J kg⁻¹ K⁻¹, comparable to commercial insulation materials. However, significant shrinkage (up to 53.6%) and high water absorption limit their scalability for broader applications. These findings enhance the understanding of MBC’s potential for non-structural building materials made of regional lignocellulosic waste, promoting a circular economy in waste management for developing countries. Full article

Noises on the field can affect the electromechanical system characteristics in the bearing fault diagnostic process. This paper presents a deep learning-based fault-diagnosis model DRSN–GCE (Deep Relative Shrinkage Network with Gated Convolutions and Enhancements), which is designed to deal with noise and improve noise resistance. In the first step, the data are preprocessed by adding different noises with different ratios of signal to noise and different frequencies to the vibration signals. This simulates the field noise environments. The continuous wavelet transformation (CWT), which converts the time-series signal from one dimension to a time-frequency two-dimensional image, provides rich data input for the deep learning model. Secondly, a convolutional gated layer is added to the deep residual network (DRSN), which suppresses the noise interference. The residual connection structure has also been improved in order to improve the transfer of features. In complex signals, the Gated Convolutional Shrinkage Module is used to improve feature extraction and suppress noise. The experiments on the Case Western Reserve University bearing dataset show that the DRSN–GCE exhibits high diagnostic accuracy and strong noise immunity in various noise environments such as Gauss, Laplace, Salt-and-Pepper, and Poisson. DRSN–GCE is superior to other deep learning models in terms of noise suppression, fault detection accuracy, and rolling bearing fault diagnoses in noisy environments. Full article

Rolling bearing vibration signals in rotating machinery exhibit complex nonlinear and multi-scale features with redundant information interference. To address these challenges, this paper presents a multi-scale channel mixing convolutional network (MSCMN) and an enhanced deep residual shrinkage network (eDRSN) for improved feature learning and fault diagnosis accuracy in industrial settings. The MSCMN, applied in the initial and intermediate network layers, extracts multi-scale features from vibration signals, providing detailed information. By incorporating 1 × 1 convolutional blocks, the MSCMN mixes and reduces the feature dimensions, generating attention weights to suppress the interference from redundant information. Due to the high noise and nonlinear nature of industrial vibration signals, traditional linear layer representation is often inadequate. Thus, we propose an eDRSN with a Kolmogorov–Arnold Network–linear layer (KANLinear), which combines linear transformations with B-spline interpolation to capture both linear and nonlinear features, thereby enhancing threshold learning. Experiments on datasets from Case Western Reserve University and our laboratory validated the efficacy of the MSCMN-eDRSN model, which demonstrated improved diagnostic accuracy and robustness under noisy, real-world conditions. Full article

The robotic arm frequently performs grasping tasks in unstructured environments. However, due to the complex network architecture and constantly changing operational environments, balancing between grasping accuracy and speed poses significant challenges. Unlike fixed robotic arms, mobile robotic arms offer flexibility but suffer from relatively unstable bases, necessitating improvements in disturbance resistance for grasping tasks. To address these issues, this paper proposes a light-weight grasping pose estimation method called Grasp-DSC, specifically tailored for mobile robotic arms. This method integrates the deep residual shrinkage network and depthwise separable convolution. Attention mechanisms and soft thresholding are employed to improve the arm’s ability to filter out interference, while parallel convolutions enhance computational efficiency. These innovations collectively enhance the grasping decision accuracy and efficiency of mobile robotic arms in complex environments. Grasp-DSC is evaluated using the Cornell Grasp Dataset and Jacquard Grasp Dataset, achieving 96.6% accuracy and a speed of 14.4 ms on the former one. Finally, grasping experiments conducted on the MR2000-UR5 validate the practical applicability of Grasp-DSC in practical scenarios, achieving an average grasping success rate of 96%. Full article

Accurate photovoltaic (PV) power forecasting is crucial for effective smart grid management, given the intermittent nature of PV generation. To address these challenges, this paper proposes the Temporal Bottleneck-enhanced Bidirectional Temporal Convolutional Network with Multi-Head Attention and Autoregressive (TB-BTCGA) model. It introduces a temporal bottleneck structure and Deep Residual Shrinkage Network (DRSN) into the Temporal Convolutional Network (TCN), improving feature extraction and reducing redundancy. Additionally, the model transforms the traditional TCN into a bidirectional TCN (BiTCN), allowing it to capture both past and future dependencies while expanding the receptive field with fewer layers. The integration of an autoregressive (AR) model optimizes the linear extraction of features, while the inclusion of multi-head attention and the Bidirectional Gated Recurrent Unit (BiGRU) further strengthens the model’s ability to capture both short-term and long-term dependencies in the data. Experiments on complex datasets, including weather forecast data, station meteorological data, and power data, demonstrate that the proposed TB-BTCGA model outperforms several state-of-the-art deep learning models in prediction accuracy. Specifically, in single-step forecasting using data from three PV stations in Hebei, China, the model reduces Mean Absolute Error (MAE) by 38.53% and Root Mean Square Error (RMSE) by 33.12% and increases the coefficient of determination ($R^2$) by 7.01% compared to the baseline TCN model. Additionally, in multi-step forecasting, the model achieves a reduction of 54.26% in the best MAE and 52.64% in the best RMSE across various time horizons. These results underscore the TB-BTCGA model’s effectiveness and its strong potential for real-time photovoltaic power forecasting in smart grids. Full article

Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR). We optimize the network paths to enhance the phase unwrapping process of InSAR. Additionally, to address design matrix bias resulting from inaccurate temperature data, we employ the mixed total least squares method to estimate deformation parameters. Subsequently, we utilize independent component analysis to analyze the spatiotemporal deformation characteristics of the bridge. The average standard deviation of the unwrapped phase and the modeling residuals have been reduced by 87% and 5%, respectively. Our findings indicate that thermal expansion deformation is primarily concentrated in the expansion joints, measuring approximately 0.6 mm/°C. In contrast, the cable-stayed bridge deck exhibits the largest deformation magnitude, exceeding 2.0 mm/°C. This research focuses on bridge structures to identify typical deformation locations and evaluate their deformation characteristics. Such analysis is beneficial for conducting safety assessments of bridges. Full article

To solve the problem that the existing planetary gear train fault diagnosis methods have, namely their low diagnostic accuracy under low signal-to-noise ratio (SNR), a fault diagnosis method based on a double channel-deep residual shrinkage network (DC-DRSN) is proposed. The short-time Fourier transform (STFT) is used to convert the original vibration signal into a two-dimensional time-frequency graph, which effectively enhances the ability to express information. A DC-DRSN model is constructed, and the optimal number of residual shrinkage modules is determined by combining the diagnostic characteristics with different noises, which effectively improves the accuracy and anti-noise ability of fault diagnosis. The results of bearing and planetary gear train crack fault diagnosis show that the diagnosis method based on DC-DRSN has higher diagnostic accuracy while realizing fault diagnosis, which is better than other deep learning diagnosis methods. At the same time, the method can adapt to fault diagnosis in different noise environments, and has good expression ability and generalization ability. Full article

Biomass–fungi composite materials primarily consist of biomass particles (sourced from agricultural residues) and a network of fungal hyphae that bind the biomass particles together. These materials have potential applications across diverse industries, such as packaging, furniture, and construction. 3D printing offers a new approach to manufacturing parts using biomass–fungi composite materials, as an alternative to traditional molding-based methods. However, there are challenges in producing parts with desired quality (for example, geometric accuracy after printing and height shrinkage several days after printing) by using 3D printing-based methods. This paper introduces an innovative approach to enhance part quality by incorporating ionic crosslinking into the 3D printing-based methods. While ionic crosslinking has been explored in hydrogel-based bioprinting, its application in biomass–fungi composite materials has not been reported. Using sodium alginate (SA) as the hydrogel and calcium chloride as the crosslinking agent, this paper investigates their effects on quality (geometric accuracy and height shrinkage) of 3D printed samples and physiochemical characteristics (rheological, chemical, and texture properties) of biomass–fungi composite materials. Results show that increasing SA concentration led to significant improvements in both geometric accuracy and height shrinkage of 3D printed samples. Moreover, crosslinking exposure significantly enhanced hardness of the biomass–fungi mixture samples prepared for texture profile analysis, while the inclusion of SA notably improved cohesiveness and springiness of the biomass–fungi mixture samples. Furthermore, Fourier transform infrared spectroscopy confirms the occurrence of ionic crosslinking within 3D printed samples. Results from this study can be used as a reference for developing new biomass–fungi mixtures for 3D printing in the future. Full article

Recycling of multi-source solid waste is of great benefit to energy conservation and environmental governance. In this paper, a new type of environmental protection concrete for railway accessory facilities was prepared from silicon-manganese slag, steel slag, fly ash and recycled macadam. Seven kinds of concrete with different mix proportions were designed. Through unconfined compressive strength, splitting, drying shrinkage and temperature shrinkage tests, the multivariate changing trends of steel slag content, cement dosage and age on the anti-interference ability of concrete were investigated. The main mechanisms of the development of mechanical and dry shrinkage properties were revealed by the hydration process of 3SR-60. The results show that 3SR-60 had better mechanical strength under the same cement dosage. The temperature shrinkage strain decreased and then increased with the rise of the proportion of waste residue, increased with the addition of cement dosage and decreased first and then increased with the descent in the temperature. The temperature shrinkage coefficient reached the lowest value at 0–10 °C. The drying shrinkage coefficient decreases with the increase in the proportion of waste residue and increases with the increase in cement dosage. The dry shrinkage strain increased rapidly during the first 8 days and became almost constant after 30 days. Cementation of calcium silicate hydrate (C-S-H) and ettringite (AFt) developed continuously and filled the internal pores of the structure, interlocking and cementing with each other, which made the microstructure develop from a three-dimensional network to a dense complex, and the macro dimension was reflected in the enhancement of the power to resist external interference. The conclusion of the test summarized that SR-60 had preferable mechanical and shrinkage performance. Full article

Different types of surface defects will occur during the production of strip steel. To ensure production quality, it is essential to classify these defects. Our research indicates that two main problems exist in the existing strip steel surface defect classification methods: (1) they cannot solve the problem of unbalanced data using few-shot in reality, (2) they cannot meet the requirement of online real-time classification. To solve the aforementioned problems, a relational knowledge distillation self-adaptive residual shrinkage network (RKD-SARSN) is presented in this work. First, the data enhancement strategy of Cycle GAN defective sample migration is designed. Second, the self-adaptive residual shrinkage network (SARSN) is intended as the backbone network for feature extraction. An adaptive loss function based on accuracy and geometric mean (Gmean) is proposed to solve the problem of unbalanced samples. Finally, a relational knowledge distillation model (RKD) is proposed, and the functions of GUI operation interface encapsulation are designed by combining image processing technology. SARSN is used as a teacher model, its generalization performance is transferred to the lightweight network ResNet34, and it is conveniently deployed as a student model. The results show that the proposed method can improve the deployment efficiency of the model and ensure the real-time performance of the classification algorithms. It is superior to other mainstream algorithms for fine-grained images with unbalanced data classification. Full article

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%. Full article

Voiceprint recognition can extract voice features and identity the speaker through the voice information, which has great application prospects in personnel identity verification and voice dispatching in the electric industry. The traditional voiceprint recognition algorithms work well in a quiet environment. However, noise interference inevitably exists in the electric industry, degrading the accuracy of traditional voiceprint recognition algorithms. In this paper, we propose an enhanced deep residual shrinkage network (EDRSN)-based voiceprint recognition by combining the traditional voiceprint recognition algorithms with deep learning (DL) in the context of the noisy electric industry environment, where a dual-path convolution recurrent network (DPCRN) is employed to reduce the noise, and its structure is also improved based on the deep residual shrinkage network (DRSN). Moreover, we further use a convolutional block attention mechanism (CBAM) module and a hybrid dilated convolution (HDC) in the proposed EDRSN. Simulation results show that the proposed network can enhance the speaker’s vocal features and further distinguish and eliminate the noise features, thus reducing the noise influence and achieving better recognition performance in a noisy electric environment. Full article

With the rapid development of more-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. However, due to the complex coupling within the power supply system, a fault in one component often leads to parameter abnormalities in multiple components within the system, which are termed associated faults. Compared with conventional faults, the diagnosis of associated faults is difficult because the fault source is hard to trace and the fault mode is difficult to identify accurately. To this end, this paper proposes a graph-matching approach for the associated fault diagnosis of power supply systems based on a deep residual shrinkage network. The core of the proposed approach involves supplementing the incomplete prior fault knowledge with monitoring data to obtain a complete cluster of associated fault graphs. The association graph model of the power supply system is first constructed based on a topology with characteristic signal propagation and the associated measurements of typical components. Furthermore, fault propagation paths are backtracked based on the Warshall algorithm, and abnormal components are set to update and enhance the association relationship, establishing a complete cluster of typical associated fault mode graphs and realizing the organic combination and structured storage of knowledge and data. Finally, a deep residual shrinkage network is used to diagnose the associated faults via graph matching between the current state graph and the historical graph cluster. The comparative experiments conducted on the simulation model of an aircraft power supply system demonstrate that the proposed method can achieve high-precision associated fault diagnosis, even under circumstances where there are an insufficient number of samples and missing parameters. Full article

Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8dB. Full article

In this paper, a fault diagnosis algorithm named improved one-dimensional deep residual shrinkage network with a wide convolutional layer (1D-WIDRSN) is proposed for quadrotor propellers with minor damage, which can effectively identify the fault classes of quadrotor under interference information, and without additional denoising procedures. In a word, that fault diagnosis algorithm can locate and diagnose the early minor faults of the quadrotor based on the flight data, so that the quadrotor can be repaired before serious faults occur, so as to prolong the service life of quadrotor. First, the sliding window method is used to expand the number of samples. Then, a novel progressive semi-soft threshold is proposed to replace the soft threshold in the deep residual shrinkage network (DRSN), so the noise of signal features can be eliminated more effectively. Finally, based on the deep residual shrinkage network, the wide convolution layer and DroupBlock method are introduced to further enhance the anti-noise and over-fitting ability of the model, thus the model can effectively extract fault features and classify faults. Experimental results show that 1D-WIDRSN applied to the minimal fault diagnosis model of quadrotor propellers can accurately identify the fault category in the interference information, and the diagnosis accuracy is over 98%. Full article