Abstract:Lithium-ion battery-powered trucks, as an emerging category in the field of heavy-duty electric commercial vehicles, their in-depth application is of great significance for China to accelerate the implementation of the “dual carbon” goals in the transportation sector. However, heavy-duty electric commercial vehicles have the problem of false battery status alarms during high-current charging, which leads to unnecessary shutdowns, low accuracy in fault diagnosis and low operational efficiency. To address the above issues, this paper proposes a battery fault detection method based on the fusion of convolutional neural network (CNN), long short-term memory network (LSTM), and dynamic autoencoder (DYAD), which is used to solve the problems of battery state changes and cumulative fault effects. The encoder-decoder architecture is adopted. The spatial features of battery data are extracted by CNN, the dynamic evolution law of time series is captured by LSTM network, and the nonlinear characteristics of the battery system are processed with DYAD information entropy, thereby achieving deep learning of complex fault modes. The model architecture effectively extracts spatio-temporal features through the integration of CNN and LSTM, introduces multimodal decoupling technology to monitor key feature errors in real time, and combines global interpretability analysis and potential spatial visualization, significantly enhancing the credibility and transparency of the model. The experimental results show that this method achieves an area under the receiver operating characteristic curve (AUROC) of 88.7% on the real dataset, which is 26.2% higher than that of the graph dynamic network (GDN). By reconstructing error analysis, false alarms caused by high-current charging events have been significantly reduced, and early fault detection has been achieved.

Abstract:For reliability optimization during the development of X-ray flat panel detectors, this study proposes a multi-dimensional reliability enhancement testing method. Two flat panel detectors were selected as test subjects. Considering their actual service environments, eight stress-loading tests were designed and conducted, including low-temperature stepping, high-temperature stepping, rapid temperature variation, vibration stepping, constant humidity heat stepping, constant humidity heat limit, alternating humidity heat, and comprehensive environmental tests. A dark-field grayscale measurement system was established to monitor performance variations. The results show that both detectors maintained stable operation across the full temperature range. In the vibration stepping test, detector-1 exhibited a temporary disconnection at 10 grms but was recoverable, whereas detector-2 experienced irreversible failure at 8 grms. Under 95%RH conditions, both detectors functioned normally. However, detector-1 failed and could not recover under the comprehensive environmental test, indicating that multi-factor coupled environments significantly accelerate damage. Microscopy and X-ray transmission characterization revealed that solder joint cracking, PCB fractures, wire misalignment, and surface contamination were the primary failure modes, and that the synergistic effects of mechanical stress and humidity markedly accelerated material aging and interfacial degradation. Based on these findings, we propose improvements in soldering-process optimization, cleanliness control, printed-circuit-board anti-vibration design, and chip-wire fixation, thereby providing insights and references for enhancing the reliability of X-ray flat panel detector design and manufacturing.

Abstract:In real industrial scenarios, due to the difficulty in obtaining fault data and the continuous emergence of new fine-grained fault modes under variable operating conditions, the demand for small-sample cross-domain fault diagnosis under variable operating conditions has gradually come to the fore. However, in the context of large-scale fine-grained fault classification, the current small-sample cross-domain fault diagnosis methods have shortcomings such as weak feature screening ability, poor model generalization, and difficulty in identifying new categories. To this end, this paper proposes a small-sample cross-domain fault diagnosis method based on attribute-guided fine-grained feature comparison learning, integrating supervised contrastive learning and multi-attribute learning on the basis of multi-task learning method, guiding the fault diagnosis model to efficiently differentiate the known fine-grained fault features in the source domain through the attribute learning process, and realizing the accurate identification of unknown new faults in the target domain based on the extracted semantic information on cross-category attributes and the fine-tuning of fewer samples. In this paper, by comparing the performance of each fault diagnosis method on the sample-small, multi-classification bearing fault dataset, we verify that the proposed method has superior cross-domain diagnosis performance in small-sample fine-grained scenarios.

Abstract:In the traditional acoustic emission valve internal leakage detection scenario, the portable inspection type instrument detection exists problems such as lack of real-time, low efficiency of data storage and management, and limited environmental adaptability, while the wireless acquisition and cloud processing are constrained by the battery life, and the cost of cloud computing power. To address the above problems, we propose an edge-cloud collaborative acoustic emission signal recognition method for valve internal leakage. Firstly, a lightweight recognition model is constructed, and residual blocks and multiple attention mechanisms are introduced in the complex frequency domain to adaptively focus on the global relationship between different frequency components and enhance the model’s ability to focus on key features. Deep convolution is used in the residual structure, and dimensional splitting of K and V is done in the attention mechanism to realize the compressed attention mechanism, so as to ensure the model lightweight. After mapping back to the time domain, the original input is summed with the reconstructed signal in the frequency domain to avoid information loss during frequency domain processing and to alleviate the problem of gradient vanishing. The encoder, decoder and recognition model are trained together in the training phase, the encoder is deployed in the wireless detection device to reduce the power consumption of wireless transmission in the deployment phase, and the decoder and recognition model are deployed in the cloud. The experimental results demonstrate that the proposed neural network model requires a mere 10.1×103 parameters to achieve optimal performance. This method, when implemented with a compression ratio of 8, reduces the accuracy from 99.5% to 98.9%, while concurrently reducing the energy consumption of the device from 0.49 mAh to 0.15 mAh. This enhancement not only prolongs the battery’s operational lifespan but also facilitates the enhancement of the detection frequency. This solution offers a cost-effective approach for the online monitoring and identification of leakage in valves.

Abstract:To address the limitations of traditional graph neural networks in processing single-modality data, which lead to incomplete information, inaccurate graph structure construction, and difficulties in effectively capturing spatial dependencies among nodes, this paper proposes a fault diagnosis method based on Multimodal Manhattan Graph Lap-Transformer. The method constructs a novel graph structure using Manhattan distance, enabling more stable measurement of inter-node similarity while eliminating dependence on fixed topological structures, thereby enhancing adaptability to complex fault data relationships. By encoding graph topological information through graph Laplacian matrices, the attention mechanism simultaneously considers both node feature similarity and graph structural connectivity. This dual-focus approach strengthens the modeling of local and global dependencies, effectively capturing spatial relationships between nodes. Through experiments on the PU bearing dataset, the AUST bearing dataset, and the tunnel boring machine bearing dataset, the average accuracy rates of fault diagnosis reached 99.7%, 98.8%, and 99.8% respectively, verifying the superiority of this method in bearing fault diagnosis. It demonstrates significant diagnostic accuracy and strong adaptability to various working conditions under noisy and multi-condition circumstances. Moreover, this method exhibits good robustness and stability, providing a novel and efficient solution for the fault diagnosis of bearings and other mechanical equipment.

Abstract:Transmission systems are widely applied in rotating machinery, and the diagnosis of their composite faults is crucial for ensuring the healthy operation of mechanical equipment. In order to improve the accuracy and generalization of transmission compound fault diagnosis, a method of transmission compound fault diagnosis based on multi-domain feature map neural network (MDFGNN) is proposed. Firstly, multiple features of vibration signals are extracted from time domain, frequency domain and entropy to obtain rich multi-feature status information of the transmission, and a node feature matrix is constructed. Then k-nearest neighbor (KNN) algorithm is used to extract the sequence regularity and order of node features, and an edge index matrix is constructed. Secondly, the node feature matrix and the edge index matrix are combined to build the feature map, and the feature map is input into the Graph Neural Networks (GNN) model for classification and recognition. Finally, the accuracy and generalization of the proposed model were tested by adding Gaussian white noise with different signal-to-noise ratios to the original data and the HUST Bearing dataset. In order to verify the effectiveness of the proposed method, a transmission vibration test platform was built, and transmission data of five states were collected by piezoelectric acceleration sensors. The results show that: The multi-domain feature map can fully and comprehensively mine the fault information of the compound fault state of the transmission, overcome the weak, non-linear and complex problems of the compound fault signal, obtain more sensitive information of the transmission operation state, improve the utilization rate of the original data and the stability of the model. Compared with other existing transmission fault diagnosis methods, the accuracy rate can be increased by 4.75%~12.26%, the accuracy difference fluctuation range is 0.07%~1.28%, and the generalization test can reach 96.25%.

Abstract:As an important protection equipment in distribution system, the fault diagnosis of universal circuit breaker (ACB) is very important for the stable operation of power system. However, the traditional single-modal model cannot fully describe the characteristics of the data when extracting features, resulting in a decrease in the accuracy of fault diagnosis. To solve this problem, this paper proposes an improved osprey algorithm to optimize the gated recurrent unit-graham angle and field-recurrence plot-vision transformer (GRU-GASF-RP-ViT) universal circuit breaker fault diagnosis model. The model combines one-dimensional signal and two-dimensional image features to describe the characteristics of the data more comprehensively from the perspective of time series and space. The accuracy of fault classification and recognition is improved. Firstly, the one-dimensional vibration signal is converted into two sets of two-dimensional images by GASF and RP respectively. Then, the two-branch ViT is used to effectively learn the spatial and local features of the two sets of two-dimensional images. The other branch captures the dynamic changes and trends in the one-dimensional time series signal through the GRU, and realizes the parallel combination of GRU and the new two-branch ViT. For the hyperparameters that are difficult to determine in the model. The improved osprey algorithm is introduced to optimize the parameters to make the model more reasonable. Finally, a circuit breaker fault simulation experiment platform is built. By comparing with the other four models, the accuracy rate has increased by 3.3%~13.3%, it is verified that the proposed model has higher diagnostic accuracy.

Abstract:Aiming at the bad fault classification accuracy of rolling bearing in motor due to different probability distributions and insufficient sample data, a fault classification method is proposed based on improved multi-channel convolutional network with bidirectional gated units for cross-working conditions. A multi-channel convolutional neural network with bottleneck and BiGRU module was designed to capture global fault information from raw vibration signals end-to-end, while reducing computational load through the bottleneck module and optimizing information flow through the BiGRU module. Local maximum mean discrepancy (LMMD) is adopted to complete subdomain adaptation, reducing the feature distribution differences between the source and target domains in the pre-trained models. Three scenarios were distinguished: different loads at the same speed, different loads at different speeds, and conditions with a wide range of variations. Twelve transfer tasks were designed on the SDUST, CWRU, and PU public datasets to experimentally validate the proposed method. The experimental results show that the average accuracy of the proposed method reaches 90.09%, 99.70%, and 91.75% respectively, significantly higher than comparison methods such as MMD, DANN, and CDAN. It maintains a top single-task accuracy of 99.99% under strong scenario shifts, showing high precision and generalization. The results of CWRU dataset show that, compared with other methods, such as DAMSCN-BiGRU, MSDAM, and improved DANN unsupervised domain adaptation model, the proposed method has also a higher accuracy quantity under cross-working conditions.

Abstract:To address the issues of scarce fault data for varying blockage levels of intake or exhaust system in aero piston engine, and the resultant unbalanced sample sizes that lead to poor diagnostic performance and low robustness, this paper defined two experimental scenarios: fault diagnosis under small-sample conditions and fault diagnosis under class-imbalance conditions. A Transfer-Architecture-based classconditional Wasserstein GAN with gradient penalty (TCWGAN-GP) was proposed to generate high-quality multi-source fault samples of specified categories. The generator of TCWGAN-GP was based on the encoder of Vision Transformer as the backbone network to fully capture the corresponding relationships among different block data sources. The loss function combines the Wasserstein distance and the gradient penalty term GP to prevent model collapse and gradient vanishing, thereby enhancing the stability of adversarial training. The screened and generated samples were merged with the original data for training the diagnostic model to verify the quality of the samples. Experiments were conducted under two stable operating conditions across the two defined scenarios. The average test accuracy was improved to varying degrees compared to the original dataset. For example, in the class-imbalanced experiment of the 1 750 r/min_50% throttle dataset, the average test accuracy increased by 55.74% and 59.26% when the training rounds were 30 and 50, respectively. In the ablation experiment, the samples generated by the proposed method were closer to the real samples, achieving an accuracy rate of 100% in the diagnostic test, Its test accuracy and robustness were superior to other generation methods.

Abstract:To improve the detection efficiency and accuracy of series arc fault (SAF) in low-voltage alternating current power system, this study takes industrial motor distribution circuits as the object, constructs datasets through arc fault experiments, and designs a lightweight SAF identification model based on MobileViT architecture. The model uses lightweight convolution modules and transformer modules to extract local and global features from the current signal respectively, and uses the unfold-transformer-fold mechanism and global average pooling to achieve parameter and complexity reduction. Further, the TensorRT inference optimizer and engine are used to deploy and optimize the model, which significantly improves the inference speed of the model in embedded devices, and based on this, the full-circuit SAF on-line detection device is developed. The detection device has flexible deployment characteristics: when installed at the front end of the frequency converter, it can simultaneously monitor the SAF of the front and back end of the frequency converter. It can also achieve precise monitoring of the SAF of the back end when installed at the back end. The test results show that the average runtime of the device is less than 0.874 ms, and the accuracy is above 97.20%, which can meet the requirements of IEC62606 standard and industrial scenarios. In addition, the comparison experiments show that the device is superior to the existing arc fault detector products and can provide a reference for the development of industrial arc fault circuit breakers.

Abstract:Aiming at the problems of complex feature extraction and low detection rate in sea-surface small target detection under the background of sea clutter, the data characteristics of sea clutter and target echoes are analyzed, and the applicability of feature mode decomposition (FMD) in sea clutter signal processing is studied. Based on this, a sea-surface small target detection method combining optimized feature mode decomposition and spectral entropy features is proposed. A hybrid intelligent algorithm combining symbiotic organism search (SOS) and particle swarm optimization (PSO) was used for parameter optimization, and multi-scale envelope spectrum entropy (MSESEn) was used to extract signal features. A deep extreme learning machine (DELM) classifier model with controllable false alarm is constructed. The normalized feature data is input into the model, and the decision threshold is updated in real time by comparing the predicted value and the decision threshold. The false alarm rate of the control model is realized, and the reliability and detection efficiency of the algorithm are improved. The IPIX data set is used for verification, and the detection rate is improved by 18% on average under HV polarization mode, which shows that the performance of the proposed method is better than that of Fourier Transform and three-feature detection method.

Abstract:Wheel-rail force is a core indicator for measuring railway safety. The most direct and effective way to obtain wheel-rail force data is to use the force-measuring wheel pairs on inspection vehicles. However, the data collected from the force-measuring wheel pairs often contains multiple interference factors, which makes the accurate assessment of railway conditions complex. Moreover, existing algorithms are difficult to separate the required wheel-rail force signals from the complex interferences in reality. To this end, this paper proposes a robust wheel-rail force detection method that integrates an improved complexity tracking algorithm with signal feature extraction. Firstly, a new small-batch iterative strategy is adopted to extract subsets from the total wheel-rail force data set as small-batch samples, which enhances the global optimization ability of the algorithm and avoids getting trapped in local extremum. Secondly, the gradient descent algorithm based on the adaptive learning rate scheduler is used for complexity tracking, which effectively optimizes the convergence speed and overall performance of the model, making it more suitable for practical engineering. Then, the Hilbert-Huang transform method is utilized to extract the characteristic parameters of the separated wheel-rail force source signals. Finally, through the experimental verification of actual wheel-rail force data, the results show that this detection method can effectively separate the wheel-rail force signals from the mixed signals and accurately extract the characteristic parameters, providing strong data support for the monitoring of railway safety conditions.

Abstract:To reduce the calculation errors of ultrasonic flowmeters using the traditional time-difference method under different flow states, a correction method for flow velocity calculation is proposed. The flow field is classified into three states according to the Reynolds number (Re):laminar flow (Re<2 000), transitional flow (2 0004 000). Correction factors are added to the linear velocity distribution formula of the ideal flow state to address errors caused by different flow states. A water circulation system and a PIV system are used to collect flow rate data at Re=2 000 and Re=4 000 as calibration values. The linear velocity distribution formula with correction factors is combined with the integral time-difference method to calculate flow velocity and rate. By adjusting the correction factors and minimizing the errors between calculated and calibrated values, the correction factors for laminar and turbulent linear velocity distributions are determined as 1.847 1 and 1.436 8, respectively. Then, linear interpolation is applied to obtain the linear velocity distribution and corresponding flow values for transitional flow, The experimental data, are used for validation. Results show that the calculation error for transitional flow (Re 2 000-4 000) is about 0.2% relative to the experimental error, and for high-Re turbulent flow, the relative error is around 0.45%. This proves that the method of combining correction factors with the integral time-difference method via Reynolds number classification is effective and yields more accurate results.

Abstract:In order to address the challenges of local convergence and robustness degradation in the identification of recursive systematic convolutional (RSC) subcodes of Turbo codes under low signal-to-noise ratio (SNR) conditions, this paper proposes a novel cost function based on an exponential error penalty mechanism, combined with an improved Particle Swarm Optimization (PSO) algorithm for efficient global search. The proposed method applies a nonlinear exponential amplification to the mismatch errors of parity-check equations, which markedly strengthens noise suppression and ensures reliable identification performance even under low-SNR conditions. In addition, an adaptive velocity-position update strategy is incorporated into the PSO framework, allowing particles to maintain strong global exploration in the early search phase and to converge efficiently toward the optimal solution in the later phase, thereby mitigating the risk of stagnation in local optima. Simulation results show that under an SNR of 1.5 dB, the proposed method achieves over 95% identification accuracy for a rate-1/2 RSC code with constraint length 5, using only 2 000 intercepted bits and within 8 iterations. Compared with existing state-of-the-art methods, it achieves a performance gain of approximately 0.5 dB. Additional experiments further confirm that the proposed method exhibits strong adaptability and robustness across different SNR levels and code lengths. Overall, the proposed approach achieves a balance between identification accuracy and computational complexity, making it particularly well-suited for practical applications in low-SNR environments and offering a robust solution for blind Turbo code parameter estimation.

Abstract:A retinal vessel segmentation algorithm based on multi-level semantic fusion and feature-coupled Transformer was proposed to address challenges such as the difficulty in extracting fine vessels, low imaging contrast, and interference from lesion information in retinal images. First, a column nonuniformity correction module was used to construct a dual-branch joint feature extraction module, which effectively preserved vessel texture information and enhanced the model’s ability to extract fine vessels. Then, a feature-coupled Transformer module was introduced at the encoder-decoder connection to enhance the representation of vessel features, enabling more accurate recognition of vascular semantic information. Finally, a multi-level semantic fusion module was added to the encoder to suppress background noise interference and focus on vessel-related features. Experiments were conducted on public datasets DRIVE, STARE, and CHASE_DB1. The sensitivity achieved was 80.30%, 80.84% and 82.43%, respectively, the accuracy reached 97.11%, 97.61% and 97.63%, respectively, and the F1-scores were 82.96%, 83.76% and 81.48%, respectively. The experimental results indicate that the proposed method achieves superior segmentation accuracy, preserves the integrity of fine vascular structures, and effectively handles complex lesion regions; overall performance surpasses that of most existing advanced methods and exhibits strong generalization and robustness, thereby providing more reliable technical support for intelligent auxiliary diagnosis of retinal vascular diseases.

Abstract:To address the issues of low detection accuracy and misclassification of similar components in existing micro-motor armature surface defect detection methods, this study proposes an improved YOLOv11n-based approach for detecting surface defects in micro-motor armatures by integrating deep learning techniques. First, by adopting the concepts of efficient partial convolution and residual connections, we designed a partial multi-scale feature aggregation module named C3K2-multi scale partial feature aggregation (C3K2-MSPFA). This significantly enhances the detection capability for objects at different scales, thereby improving the model’s detection accuracy. Second, we introduce omni-dimensional dynamic convolution (ODConv) and adaptive downsampling (ADown) to design a lightweight omni-dimensional adaptive downsampling (OD-ADown) module, reducing the parameter count and computational load of the C3K2-MSPFA module. Finally, to address the weak generalization and slow convergence issues of complete-IoU loss (CIoU) in detection tasks, we employ distance-IoU (DIoU) loss to enhance model accuracy and accelerate bounding box regression speed. Experiments were conducted on a self-built dataset, and the results showed that the improved model achieved an average accuracy of 94.2%, a recall rate of 90.9%, an accuracy rate of 95.9%, 2.15×106 parameters, and a model size of 4.5 MB. Compared with the original YOLOv11n network model, the accuracy, recall, and average precision have been improved by 1.3%, 4.6%, and 2.7%, respectively. Compared with the original model, the number of parameters and model size were reduced by 16.67% and 15.09%, respectively. It can meet the deployment requirements of mobile and embedded devices, and provide certain effective technical support for the development of surface defect detection of armatures in micro and special electric machines.

Abstract:Against the backdrop of widespread application of large-scale equipment, online monitoring of equipment structural health status has become of paramount importance. Structural health monitoring (SHM) methods based on active guided waves have found applications in the field of damage diagnosis due to their characteristics and advantages such as high sensitivity to damage and the ability to propagate over long distances. However, the random and irregular vibrations generated by large-scale equipment during operation can affect the propagation characteristics of guided wave signals. Severe vibrations may even obscure the guided wave signals in the structure, hinder the extraction of these signals, and reduce the accuracy of SHM. To address this issue, this study proposes a guided wave-Gaussian process (GW-GP) damage prediction model. The model integrates active guided wave-based SHM technology with Gaussian process machine learning algorithms. It constructs a nonlinear mapping relationship between damage indices and crack length using damage indices such as root mean square deviation and normalized cross-correlation moment, and optimizes hyperparameters via the conjugate gradient method. Results from aluminum plate crack propagation experiments show that the maximum absolute error between the model-predicted crack length and the true value is 1.52 mm, and the root mean square error is 0.72 mm. This effectively enables quantitative diagnosis and prediction of structural damage under vibration conditions, providing a new technical pathway for structural health monitoring of large-scale equipment.

Abstract:In order to solve the problem of difficulty in balancing real-time and accuracy of lane detection in complex scenes such as shadows and nights using traditional image processing methods, a fast lane detection algorithm based on feature fusion and anchor point classification is proposed to meet the needs of real-time traffic scenes. In the image preprocessing stage, the image is divided into grid like row anchors, and lane detection is transformed into a row anchor classification problem, significantly reducing computational complexity. The lane detection network adopts ResNet-18 as the backbone network and introduces an aggregation module to enhance context feature extraction and improve the ability to capture lane structure information. Combining feature pyramid network (FPN) to achieve multi-scale feature fusion and complement local and global features of lane markings. In addition, an auxiliary segmentation branch with ASPP module is introduced to further optimize the accuracy of lane detection. Experiments were conducted on the public datasets TuSimple and CULane, and the accuracy on the TuSimple dataset reached 96.16%, with a running time of only 3.2 ms; Obtained 70.3% F1 score and FPS of 310 fps on the CULane dataset. The experimental results show that the proposed method significantly improves detection speed while ensuring detection accuracy.

Abstract:In view of the limitations of single-domain analysis and the differentiated distribution of scanning features in image deblurring, a novel Mamba deblurring method based on dual-domain feature fusion is proposed. By introducing a state-space model, the proposed method simultaneously extracts spatial structural features from blurred images and multi-scale frequency-domain features generated by wavelet transformation. This approach overcomes the constraints of single-domain analysis and enables deep integration and adaptive fusion of spatial-domain contextual information with high-frequency details in the wavelet domain, all under the guidance of the state-space model. A dual-branch state-space module is designed to independently model spatial and frequency-domain information, accurately adapting to the differentiated distribution characteristics of spatial structures and high-frequency details in the frequency domain. While significantly enhancing feature representation capabilities, the method effectively addresses the challenges posed by the differentiated distribution of scanning features and achieves high-quality image restoration. Experimental results demonstrate that the proposed method achieves PSNR of 33.75 dB and SSIM of 0.968 on the GoPro dataset, PSNR of 31.81 dB and SSIM of 0.949 on the HIDE dataset, and PSNR/SSIM of 32.92/0.937 and 40.15/0.974 on RealBlur-J and RealBlur-R datasets, respectively, outperforming classical deblurring approaches in terms of blur removal, structural restoration, edge preservation, and overall visual quality. Devices developed based on this method are capable of high-precision image enhancement in practical engineering applications.

Abstract:Abstracs: Sensorless control technology addresses critical limitations of conventional position sensors—including environmental susceptibility and low operational reliability—while significantly enhancing system power density, making it highly suitable for high-speed aerospace propulsion systems. The dynamic performance of sensorless control systems for high-speed permanent magnet motors (HSPMMs) is predominantly governed by advanced control strategies. Traditional sliding mode observers (SMO) exhibit inherent challenges such as chattering, phase delay, and insufficient dynamic tracking capabilities. To overcome these limitations, this study proposes a hybrid control strategy combining an adaptive super-twisting sliding mode observer (ASTSMO) and an extended state observer-based quadrature phase-locked loop (ESO-PLL). The core innovations are the ASTSMO structure effectively suppresses inherent chattering on the sliding mode surface; an adaptive law replaces the traditional low-pass filter, thereby avoiding amplitude attenuation and phase shift of the back electromotive force signal and significantly enhancing system robustness; and a fundamental frequency speed-superimposed ESO-PLL is designed to replace the traditional quadrature phase-locked loop, improving the dynamic estimation performance of position and speed. Validation was conducted based on an established simulation model of a sensorless high-speed permanent magnet propulsion system and a 9 kW high-speed propulsion motor test platform for UAVs. Results demonstrate that, compared to the traditional SMO method, the proposed composite strategy reduces speed regulation time by 33%, decreases steady-state speed fluctuation by 59%, and reduces steady-state position error by 50%. The system′s dynamic response and control accuracy are significantly improved, meeting the high dynamic response and high-precision control requirements of high-speed aviation propulsion motor systems.

Abstract:Clock synchronization is the key to ensuring the accuracy of time difference of arrival (TDOA) positioning systems. Currently, most TDOA systems adopt wireless clock synchronization methods, which necessitate frequent timing exchanges between master and slave base stations. Such wireless synchronization scheme suffers from susceptibility to interference, channel occupation issues, limited tag capacity and so on. Accordingly, a design for a TDOA positioning system utilizing UWB-based wired clock synchronization is proposed. This system has designed a differential remote transmission circuit for clock signal and synchronization signal. Particularly, the circuit for modulation and demodulation of the synchronous signal employs D flip-flop and NOR gate to ensure the phase alignment between the clock signal and the synchronization signal. Moreover, a clock synchronization calibration method designed to compensate for system errors, which confirms a synchronization accuracy of 0.5 ns with a maximum operational range of 100 m via standard Category 5e cables, superior to conventional wireless clock synchronization methods. In addition, a multi-tier cascaded clock synchronization architecture (Master→Submaster→Slave) was implemented to achieve high system scalability. Experimental verification in indoor environments demonstrates that the tag positioning derived from this TDOA positioning implementation yields an average R95 accuracy of approximately 9.75 cm. To sum up, the developed system enhances both precision and stability in UWB-based TDOA localization and reduce wireless channel occupancy of ultra-wideband TDOA positioning.

Abstract:To address the challenges faced by traditional simultaneous localization and mapping (SLAM) algorithms in dynamic occlusion scenarios—namely, the inability to effectively label occluded objects, accurately determine the motion state of potential objects, and the reduction in feature point count after dynamic object removal—this paper proposes an improved visual SLAM (VSLAM) algorithm based on the Oneformer segmentation network. This algorithm enhances attention to occluded regions by designing feature-enhancing convolutions, feature enhancement modules, and occlusion attention modules. It optimizes relative position encoding to improve semantic accuracy of occluded object boundaries, enabling precise marking of potential dynamic objects. Object motion is assessed by first determining the camera position via camera pose estimation, followed by object motion estimation. An optimal nearest-neighbor pixel matching strategy is employed to repair dynamic regions using static information from adjacent frames, enabling the extraction of repaired feature points for pose estimation. Validation on the TUM public dataset and real-world scenarios demonstrated superior trajectory accuracy. Compared to DS-SLAM and DynaSLAM algorithms, the mean root mean square error of absolute trajectory error decreased by 84.08% and 22.29%, demonstrated excellent trajectory accuracy.

Abstract:To address the issue of crossinterference from ethanol gas in electrochemical formaldehyde sensors during indoor environmental monitoring, this study developed a formaldehyde detection system using the embedded AI chip RV1126 as the computing platform, with electrochemical formaldehyde and ethanol sensors as sensing components for environmental data acquisition. At the same time, combined with embedded artificial intelligence technology, algorithm compensation is used at the device end to improve the formaldehyde detection instrument’s ability to resist ethanol cross-interference. This study obtained a dataset using grid sampling method, and based on this data, regression models were constructed using linear equation regression, conventional machine learning regression methods, and neural network regression methods. Comparative experiments on root-mean-square error (RMSE) revealed that the linear regression model exhibited the highest prediction error (≈600 μg/m3), conventional machine learning models achieved ≈100 μg/m3 error, while the neural network regression model demonstrated superior accuracy with an MSE of 30 μg/m3. According to the World Health Organization’s Indoor Air Quality Guidelines, which recommend a long-term average formaldehyde concentration threshold of 80 μg/m3, the proposed formaldehyde detector, combined with the neural network model, effectively fulfills detection requirements in environments with ethanol cross-interference, ensuring reliable formaldehyde pollution monitoring.

Abstract:The dense crowd detection algorithm is of great significance in fields such as public safety monitoring and intelligent traffic scheduling. Aiming at the problems of target occlusion, low detection accuracy of small targets, and missed detection in dense scenes, this paper, based on the lightweight YOLOv10, proposes an improved YOLOv10-SCD algorithm. First, the convolutional block attention module （CBAM) is integrated. Through channel-space two-dimensional weighting, the motion blur processing ability is enhanced and the pedestrian detection accuracy is improved. Second, the dynamic sample (DySample) up-sampler is introduced to improve the image resolution and processing efficiency. At the same time, the SIoU optimization loss function is adopted to further improve the positioning accuracy and the bounding box regression speed. Finally, the performance of the algorithm is verified on the dense crowd dataset, and the role of each module is analyzed through ablation experiments. Experiments show that compared with the original YOLOv10, the core indicators of YOLOv10-SCD are significantly improved:the precision is increased by 1.5%, the recall rate is increased by 2.9%, mAP@0.5 is increased by 0.8%, and mAP@0.5:0.95 is increased by 1.8%. The ablation experiments are carried out on two sets of datasets,the self-built dataset focuses on analyzing the individual and synergistic effects of each module on the algorithm accuracy and efficiency; the WiderPerson public dataset verifies the generalization ability of the algorithm. Therefore, YOLOv10-SCD can efficiently cope with the complex scenes of dense crowds, alleviate the problems of target occlusion and difficult recognition of small targets, and significantly improve the robustness and comprehensive performance of target detection.

Abstract:To improve the accuracy of fault diagnosis of shaft bearing of mine hoist under strong noise influence, this paper proposes a method combining variational mode decomposition （VMD）, multipoint optimal minimum entropy deconvolution adjusted （MOMEDA）, and convolutional neural network (CNN). The Sparrow search algorithm combining sine-cosine and Cauchy mutation is used to perform multi-objective optimization of the penalty factor and decomposition levels of VMD. The vibration signal is decomposed by VMD according to the kurtosis criterion to obtain intrinsic mode functions (IMF). The intrinsic mode functions containing shock components are selected to reconstruct the original signal. MOMEDA is applied to the reconstructed signal for noise reduction. An autocorrelated kurtosis index is established as the fitness function to optimize the key parameter, fault period T, of MOMEDA; permutation entropy is used as the objective function to optimize the filter length. The signal enhanced by MOMEDA is envelope-demodulated, and the envelope amplitude sequence is used as a feature input to the CNN model for training and validation to obtain fault diagnosis results. The methods of VMD-MED-CNN, VMD-MCKD-CNN and VMD-CNN are compared and analyzed. The results show that the average accuracy of VMD-MOMEDA-CNN proposed in this paper is the highest, reaching more than 98%. It is proved that the algorithm has superior accuracy and stability under the influence of strong background noise.

Abstract:To achieve precise discrimination and measurement of dielectric properties for multi-form materials, this study proposes a microwave planar sensor based on a complementary split-ring resonator-defected ground structure (CSRR-DGS), whose unique configuration significantly enhances localized electric field intensity, resulting in a 51.1% improvement in quality factor compared to conventional planar structures, enabling accurate measurements for both solid and liquid dielectrics. Simulation results demonstrate that when testing 4 mm×4 mm×1 mm cubic samples with relative permittivity ranging from 1 to 15, the sensor exhibits excellent linear response characteristics in resonant frequency, which was experimentally validated through measurements of polytetrafluoroethylene (PTFE), 3240 epoxy board, FR4 fiberglass laminate, and acrylonitrile-butadiene-styrene (ABS) resin, showing strong agreement with simulation data and yielding an average relative error of 2.4% for solid dielectric measurements based on the established mathematical model. Furthermore, characterization of ethanol solutions with varying concentrations established a reliable correlation between solution concentration and relative frequency shift, achieving an average relative error of 4.6% for liquid dielectric measurements, collectively demonstrating the sensor′s outstanding performance in dielectric characterization and its effectiveness as a high-precision solution for multi-form material detection.

Abstract:In response to the severe stacking of scrap steel samples and the need for refined classification of scrap steel types, this paper proposes a scrap steel image classification method based on cross-layer fusion of semantic-enhanced features. The proposed method consists of several stages, aiming to optimize the accuracy and efficiency of scrap steel classification. The first stage is motion detection, which is used to extract scrap steel images without moving objects such as grapples from video sequences. This step ensures that the dataset excludes irrelevant objects, providing a more accurate foundation for subsequent analysis. Next, the state-of-the-art visual model “Segment Anything Model (SAM)” is applied to perform semantic segmentation on scrap steel images without moving objects such as grapples, to segment the instances in the scrap steel images. The core contribution of this paper lies in the design of a scrap steel image classification model, EfficientNetB5-CLFSEF, which can effectively handle the subtle differences between scrap steel categories and the significant morphological changes within each category. This model uses EfficientNetB5 as the feature extractor, as it is renowned for its efficiency and high performance in visual recognition tasks. Additionally, the model integrates a novel cross-layer fusion of semantic-enhanced features (CLFSEF) module, which is crucial for improving the classification accuracy of scrap steel images. The CLFSEF module consists of two key components:cross-layer feature fusion (CLF) and semantic-enhanced features (SEF). CLF fuses the features from different layers of the EfficientNetB5 feature extractor, enabling the model to capture deep semantic information and low-level details such as boundaries, which is crucial for distinguishing similar scrap steel categories. On the other hand, the SEF module groups the fused features based on semantic similarity between channels. This grouping process enables the model to focus on the most discriminative features in the image. Moreover, the SEF module also integrates knowledge distillation and maximum entropy regularization techniques to enhance the model’s ability to recognize the most significant parts of the input scrap steel images. To validate the proposed method, experiments were conducted using a specially customized dataset for scrap steel classification. The benchmark EfficientNetB5 achieved an accuracy of 87.98% on the test set. After introducing the CLF module, the accuracy increased to 89.63%. Adding the SEF module resulted in an accuracy of 89.23%, and when the CLF and SEF modules are combined into the complete CLFSEF module, the accuracy increased to 90.51%. Compared to the benchmark classification model, these improvements increased by 1.65%, 1.25%, and 2.53% respectively. Moreover, the proposed model outperforms the comparison classification models.

Abstract:Addressing the key issues in coal mine underground environments, such as poor imaging quality under low illumination, high miss-detection rates for small-scale foreign objects, and feature information loss caused by object occlusion, this paper proposes a low-illumination lightweight foreign object detection model, DEL-YOLO, based on YOLOv11s. Firstly, in the image preprocessing stage, the Contrast-Limited Adaptive Histogram Equalization algorithm is introduced to enhance the detailed features of low-illumination images, effectively improving the visibility of foreign objects in dark areas. Secondly, at the network architecture level, an innovative feature extraction module, DE-Block, is designed, and a DE-C3K2 module is constructed to extract features from foreign objects with irregular shapes and overlapping occlusions. Furthermore, a feature fusion module, EFC, is embedded in the neck network, which suppresses redundant feature fusion through an interlayer feature correlation enhancement mechanism while strengthening the feature representation capability for small objects. Finally, a lightweight detection head, L-Detect, is designed, which achieves parameter compression through a neck feature sharing strategy. Experimental results show that DEL-YOLO achieves an average detection accuracy of 80.8%. Compared with YOLOv11, it improves the average precision rate by 4.9%, reduces the computational load by 40.74%, and decreases the number of parameters by 41.75%, with a model size of only 6.45 MB. While significantly reducing complexity, the improved model can still effectively address the issues of small object miss-detection and occluded object detection in the complex low-illumination environments of coal mines.

Abstract:In tunnel advance drilling for geological forecasting, the detection of defects, cracks, and seepage within borehole walls is crucial to guide safe and efficient tunneling operations. This study addresses this challenge by constructing a borehole wall defect dataset from real-world drilling images and proposing an optimized YOLOv8n-based detection model. The technical advancements are threefold:Firstly, coordinate channel-spatial convolutional module with attention mechanism:A novel module integrating coordinate attention is designed to enhance feature extraction by establishing interdependencies between channel dimensions and spatial coordinates. Secondly, rapid spatial pyramid pooling convolutional module:A lightweight hierarchical architecture is developed to improve the fusion and transmission of shallow and deep network features. Lastly, ghost convolution-enhanced C2f module:A residual-connected C2f structure incorporating ghost convolution operators is proposed to refine multi-scale feature extraction while achieving model lightweighting. Experimental results demonstrate that the proposed algorithm achieves a 5.5% improvement in mean average precision (mAP) over the baseline YOLOv8n model on the custom dataset, with a computational load reduction of 0.1 GFLOPs. Compared to state-of-the-art models such as YOLOv11 and RT-DETR, it exhibits a 7% superiority in average detection accuracy. The improved algorithm effectively enhances detection accuracy, enabling real-time, efficient, and advance forecasting, demonstrating promising prospects for engineering applications.